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Groove is a popular and widely