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Previous research has found that higher-pitched female voices elicit jealousy among women. However, it is unknown whether jealousy towards higher-pitched female voices is driven by perceptions of the rival's mating strategy or by beliefs about the speaker's attractiveness to one's romantic partner. In addition, the degree to which higher-pitched female voices elicit jealousy could be associated with variation in trait jealousy among women listeners. Here, I manipulated women's voices to be higher or lower in pitch, and tested whether variation in jealousy towards female voices was more strongly associated with perceptions of mate poaching, beliefs about the speaker's attractiveness to listeners' romantic partner, or with individual differences in trait jealousy. I replicated findings that higher voice pitch elicits more jealousy from women, which was positively associated with perceptions of mate poaching. I found no evidence of an association between trait jealousy and any voice-based perception. The findings suggest that perceptions of a target's proclivity to mate poach better explain the jealousy-inducing nature of higher-pitched female voices than do beliefs about the speaker's attractiveness to one's romantic partner. [ABSTRACT FROM AUTHOR]

Humans perceive a range of basic emotional connotations from music, such as joy, sadness, and fear, which can be decoded from structural characteristics of music, such as rhythm, harmony, and timbre. However, despite theory and evidence that music has multiple social functions, little research has examined whether music conveys emotions specifically associated with social status and social connection. This investigation aimed to determine whether the social emotions of dominance and affiliation are perceived in music and whether structural features of music predict social emotions, just as they predict basic emotions. Participants (N = 1513) listened to subsets of 750 music excerpts and provided ratings of energy arousal, tension arousal, valence, dominance, and affiliation. Ratings were modelled based on ten structural features of music. Dominance and affiliation were readily perceived in music and predicted by structural features including rhythm, harmony, dynamics, and timbre. In turn, energy arousal, tension arousal and valence were also predicted by musical structure. We discuss the results in view of current models of music and emotion and propose research to illuminate the significance of social emotions in music. [ABSTRACT FROM AUTHOR]

Background: Cochlear synaptopathy is a disorder where auditory perceptual impairments, such as speech perception in noise and tinnitus, may not be accurately reflected by audiometric thresholds, even if the audiogram appears normal. It is proposed that the connections between hair cells and the auditory nerve are more susceptible to sound and aging damage than the cochlea's hair cells. Cochlear synaptopathy can be present in ears with normal audiograms and undamaged hair cells, leading to hidden hearing loss. This study aims to construct electrophysiological and behavioral auditory parameters associated with persons exposed to loudness and having normal audiograms and auditory complaints to identify hidden hearing loss. Results: A case–control study was done with forty participants with a documented history of exposure to loud sounds and various auditory complaints, compared to a control group of forty persons who had all been confirmed to have normal audiograms. The chosen behavioral core tests comprised the speech intelligibility in noise test (SPIN), gaps detection in noise (GIN test), and pitch pattern sequence test (PPS). The electrophysiological measures utilized in the study were the auditory brainstem response test (ABR) and electrocochleography (ECochG). The SPIN, PPS, and GIN test results demonstrated statistically significant disparities between the control and case groups. The amplitude ratio of wave I to wave V in ABR and the ratio of EcochG AP to SP demonstrated a statistically significant variance between the two groups. The SPIN test exhibited the highest AUC, signifying its superior diagnostic capability in identifying hidden hearing loss. Conclusion: The present study has shown that the SPIN, as a behavioral test, and the EcochG AP amplitude measure, as an electrophysiological test, provide the greatest auditory diagnostic capability for identifying cochlear synaptopathy. Wave I amplitude in the auditory brainstem response (ABR) and the SP/AP ratio in electrocochleography (EcochG) are promising non-behavioral measures of cochlear synaptopathy or hidden hearing loss. [ABSTRACT FROM AUTHOR]

This study explored the unique connections between music and mathematics as perceived by four groups of experts: professional mathematicians and musicians, as well as teacher educators in these two fields. Using 2 × 2 study design, we studied four groups of participants, comprising theorists and educators from various Israeli universities. During semi-structured interviews, the study participants were asked about their views on the connections between mathematics and music. This study proposes a model of experts' conceptions of the connection between mathematics and music, which is of descriptive and explanatory power. that reveals differences between the four groups of experts. Theoreticians in both disciplines highlighted Mathematics as a key tool for music analysis and creation. Musical educators emphasized the role of music as a tool for learning mathematics. All the study participants, independently of the field of their expertise, value structure, beauty, sense of wonder, freedom and creative thinking as characteristics of both fields. Additionally, all the experts hold conceptions of the importance of integrating music and mathematics into various discipline. This study opened new doors for future research wherein utilization of experts' insights to craft integrated study modules of music and mathematics can be explored, a pursuit that carries substantial significance. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to translate and validate the "Music-Related Quality of Life Questionnaire" into Spanish (sMuRQoL) and assess its convergent validity and discriminative capacity by comparing its scores with the outcomes of the musical perception test Meludia. Methods: The sMuRQoL was completed by 129 patients: 55 cochlear implant (CI) users and 74 normal hearing (NH) individuals. Conducted in this study were an exploratory factor analysis, an evaluation of internal consistency, an assessment of score stability through test–retest reliability, a comparison of sMuRQoL scores between CI users and NH individuals and an examination of potential evidence of convergent validity and discriminative capacity of sMuRQoL in relation to other tools. This involved the comparison of the questionnaire scores with the Meludia outcomes. Results: The sMuRQoL demonstrated a two-dimensional structure. All the dimensions displayed high internal consistency (α = 0.879–0.945) and score stability (ICC = 0.890–0.942). There were significant differences in the Frequency test between NH and CI users (d = 1.19–1.45). There's evidence of convergent validity between the scores of the Frequency test and the results of Meludia (r = 0.242–0.645). Additionally, the Frequency test demonstrate a good discriminative capacity to identify patients with poorer musical perception. Conclusions: The sMuRQoL is a reliable questionnaire, with adequate evidence of validity based on internal structure. This study provides an accessible, cost-effective, and quick-to-administer instrument in Spanish, optimizing available healthcare resources and bringing us closer to the patient needs. [ABSTRACT FROM AUTHOR]

Previous studies reported mixed findings on autistic individuals' pitch perception relative to neurotypical (NT) individuals. We investigated whether this may be partly due to individual differences in cognitive abilities by comparing their performance on various pitch perception tasks on a large sample (n = 164) of autistic and NT children and adults. Our findings revealed that: (i) autistic individuals either showed similar or worse performance than NT individuals on the pitch tasks; (ii) cognitive abilities were associated with some pitch task performance; and (iii) cognitive abilities modulated the relationship between autism diagnosis and pitch perception on some tasks. Our findings highlight the importance of taking an individual differences approach to understand the strengths and weaknesses of pitch processing in autism. [ABSTRACT FROM AUTHOR]

Auditory scene analysis (ASA) is the process through which the auditory system makes sense of complex acoustic environments by organising sound mixtures into meaningful events and streams. Although music psychology has acknowledged the fundamental role of ASA in shaping music perception, no efficient test to quantify listeners' ASA abilities in realistic musical scenarios has yet been published. This study presents a new tool for testing ASA abilities in the context of music, suitable for both normal-hearing (NH) and hearing-impaired (HI) individuals: the adaptive Musical Scene Analysis (MSA) test. The test uses a simple 'yes–no' task paradigm to determine whether the sound from a single target instrument is heard in a mixture of popular music. During the online calibration phase, 525 NH and 131 HI listeners were recruited. The level ratio between the target instrument and the mixture, choice of target instrument, and number of instruments in the mixture were found to be important factors affecting item difficulty, whereas the influence of the stereo width (induced by inter-aural level differences) only had a minor effect. Based on a Bayesian logistic mixed-effects model, an adaptive version of the MSA test was developed. In a subsequent validation experiment with 74 listeners (20 HI), MSA scores showed acceptable test–retest reliability and moderate correlations with other music-related tests, pure-tone-average audiograms, age, musical sophistication, and working memory capacities. The MSA test is a user-friendly and efficient open-source tool for evaluating musical ASA abilities and is suitable for profiling the effects of hearing impairment on music perception. [ABSTRACT FROM AUTHOR]

Emotion studies have commonly reported impaired emotional processing in individuals with heightened anhedonic depressive symptoms, as typically measured by collecting single subjective ratings for a given emotional cue. However, the interindividual variation in moment-to-moment emotional reactivity, and associated time-varying brain networks recruitment as emotions are unfolding, remains unclear. In this study, we filled this gap by using the unique temporal characteristics of music to investigate behavioural and brain network dynamics as a function of anhedonic depressive symptoms severity. Thirty-one neurotypical participants aged 18–30 years completed anhedonic depression questionnaires and then continuously rated happy, neutral and sad pieces of music whilst undergoing MRI scanning. Using a unique combination of dynamic approaches to behavioural (i.e., emotion dynamics) and fMRI (i.e., leading eigenvector dynamics analysis; LEiDA) data analysis, we found that participants higher in anhedonic depressive symptoms exhibited increased recruitment of attentional networks and blunted emotional response to both happy and sad musical excerpts. Anhedonic depression mediated the relationship between attentional networks recruitment and emotional blunting, and the elevated recruitment of attentional networks during emotional pieces of music carried over into subsequent neutral music. Future studies are needed to investigate whether these findings could be generalised to a clinical population (i.e., major depressive disorder). [ABSTRACT FROM AUTHOR]

This research discusses that cognitive processes such as memory, attention and creativity differ in students and professionals, among musicians and non-musicians, dealing with musical performance. The purpose of the study was to evaluate and compare the role of memory, attention and creativity as cognitive processes in musical performance, focusing on the differences between non-musicians and musicians. The sample involved 400 individuals, students and professionals, specialising in music and economics. The research instruments used by the scholars were the Wechsler Memory Scale, the Conners Performance Test, and the Torrance Tests of Creative Thinking. Musical students possessed better-developed auditory and short-term memory, while professional musicians had better auditory, visual working and short-term memory. Analysis of attention reveals that music students score better than non-musicians on all four aspects: inattention, impulsivity, sustained attention, and vigilance. For professionals, the key aspects are impulsivity and sustained attention with better results revealed in musicians. Creative thinking was the only factor where the differences were statistically significant in all five scales and the findings proved that creativity was better developed among musicians. This study provides an in-depth analysis and adds new knowledge to existing literature and empirical data on the cognitive processes associated with musical performance, focusing on memory, attention and creativity. By examining the differences between non-musicians and musicians, as well as students and professionals, the study provides insight into how musical performance can be used as a way to develop these cognitive processes. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the impact of music training on the processing of temporal order in Mandarin sentence reading using event-related potentials (ERPs). Two-clause sentences with temporal connectives ("before" or "after") were presented to both musicians and non-musicians. Additionally, a verbal N-back task was utilized to evaluate the participants' working memory capacities. The findings revealed that musicians, but not nonmusicians, demonstrated a more negative amplitude in the second clauses of "before" sentences compared with "after" sentences. In the N-back task, musicians exhibited faster reaction times than nonmusicians in the two-back condition. Furthermore, a correlation was observed between the ERP amplitude differences (before vs. after) and reaction time differences in the N-back task (0-back vs. 2-back) among musicians. These findings suggested that music training enhances the depth of temporal order processing, potentially mediated by improvements in working memory capacity. [ABSTRACT FROM AUTHOR]

Background: Music learning induces significant neural changes, enhancing auditory, verbal, and visuospatial skills, IQ, and speech perception. Research studies reveal structural and functional brain plasticity due to music training in adults and children. While musical abilities are linked to formal training, the existence of "musical sleepers" with heightened speech perception without formal training is noteworthy in adults. This study addresses the gap in understanding such phenomena in children, exploring psychophysical abilities in musically adept children, and aiming to impact rehabilitation models. Materials and method: A pre-experimental study involved 164 typically developing children (mean age: 9.93) without musical training. Musical abilities were evaluated using the abbreviated version of Montreal Battery for Evaluation of Musical Abilities (MBEMA) test in DMDX software, covering melody, rhythm, and memory subtests. Psychophysical tests in MATLAB assessed temporal, frequency, intensity, timbre, and binaural resolution. Results: Using a median split based on MBEMA test scores (median: 42), 84 children exhibited poor musical abilities, while 80 showed good ones. Descriptive statistics for MBEMA scores and psychophysical tests were conducted. As data did not follow normal distribution, non-parametric inferential statistics were employed. Mann–Whitney U tests revealed significant differences favouring good musical abilities in all assessments: gap deduction (p = 0.01), pitch discrimination at 500 Hz and 4 kHz (p = 0.00), intensity discrimination at 500 Hz (p = 0.00) and 4 kHz (p = 0.01), profile analysis (p = 0.01), interaural level difference (p = 0.023), and interaural time difference (p = 0.038). Spearman correlation showed highest correlation with pitch discrimination at 500 Hz (r = − 0.538), 4 kHz (− 0.416), intensity discrimination at 4 kHz (r = − 0.367), and 500 Hz (r = − 0.311), profile analysis (r = − 0.313), interaural level difference (r = − 0.276), and gap deduction (r = − 0.235). All were significant (p < 0.01), except for the interaural time difference. Conclusion: Children excelling in music surpassed those with weaker abilities in psychophysical tasks. This echoes past research, underscoring auditory discrimination's influence on musical skills in untrained children. These results potentially support the concept of musical sleepers in children. Key predictors include pitch discrimination, while interaural time difference exhibited minimal correlation. Utilising such assessments may predict musical training outcomes for children with auditory disorders, but additional robust statistical investigation is necessary. [ABSTRACT FROM AUTHOR]

Most individuals, regardless of formal musical training, have long-term absolute pitch memory (APM) for familiar musical recordings, though with varying levels of accuracy. The present study followed up on recent evidence suggesting an association between singing accuracy and APM (Halpern & Pfordresher, 2022, Attention, Perception, & Psychophysics, 84(1), 260–269), as well as tonal short-term memory (STM) and APM (Van Hedger et al., 2018, Quarterly Journal of Experimental Psychology, 71(4), 879–891). Participants from three research sites (n = 108) completed a battery of tasks including APM, tonal STM, singing accuracy, and self-reported auditory imagery. Both tonal STM and singing accuracy predicted APM, replicating prior results. Tonal STM also predicted singing accuracy, music training, and auditory imagery. Further tests suggested that the association between APM and singing accuracy was fully mediated by tonal STM. This pattern comports well with models of vocal pitch matching that include STM for pitch as a mechanism for sensorimotor translation. [ABSTRACT FROM AUTHOR]

Spontaneous rhythmic movements are part of everyday life, e.g., in walking, clapping or music making. Humans perform such spontaneous motor actions at different rates that reflect specific biomechanical constraints of the effector system in use. However, there is some evidence for intra-individual consistency of specific spontaneous rates arguably resulting from common underlying processes. Additionally, individual and contextual factors such as musicianship and circadian rhythms have been suggested to influence spontaneous rates. This study investigated the relative contributions of these factors and provides a comprehensive picture of rates among different spontaneous motor behaviors, i.e., melody production, walking, clapping, tapping with and without sound production, the latter measured online before and in the lab. Participants (n = 60) exhibited high intra-individual stability across tasks. Task-related influences included faster tempi for spontaneous production rates of music and wider ranges of spontaneous motor tempi (SMT) and clapping rates compared to walking and music making rates. Moreover, musicians exhibited slower spontaneous rates across tasks, yet we found no influence of time of day on SMT as measured online in pre-lab sessions. Tapping behavior was similar in pre-lab and in-lab sessions, validating the use of online SMT assessments. Together, the prominent role of individual factors and high stability across domains support the idea that different spontaneous motor behaviors are influenced by common underlying processes. [ABSTRACT FROM AUTHOR]

Background: Frequency discrimination underlies more complicated auditory activities like speech comprehension and interpretation. However, intensity differences indicate how far apart noises are. This study aimed to evaluate and compare results of frequency modulation and difference limen for intensity in children with cochlear Implants (CI) as well as normal hearing children. Results: This case–control work was performed on 40 children, aged from 5–18 years, divided into two equal groups: a study group with unilateral CI and a control group with normal peripheral hearing. All patients were subjected to otological examination, audiological evaluation, frequency modulation difference limen (FMDL) and difference limen for intensity (DLI) tests. Patients with CI, as compared to normal hearing (NH) subjects, required significantly higher frequencies to discriminate FMDL and DLI respectively (P value = 0.001). At 2000 and 4000 Hz: FMDL had a significant diagnostic power for patients with CI (AUC = 0.980, 0.998 respectively, P < 0.001), at cut off 1.5, with 100% sensitivity and 100% specificity. While, at 4000 Hz: DLI had a significant diagnostic power for patients with CI (AUC = 0.999, P < 0.001), at cut off 1.5, with 100% sensitivity and 100% specificity. Conclusion: A statistically substantial variation was observed among CI and NH children in FMDL and DLI at all frequencies tested. Patients with CI required significantly higher frequencies to discriminate as compared to NH subjects. Frequencies at 2000 Hz and 4000 Hz possess the best specificity and sensitivity of FMDL. While frequency 4000 Hz possess the best specificity and sensitivity of DLI. [ABSTRACT FROM AUTHOR]

Music information retrieval (MIR) is an interdisciplinary research field that focuses on the extraction, processing, and knowledge discovery of information contained in music. While previous studies have utilized Spotify audio features and Last.fm tags as input values for classification tasks, such as music genre recognition, their potential as target values has remained unexplored. In this article, we address this notable gap in the research landscape by proposing a novel approach to predict Spotify audio features based on a set of Last.fm tags. By predicting audio features, we aim to explore the relationship between subjective perception and concrete musical features, shedding light on patterns and hidden correlations between how music is perceived, consumed, and discovered. Additionally, the predicted audio features can be leveraged in recommendation systems to provide users with explainable recommendations, bridging the gap between algorithmic suggestions and user understanding. Our experiments involve training models such as GPT-2, XGBRegressor, and Bayesian Ridge regressor to predict Spotify audio features from Last.fm tags. Through our findings, we contribute to the advancement of MIR research by demonstrating the potential of Last.fm tags as target values and paving the way for future research on the connection between subjective and objective music characterization. Our approach holds promise for both listeners and researchers, offering new insights into the intricate relationship between perception and audio signal in music. Our study aims to explore the feasibility and efficacy of this unique approach, where we intentionally refrain from using traditional audio-based or metadata-driven methods. [ABSTRACT FROM AUTHOR]

Music training is associated with better beat processing in the auditory modality. However, it is unknown how rhythmic training that emphasizes visual rhythms, such as dance training, might affect beat processing, nor whether training effects in general are modality specific. Here we examined how music and dance training interacted with modality during audiovisual integration and synchronization to auditory and visual isochronous sequences. In two experiments, musicians, dancers, and controls completed an audiovisual integration task and an audiovisual target-distractor synchronization task using dynamic visual stimuli (a bouncing figure). The groups performed similarly on the audiovisual integration tasks (Experiments 1 and 2). However, in the finger-tapping synchronization task (Experiment 1), musicians were more influenced by auditory distractors when synchronizing to visual sequences, while dancers were more influenced by visual distractors when synchronizing to auditory sequences. When participants synchronized with whole-body movements instead of finger-tapping (Experiment 2), all groups were more influenced by the visual distractor than the auditory distractor. Taken together, this study highlights how training is associated with audiovisual processing, and how different types of visual rhythmic stimuli and different movements alter beat perception and production outcome measures. Implications for the modality appropriateness hypothesis are discussed. [ABSTRACT FROM AUTHOR]

Music therapy is a therapeutic strategy that uses the natural mood enhancing feature of music to help patients improve their mental health. The music therapist employs a variety of therapies, which can be grouped into two main groups: active interventions and passive interventions. Passive music therapy strategies allow the patient to listen live or recorded music, while active methods engage the patient to sing, compose, or play an instrument. Natural sounds, classical music, or Western music can help patients feel less anxious by lowering cortisol levels, blood pressure, and heart rate. The frequency and length of each music therapy session might vary greatly depending on the desired result, the patient's preferences, and the environment in which the therapy is provided. This paper provides the frequencies for swaras of both classical music and western music to carefully study the frequency difference between the music. Also, it studies the Indian Classical Ragas Structure and its Influence on Human Body for Music Therapy. This investigation has explicitly demonstrated that different ragas can generate different emotions and examined the significance of various music for metabolic syndrome. Finally, it identifies the need for AI to provide valuable information and recommendations for patients. This study can be used to increase the understanding ability of researchers regarding music therapy and its effects related to different diseases. [ABSTRACT FROM AUTHOR]

Music classification has achieved great progress due to the development of Convolutional Neural Networks (CNNs), which is important for music retrieval and recommendation. However, CNN cannot capture temporal information from music audio, which restricts the prediction performance of the model. To address the issue, we propose a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model to learn local spatial features by CNN and learn temporal dependencies by LSTM. In addition, the traditional softmax loss function commonly lacks sufficient discrimination in music classification. Therefore, we propose an additive angular margin and cosine margin softmax (AACM-Softmax) loss function to improve classification results, which minimizes intra-class variances and maximizes inter-class variances simultaneously by enforcing combined margin penalties. Furthermore, we combine the CNN-LSTM model with AACM-Softmax loss function to comprehensively improve the classification performance by learning temporal-dependencies-included discriminative essential features. Extensive experiments on music genre datasets and music emotion datasets show that the proposed model consistently outperforms other models. [ABSTRACT FROM AUTHOR]

With the introduction of ChatGPT, the public's perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field's development prospects. [ABSTRACT FROM AUTHOR]

In order to explore the application of neural networks in the intelligent protection of digital music copyrights, this paper initially investigates the key aspects of digital music-related supply chain management (SCM). Subsequently, this paper introduces a fuzzy, comprehensive evaluation-based method for authenticating digital music infringement. Furthermore, it employs a convolutional neural network to analyze digital music's source components. Within this context, an attention mechanism and an adaptive gate model are meticulously designed. Moreover, a selective adaptive cascade is implemented to optimize the structure of the multi-resolution coder–decoder. Through fuzzy authentication, it becomes feasible to accurately and reliably track and verify copyright information pertaining to digital music, thus providing an effective protective mechanism for the digital music industry. Experimental results reveal that in comparison with the SHN-4 series models, the network model embedded with the channel space (CS) attention module achieves improvements of up to 0.04 dB in terms of performance metrics for human voice and accompaniment sources. Furthermore, it outperforms these models by up to 0.19 dB in music source separation metrics. The incorporation of the adaptive attention mechanism profoundly enhances the model's performance in audio source separation tasks, benefiting both vocal and accompaniment sources. This improvement is evident in the form of a 0.44 SDR enhancement for vocal sources and a 0.18 SDR enhancement for accompaniment sources. Additionally, the proposed two-stage music separation-gate self-attention model surpasses the two-stage music separation-gate model in metrics associated with human voice and accompaniment sources. This outcome convincingly underscores the enhanced effectiveness achieved by integrating CS, self-attention, and adaptive connection in network performance, as well as the adaptive gate structure for music copyright protection. This approach heightens the model's autonomous selectivity, minimizes outliers, stabilizes waveform characteristics, and significantly contributes to the safeguarding of digital content. [ABSTRACT FROM AUTHOR]

Many autistic children show musical interests and good musical skills including pitch and melodic memory. Autistic children may also perceive temporal regularities in music such as the primary beat underlying the rhythmic structure of music given some work showing preserved rhythm processing in the context of basic, nonverbal auditory stimuli. The temporal regularity and prediction of musical beats can potentially serve as an excellent framework for building skills in non-musical areas of growth for autistic children. We examine if autistic children are perceptually sensitive to the primary beat of music by comparing the musical beat perception skills of autistic and neurotypical children. Twenty-three autistic children and 23 neurotypical children aged 6–13 years with no group differences in chronological age and verbal and nonverbal mental ages completed a musical beat perception task where they identified whether beeps superimposed on musical excerpts were on or off the musical beat. Overall task performance was above the theoretical chance threshold of 50% but not the statistical chance threshold of 70% across groups. On-beat (versus off-beat) accuracy was higher for the autistic group but not the neurotypical group. The autistic group was just as accurate at detecting beat alignments (on-beat) but less precise at detecting beat misalignments (off-beat) compared to the neurotypical group. Perceptual sensitivity to beat alignments provides support for spared music processing among autistic children and informs on the accessibility of using musical beats and rhythm for cultivating related skills and behaviours (e.g., language and motor abilities). [ABSTRACT FROM AUTHOR]

Music is omnipresent among human cultures and moves us both physically and emotionally. The perception of emotions in music is influenced by both psychophysical and cultural factors. Chinese traditional instrumental music differs significantly from Western music in cultural origin and music elements. However, previous studies on music emotion perception are based almost exclusively on Western music. Therefore, the construction of a dataset of Chinese traditional instrumental music is important for exploring the perception of music emotions in the context of Chinese culture. The present dataset included 273 10-second naturalistic music excerpts. We provided rating data for each excerpt on ten variables: familiarity, dimensional emotions (valence and arousal), and discrete emotions (anger, gentleness, happiness, peacefulness, sadness, solemnness, and transcendence). The excerpts were rated by a total of 168 participants on a seven-point Likert scale for the ten variables. Three labels for the excerpts were obtained: familiarity, discrete emotion, and cluster. Our dataset demonstrates good reliability, and we believe it could contribute to cross-cultural studies on emotional responses to music. [ABSTRACT FROM AUTHOR]

Objective: The objective of the present study was to compare the effectiveness of the selected tinnitus management protocols for the experimental groups, based on the scores obtained for psychosocial variables, quality of life and severity of tinnitus between the four experimental groups following the various treatment protocols. Method: Two-hundred adults ages ranged from 20 to 55 years (mean age 44.14, SD = 6.16) with mild-to-moderate sensorineural hearing loss without tinnitus were randomly selected for the control group, whereas the experimental group comprised 200 adults with mild-to-moderate sensorineural hearing loss with tinnitus without any other associated medical problems. Participants of both control and experimental groups underwent audiological tests and were administered the QoL questionnaire. The experimental group, in addition, was also administered the PSQ, TSI and THI questionnaire and psychoacoustic tests, i.e. measuring pitch, loudness and MML of tinnitus. Participants of experimental group were randomly assigned to 4 groups, each of 50 participants, and treatment was given as below: Group 1: only tinnitus masking sound was administered, Group 2: only counselling was given, Group 3: masking with counselling was given and Group 4: tinnitus masking combined with counselling and attention diversion task therapy was given. Result: The patients without tinnitus had significantly better quality of life in comparison to the patients with tinnitus. Pre-post comparisons of the treatment groups revealed that "masking + counselling + attention diversion task" group showed highly significant differences for psychosocial aspects, QoL and severity of tinnitus. Further, pairwise comparison based on differences in mean scores indicated significant impact of "masking + counselling + attention diversion task" as compared to both in combination like "masking + counselling" or independently, i.e. masking or counselling alone. Conclusion: It appears that an integrated package of intervention (masking + counselling + attention diversion task) might be preferable for providing immediate tinnitus relief by masking through reducing tinnitus loudness and pitch by altering neuroplasticity (tonotopicity); in the long term, it reduces tinnitus impact through positive thinking by counselling treatment and also diverts attention to daily activity through attention training. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence (AI), music generation has gained widespread attention. Long short-term memory (LSTM) has advantages in handling time series data and has achieved success in the field of music generation. This neural network is capable of capturing the long-term dependencies in music, thus generating chord music that is coherent and innovative. Therefore, to develop a creative and artistic music generation model, this study initially establishes a hidden Markov model (HMM) for chord recognition in music. Subsequently, the algorithm, leveraging the multi-style chord music generation (MSCMG) network, is proposed and applied for chord music generation. Furthermore, an evaluation of the chord music generation algorithm is conducted, utilizing LSTM neural networks within the context of AI. The findings indicate that the HMM, devised in this study, attains an impressive 81.8% chord recognition rate for piano compositions. Additionally, the algorithm, based on the MSCMG network, achieves a notable similarity score of 82.1% for generating classical-style music, with corresponding scores of 3.45, 3.42, and 3.44 for folk-style, classical-style, and pop-style music, respectively. This investigation lays the groundwork for the fusion of AI technology and music composition, exploring novel avenues for music generation and providing novel tools and insights for creative and theoretical exploration within the realm of music. [ABSTRACT FROM AUTHOR]

We describe the development and validation of a test battery to assess musical ability that taps into a broad range of music perception skills and can be administered in 10 minutes or less. In Study 1, we derived four very brief versions from the Profile of Music Perception Skills (PROMS) and examined their properties in a sample of 280 participants. In Study 2 (N = 109), we administered the version retained from Study 1—termed Micro-PROMS—with the full-length PROMS, finding a short-to-long-form correlation of r =.72. In Study 3 (N = 198), we removed redundant trials and examined test–retest reliability as well as convergent, discriminant, and criterion validity. Results showed adequate internal consistency ( ω ¯ =.73) and test–retest reliability (ICC =.83). Findings supported convergent validity of the Micro-PROMS (r =.59 with the MET, p <.01) as well as discriminant validity with short-term and working memory (r ≲.20). Criterion-related validity was evidenced by significant correlations of the Micro-PROMS with external indicators of musical proficiency ( r ¯ =.37, ps <.01), and with Gold-MSI General Musical Sophistication (r =.51, p<.01). In virtue of its brevity, psychometric qualities, and suitability for online administration, the battery fills a gap in the tools available to objectively assess musical ability. [ABSTRACT FROM AUTHOR]

In this study, we explore to address challenges related to incorrect scores, inconsistent rhythm and labeling in the generation of symbolic music scores, with a focus on the utilization of self-supervised models. We present the SymforNet model for symbolic music generation, which is based on self-supervision and deep learning. The model incorporates an attention mechanism and demonstrates exceptional proficiency in recognizing contextual elements of various categories. Experimental results indicate that: (1) The SymforNet model achieve an impressive 88% accuracy in generating music score; (2) In both the training and test sets, the SymforNet model exhibits significantly superior loss values, surpassing the all baseline models; (3) An examination of the multi-track Used Pitch Class data reveals that the SymforNet model, particularly in the context of sequences comprising three to four tracks, displays a strong correlation; (4) By comparing about the quality of music scores, SymforNet has a 87% rate of generating correct scores. [ABSTRACT FROM AUTHOR]

This study investigated the relationship between the emotional use of music and subjective well-being in older Chinese adults. Specifically, it examined how the emotionally adaptive functions of music mediate the relationship between the emotional use of music and subjective well-being moderated by perceived stress. The participants were 347 Chinese men and women aged 60 years or older. The moderated mediating effect was analyzed using PROCESS Macro 3.5 Model 15. Results revealed that the use of music was positively correlated with the emotionally adaptive functions of music and the subjective well-being of older adults. Perceived stress was negatively correlated with subjective well-being, but it was not significantly correlated with the emotional use of music and emotionally adaptive functions of music in senescence. The emotionally adaptive functions of music were positively correlated with subjective well-being. In a moderated mediating model, there was a significant interaction effect between the use of music and perceived stress on life satisfaction. However, the emotionally adaptive functions of music could not mediate the relationship between the use of music and the life satisfaction of older adults, regardless of the level of perceived stress. These findings suggest that, although its effects may differ depending on the level of perceived stress, the use of music can promote emotionally adaptive functions of music and lead to life satisfaction among older adults who were experiencing stress. [ABSTRACT FROM AUTHOR]

In recent years, the rapid advancement of artificial intelligence (AI) technology has made remarkable strides in the music industry, profoundly impacting various aspects of our lives. This progress has extended to revolutionizing industries, streamlining processes, and enhancing personalization in music. AI's expanding capabilities have driven computer vision, natural language processing, and machine learning breakthroughs. As music enthusiasts increasingly turn to online platforms, the need for effective recommendation systems has become more pressing. The exponential growth of musical content poses a challenge in discovering music tailored to individual tastes. This paper proposed a personalized music recommendation system powered by deep learning, explicitly utilizing convolutional neural networks (CNNs) to address the challenges posed by expanding music libraries and diverse song resources. In today's era of ever-growing music databases and increasing content diversity, more than traditional retrieval methods are needed to efficiently connect users with their preferred track. Traditional music platforms rely solely on retrieval mechanisms, necessitating manual inputs such as artist names and titles to locate specific songs. However, this approach needs in-depth music data analysis and delivers personalized recommendations generated by various deep learning-based algorithms. This paper bridges this gap by harnessing deep learning algorithms for comprehensive music data analysis and personalized song suggestions. The proposed CNN-based recommendation system explores the domains of deep learning and content-based approaches, empowering users to discover their favorite tunes swiftly. This innovative mechanism leverages CNNs and deep learning algorithms to offer efficient, personalized, and data-driven music recommendations, transforming the user experience with their music collections. In experimental results, the proposed CNN model is compared with various deep learning algorithms, including DNN, RNN, and LSTM, as well as traditional algorithms like SVM, KNN, RF, and DT. Impressively, the CNN model achieved the highest accuracy at 95%, surpassing all other algorithms. [ABSTRACT FROM AUTHOR]

Rhythm and motor function are intrinsically linked to each other and to music, but the rhythm-motor interplay during music training, and the corresponding brain mechanisms, are underexplored. In a longitudinal training study with children, we examined the role of rhythm predisposition in the fine motor improvements arising from music training, and which brain regions would be implicated. Fifty-seven 8-year-olds were assigned to either a 6-month music training (n = 21), sports training (n = 18), or a control group (n = 18). They performed rhythm and motor tasks, and structural brain scans before and after training were collected. Better ability to perceive rhythm before training was related to less gray matter volume in regions of the cerebellum, fusiform gyrus, supramarginal gyrus, ventral diencephalon, amygdala, and inferior/middle temporal gyri. Music training improved motor performance, and greater improvements correlated with better pre-training rhythm discrimination. Music training also induced a loss of gray matter volume in the left cerebellum and fusiform gyrus, and volume loss correlated with higher motor gains. No such effects were found in the sports and control groups. In summary, children with finer-tuned rhythm perception abilities were prone to finer motor improvements through music training, and this rhythm-motor link was to some extent subserved by the left cerebellum and fusiform gyrus. These findings have implications for models on music-related plasticity and rhythm cognition, and for programs targeting motor function. [ABSTRACT FROM AUTHOR]

The ability of music to induce emotions has been arousing a lot of interest in recent years, especially due to the boom in music streaming platforms and the use of automatic music recommenders. Music Emotion Recognition approaches are based on combining multiple audio features extracted from digital audio samples and different machine learning techniques. In these approaches, neuroscience results on musical emotion perception are not considered. The main goal of this research is to facilitate the automatic subtitling of music. The authors approached the problem of automatic musical emotion detection in movie soundtracks considering these characteristics and using scientific musical databases, which have become a reference in neuroscience research. In the experiments, the Constant-Q-Transform spectrograms, the ones that best represent the relationships between musical tones from the point of view of human perception, are combined with Convolutional Neural Networks. Results show an efficient emotion classification model for 2-second musical audio fragments representative of intense basic feelings of happiness, sadness, and fear. Those emotions are the most interesting to be identified in the case of movie music captioning. The quality metrics have demonstrated that the results of the different models differ significantly and show no homogeneity. Finally, these results pave the way for an accessible and automatic captioning of music, which could automatically identify the emotional intent of the different segments of the movie soundtrack. [ABSTRACT FROM AUTHOR]

A study of 132 audience members of three classical public concerts (all three staged the same chamber music pieces by Ludwig van Beethoven, Brett Dean, and Johannes Brahms) had the goal of analyzing the physiological and motor responses of audiences. It was assumed that the music would induce synchronous physiology and movement in listeners (induction synchrony). In addition to hypothesizing that such synchronies would be present, we expected that they were linked to participants' aesthetic experiences, their affect and personality traits, which were assessed by questionnaires before and after the concerts. Clear evidence was found of physiological synchrony (heart rate, respiration rate, skin conductance response) as well as movement synchrony of the audiences, whereas breathing behavior was not synchronized. Thus the audiences of the three concerts resonated with the music, their music perception was embodied. There were links between the bodily synchrony and aesthetic experiences: synchrony, especially heart-rate synchrony, was higher when listeners felt moved emotionally and inspired by a piece, and were immersed in the music. Personality traits were also associated with the individual contributions to induction synchrony. [ABSTRACT FROM AUTHOR]

Background: The motivational climate within a course has been shown to be an important predictor of students' engagement and course ratings. Because little is known about how students' perceptions of the motivational climate in a computer science (CS) course vary by sex, race/ethnicity, and academic major, we investigated these questions: (1) To what extent do students' achievement and perceptions of motivational climate, cost, ease, and effort vary by sex, race/ethnicity, or major? and (2) To what extent do the relationships between students' achievement and perceptions of motivational climate, cost, and effort vary by sex, race/ethnicity, and major? Participants were enrolled in a large CS course at a large public university in the southeastern U.S. A survey was administered to 981 students in the course over three years. Path analyses and one-way MANOVAs and ANOVAs were conducted to examine differences between groups. Results: Students' perceptions of empowerment, usefulness, interest, and caring were similar across sexes and races/ethnicities. However, women and Asian students reported lower success expectancies. Students in the same academic major as the course topic (i.e., CS) generally reported higher perceptions of the motivational climate than students who did not major or minor in the course topic. Final grades in the course did not vary by sex or race/ethnicity, except that the White and Asian students obtained higher grades than the Black students. Across sex, race/ethnicity, and major, students' perceptions of the motivational climate were positively related to effort, which was positively related to achievement. Conclusions: One implication is that females, Asian students, and non-CS students may need more support, or different types of support, to help them believe that they can succeed in computer science courses. On average, these students were less confident in their abilities to succeed in the course and were more likely to report that they did not have the time needed to do well in the course. A second implication for instructors is that it may be possible to increase students' effort and achievement by increasing students' perceptions of the five key constructs in the MUSIC Model of Motivation: eMpowerment, Usefulness, Success, Interest, and Caring. [ABSTRACT FROM AUTHOR]

Most listeners can determine when a familiar recording of music has been shifted in musical key by as little as one semitone (e.g., from B to C major). These findings appear to suggest that absolute pitch memory is widespread in the general population. However, the use of familiar recordings makes it unclear whether these findings genuinely reflect absolute melody-key associations for at least two reasons. First, listeners may be able to use spectral cues from the familiar instrumentation of the recordings to determine when a familiar recording has been shifted in pitch. Second, listeners may be able to rely solely on pitch height cues (e.g., relying on a feeling that an incorrect recording sounds "too high" or "too low"). Neither of these strategies would require an understanding of pitch chroma or musical key. The present experiments thus assessed whether listeners could make accurate absolute melody-key judgments when listening to novel versions of these melodies, differing from the iconic recording in timbre (Experiment 1) or timbre and octave (Experiment 2). Listeners in both experiments were able to select the correct-key version of the familiar melody at rates that were well above chance. These results fit within a growing body of research supporting the idea that most listeners, regardless of formal musical training, have robust representations of absolute pitch – based on pitch chroma – that generalize to novel listening situations. Implications for theories of auditory pitch memory are discussed. [ABSTRACT FROM AUTHOR]

Arranging and orchestration are critical aspects of music composition and production. Traditional accompaniment arranging is time-consuming and requires expertise in music theory. In this work, we utilize a deep learning model, the flow model, to generate music accompaniment including drums, guitar, bass, and strings based on the input piano melody, which can assist musicians in creating popular music. The main contributions of this paper are as follows: 1) We propose a new pianoroll representation that solves the problem of recognizing the onset of a musical note and saves space. 2) We introduce the MuseFlow accompaniment generation model, which can generate multi-track, polyphonic music accompaniment. To the best of our knowledge, MuseFlow is the first flow-based music generation model. 3) We incorporate a sliding window into the model to enable long sequence music generation, breaking the length limitation. Comparisons on datasets such as LDP, FreeMidi, and GPMD verify the effectiveness of the model. MuseFlow produces better results in accompaniment quality and inter-track harmony. Additionally, the note pitch and duration distributions of the generated accompaniment are much closer to the real data. [ABSTRACT FROM AUTHOR]

Recent research suggests that speaking a tone language confers benefits in processing pitch in nonlinguistic contexts such as music. This research largely compares speakers of nontone European languages (English, French) with speakers of tone languages in East Asia (Mandarin, Cantonese, Vietnamese, Thai). However, tone languages exist on multiple continents—notably, languages indigenous to Africa and the Americas. With one exception (Bradley, Psychomusicology, 26(4), 337–345, 2016), no research has assessed whether these tone languages also confer pitch processing advantages. Two studies presented a melody change detection task, using quasirandom note sequences drawn from Western major scale tone probabilities. Listeners were speakers of Akan, a tone language of Ghana, plus speakers from previously tested populations (nontone language speakers and East Asian tone language speakers). In both cases, East Asian tone language speakers showed the strongest musical pitch processing, but Akan speakers did not exceed nontone speakers, despite comparable or better instrument change detection. Results suggest more nuanced effects of tone languages on pitch processing. Greater numbers of tones, presence of contour tones in a language's tone inventory, or possibly greater functional load of tone may be more likely to confer pitch processing benefits than mere presence of tone contrasts. [ABSTRACT FROM AUTHOR]

Listeners show perceptual benefits (faster and/or more accurate responses) when perceiving speech spoken by a single talker versus multiple talkers, known as talker adaptation. While near-exclusively studied in speech and with talkers, some aspects of talker adaptation might reflect domain-general processes. Music, like speech, is a sound class replete with acoustic variation, such as a multitude of pitch and instrument possibilities. Thus, it was hypothesized that perceptual benefits from structure in the acoustic signal (i.e., hearing the same sound source on every trial) are not specific to speech but rather a general auditory response. Forty nonmusician participants completed a simple musical task that mirrored talker adaptation paradigms. Low- or high-pitched notes were presented in single- and mixed-instrument blocks. Reflecting both music research on pitch and timbre interdependence and mirroring traditional "talker" adaptation paradigms, listeners were faster to make their pitch judgments when presented with a single instrument timbre relative to when the timbre was selected from one of four instruments from trial to trial. A second experiment ruled out the possibility that participants were responding faster to the specific instrument chosen as the single-instrument timbre. Consistent with general theoretical approaches to perception, perceptual benefits from signal structure are not limited to speech. [ABSTRACT FROM AUTHOR]

The octave illusion (Deutsch, 1974) is a well-known auditory illusion elicited by presenting a dichotic sequence of two tones separated by an octave during which the high and low tones alternate between both ears. This illusion engages an important mechanism of auditory perception, which is pitch perception. Previous studies used central frequencies of the useful musical spectrum to elicit the illusion. However, these studies did not cover part of that spectrum where musical pitch perception decreases (below 200 and above 1600 Hz). The present study aimed to investigate how the relative frequency distribution of percepts changes across a greater proportion of the musical scale to better understand the influence of pitch on illusion perception. Participants were presented with 7 pairs of frequencies from 40–80 Hz to 2000–4000 Hz and had to select a choice (octave, simple, complex) corresponding to their perception. When using pairs of stimuli towards the upper and lower boundaries of the selected range: (1) distributions of percepts significantly differ from the classic 400–800 Hz, (2) the octave percept was reported less frequently, particularly at very low frequencies. Results from this study revealed that the illusion perception differs significantly at the low and high limits of the musical spectrum where reduced accuracy of pitch perception is known to occur. These results support past studies who investigated pitch perception. Furthermore, these results support the model proposed by Deutsch where pitch perception is one of the central frameworks of illusion perception. [ABSTRACT FROM AUTHOR]

Lyrics-to-melody generation is an interesting and challenging topic in AI music research field. Due to the difficulty of learning the correlations between lyrics and melody, previous methods suffer from low generation quality and lack of controllability. Controllability of generative models enables human interaction with models to generate desired contents, which is especially important in music generation tasks towards human-centered AI that can facilitate musicians in creative activities. To address these issues, we propose a controllable lyrics-to-melody generation network, ConL2M, which is able to generate realistic melodies from lyrics in user-desired musical style. Our work contains three main novelties: (1) to model the dependencies of music attributes cross multiple sequences, inter-branch memory fusion (Memofu) is proposed to enable information flow between multi-branch stacked LSTM architecture; (2) reference style embedding (RSE) is proposed to improve the quality of generation as well as control the musical style of generated melodies; (3) sequence-level statistical loss (SeqLoss) is proposed to help the model learn sequence-level features of melodies given lyrics. Verified by evaluation metrics for music quality and controllability, initial study of controllable lyrics-to-melody generation shows better generation quality and the feasibility of interacting with users to generate the melodies in desired musical styles when given lyrics. [ABSTRACT FROM AUTHOR]

Previous research has indicated a critical need for cost-effective alternative therapies. The present pilot study aimed to evaluate a novel, cost-effective therapy for treating insomnia. The study employed a randomized controlled trial with two groups: therapy and control. Participants were screened using research diagnostic criteria for insomnia recommended by the American Academy of Sleep Medicine (AASM) before undergoing simple randomization. The study included participants from Hindu, Muslim, and Christian faiths who were assigned to either the therapy group (Hare Krishna Mantra Based Cognitive Therapy: HMBCT) or the non-therapy group (control with relaxing music). Both groups underwent six weeks of treatment with traditional cognitive-behavioral therapy techniques, including stimulus control, sleep restriction, and sleep hygiene. Each week, participants in the therapy group received six 45-minute sessions of HMBCT in the evening and were asked to practice the therapy in the evening of the day of sleep recording. Sleep quality was assessed using behavioral measures, sleep logs, and polysomnography recordings before and after the six-week treatment period. There was a one-week period before and after the six weeks when no treatment was provided. Results showed that HMBCT significantly improved sleep quality measures, including a 61% reduction in Epworth Sleepiness Scale (ESS) scores and an 80% reduction in Insomnia Severity Index (ISI) scores. Participants did not take any sleep-inducing medication during the study. These findings suggest that adding mantra chanting to traditional cognitive-behavioral therapy may improve sleep quality. [ABSTRACT FROM AUTHOR]

Background: Music-based interventions are promising for cognitive rehabilitation in Parkinson's disease; however, systematic reviews covering the topic are scarce. Objective: To analyze the effectiveness of music-based interventions for cognitive rehabilitation in PD. Method: Systematic review study based on PRISMA criteria. The descriptors Parkinson's disease, Parkinson's disease, idiopathic Parkinson's disease, music-based interventions, music therapy, music training, auditory stimulation, music, rhythm, rhythmic, cognition, and cognitive were used. Five databases were searched PubMed/MEDLINE, PsycInfo, Scopus, Web of Science, and Cochrane in May 2022. Only randomized controlled trials with no limit on publication date or language were included. Risk of bias was assessed following Cochrane Collaboration criteria for development of systematic intervention reviews. Results: Nine hundred nineteen articles were found by the descriptors; 266 were excluded for being repeated; 650 for not meeting the inclusion criteria. The remaining three articles were included and analyzed. The interventions consisted of practices with emphasis on rhythm and were conducted in groups. Risks of important biases were observed, such as lack of blinding in the allocation of participants and in the assessment of outcomes, as well as incomplete data for some outcomes. Conclusion: Overall, the results showed no evidence of efficacy of music-based interventions for cognitive outcomes in PD. [ABSTRACT FROM AUTHOR]

This study investigated whether individuals with high autistic traits rely on psychoacoustic abilities in affective prosody recognition (APR). In 94 college students, Autism Spectrum Quotient (AQ) and psychoacoustic abilities were measured. Results indicated that higher AQ, higher rapid auditory processing (RAP), and maleness were associated with a lower APR accuracy for low-intensity prosodies. There was a strong positive association between RAP and APR for participants with high AQ, whereas low-AQ participants showed no such pattern. The findings suggest a reliance on psychoacoustic abilities as compensatory mechanism for deficits in higher-order processing of emotional signals in social interactions, and imply potential benefits of auditory interventions in improving APR among individuals with high autistic traits. [ABSTRACT FROM AUTHOR]

Temporal envelope fluctuations of natural sounds convey critical information to speech and music processing. In particular, musical pitch perception is assumed to be primarily underlined by temporal envelope encoding. While increasing evidence demonstrates the importance of carrier fine structure to complex pitch perception, how carrier spectral information affects musical pitch perception is less clear. Here, transposed tones designed to convey identical envelope information across different carriers were used to assess the effects of carrier spectral composition to pitch discrimination and musical-interval and melody identifications. Results showed that pitch discrimination thresholds became lower (better) with increasing carrier frequencies from 1k to 10k Hz, with performance comparable to that of pure sinusoids. Musical interval and melody defined by the periodicity of sine- or harmonic complex envelopes across carriers were identified with greater than 85% accuracy even on a 10k-Hz carrier. Moreover, enhanced interval and melody identification performance was observed with increasing carrier frequency up to 6k Hz. Findings suggest a perceptual enhancement of temporal envelope information with increasing carrier spectral region in musical pitch processing, at least for frequencies up to 6k Hz. For carriers in the extended high-frequency region (8–20k Hz), the use of temporal envelope information to music pitch processing may vary depending on task requirement. Collectively, these results implicate the fidelity of temporal envelope information to musical pitch perception is more pronounced than previously considered, with ecological implications. [ABSTRACT FROM AUTHOR]

Rationale: Psychedelics are getting closer to being widely used in clinical treatment. Music is known as a key element of psychedelic-assisted therapy due to its psychological effects, specifically on the emotion, meaning-making, and sensory processing. However, there is still a lack of understanding in how psychedelics influence brain activity in experimental settings involving music listening. Objectives: The main goal of our research was to investigate the effect of music, as a part of "setting," on the brain states dynamics after lysergic acid diethylamide (LSD) intake. Methods: We used an open dataset, where a group of 15 participants underwent two functional MRI scanning sessions under LSD and placebo influence. Every scanning session contained three runs: two resting-state runs separated by one run with music listening. We applied K-Means clustering to identify the repetitive patterns of brain activity, so-called brain states. For further analysis, we calculated states' dwell time, fractional occupancy and transition probability. Results: The interaction effect of music and psychedelics led to change in the time-varying brain activity of the task-positive state. LSD, regardless of the music, affected the dynamics of the state of combined activity of DMN, SOM, and VIS networks. Crucially, we observed that the music itself could potentially have a long-term influence on the resting-state, in particular on states involving task-positive networks. Conclusions: This study indicates that music, as a crucial element of "setting," can potentially have an influence on the subject's resting-state during psychedelic experience. Further studies should replicate these results on a larger sample size. [ABSTRACT FROM AUTHOR]

The effects of background music on learning and memory are inconsistent, partially due to the intrinsic complexity and diversity of music, as well as variability in music perception and preference. By stripping down musical harmony to its building blocks, namely discrete chords, we explored their effects on memory formation of unfamiliar word-image associations. Chords, defined as two or more simultaneously played notes, differ in the number of tones and inter-tone intervals, yielding varying degrees of harmonic complexity, which translate into a continuum of consonance to dissonance percepts. In the current study, participants heard four different types of musical chords (major, minor, medium complex, and high complex chords) while they learned new word-image pairs of a foreign language. One day later, their memory for the word-image pairs was tested, along with a chord rating session, in which they were required to assess the musical chords in terms of perceived valence, tension, and the extent to which the chords grabbed their attention. We found that musical chords containing dissonant elements were associated with higher memory performance for the word-image pairs compared with consonant chords. Moreover, tension positively mediated the relationship between roughness (a key feature of complexity) and memory, while valence negatively mediated this relationship. The reported findings are discussed in light of the effects that basic musical features have on tension and attention, in turn affecting cognitive processes of associative learning. [ABSTRACT FROM AUTHOR]

The production of traditional music is too complicated, consuming a lot of financial and human resources. Therefore, this paper aims to use artificial intelligence (AI) for songwriting and to explore the development and application of the Generative Adversarial Network (GAN) in smart music. An improved GAN-based Multi-Track Music (MTM)-GAN is established. The model is validated with the generation of 5 different music tracks for bass, drums, guitar, piano, and strings. The verification results are compared with the music generated by the existing Multi-Track Sequential GAN (MuseGAN) index evaluation method. The results show that many music clips generated by the MTM-GAN model are smooth and have a certain artistic aesthetic effect. Through the comparison of the two convergence curves of MuseGAN and MTM-GAN, when the penalty term is increased, the MTM-GAN of Consistency Term (CT) converges faster, and the training process is more stable. The numerical space of the parameter distribution obtained by the MTM-GAN-based music segment test is significantly smaller than that of MuseGAN. The probability of MTM-GAN overfitting is small. 62.8% of music listeners cannot distinguish the generated melody from the real melody. Therefore, the proposed model has the advantages of a more stable, more realistic, and faster fitting speed in music generation, indicating that the music generation method is effective. [ABSTRACT FROM AUTHOR]

Streaming music applications allow their users to listen to music that they did not collect, making music collecting voluntary for the first time in the history of music consumption. Using a questionnaire (n = 370), we aimed to measure streaming collection size and its relations to other variables. Results showed a large variability in streaming collection size, with a median of only 200 songs. Furthermore, most participants drastically reduced their collection size when moving to streaming technology. Our results nevertheless found extensive evidence for the merits of streaming collections, including (a) collection size positively correlated with both listening hours and listening enjoyment; (b) participants wanted to listen to their favorite music for most of their streaming listening time; (c) young participants collected almost twice as many streaming songs as older participants, indicating that collecting is not an old habit; and (d) participants who enjoyed listening to records/CDs more than streaming were found to have previously been avid collectors who had abandoned collecting for streaming applications. In this paper, we discuss why regardless of these merits, the large majority of our participants reduced their collection size and conclude with suggestions about how the redesigning of streaming applications may encourage users to collect more songs. [ABSTRACT FROM AUTHOR]

Music is a ubiquitous stimulus known to influence human affect, cognition, and behavior. In the context of eating behavior, music has been associated with food choice, intake and, more recently, taste perception. In the latter case, the literature has reported consistent patterns of association between auditory and gustatory attributes, suggesting that individuals reliably recognize taste attributes in musical stimuli. This study presents subjective norms for a new set of 100 instrumental music stimuli, including basic taste correspondences (sweetness, bitterness, saltiness, sourness), emotions (joy, anger, sadness, fear, surprise), familiarity, valence, and arousal. This stimulus set was evaluated by 329 individuals (83.3% women; Mage = 28.12, SD = 12.14), online (n = 246) and in the lab (n = 83). Each participant evaluated a random subsample of 25 soundtracks and responded to self-report measures of mood and taste preferences, as well as the Goldsmiths Musical Sophistication Index (Gold-MSI). Each soundtrack was evaluated by 68 to 97 participants (Mdn = 83), and descriptive results (means, standard deviations, and confidence intervals) are available as supplemental material at osf.io/2cqa5. Significant correlations between taste correspondences and emotional/affective dimensions were observed (e.g., between sweetness ratings and pleasant emotions). Sex, age, musical sophistication, and basic taste preferences presented few, small to medium associations with the evaluations of the stimuli. Overall, these results suggest that the new Taste & Affect Music Database is a relevant resource for research and intervention with musical stimuli in the context of crossmodal taste perception and other affective, cognitive, and behavioral domains. [ABSTRACT FROM AUTHOR]

We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called "ground truth" needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation – including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems. [ABSTRACT FROM AUTHOR]

Music is an integral part of daily human life, and certain types of music are often associated with certain contexts, such as specific music for sleeping or for studying. The mood-arousal hypothesis suggests that music used for studying should be uplifting to boost arousal and increase cognitive performance while previous studies suggest that music used as a sleep aid should be calm, gentle and slow to decrease arousal. In this study, we created the Study music dataset by collecting tracks from Spotify playlists with the words 'study' or 'studying' in the title or description. In comparison with a pre-existing dataset, the Sleep music dataset, we show that the music's audio features, as defined by Spotify, are highly similar. Additionally, they share most of the same genres and have similar subgroups after a k-means clustering analysis. We suggest that both sleep music and study music aim to create a pleasant but not too disturbing auditory environment, which enables one to focus on studying and to lower arousal for sleeping. Using large Spotify-based datasets, we were able to uncover similarities between music used in two different contexts one would expect to be different. [ABSTRACT FROM AUTHOR]

Advancement in the fields of emotion regulation and performance in learning settings has developed our collective understanding of how affective stimuli modulate emotional responses of learners who are engaging in learning tasks. Studies in the application of background music during cognitively demanding tasks have led to a continued study of how music can be used to affect an emotional response in listeners. The present study used automated facial recognition technology to examine the effects of background music stimulation on middle school learners completing a reading comprehension task. Results indicated that there were significant differences in emotional expressions of anger and frustration while listening to music, coinciding with greater performance on accompanying reading comprehension tasks. The mediation of this 'confused' emotional state could be an indicator of the emotionally salient application of music to help manage these emotional responses. These findings contribute preliminary evidence of the application of facial-emotion recognition technology to identify the effects of musical stimulation on learning, and the possible application of musical stimuli to emotion regulation. [ABSTRACT FROM AUTHOR]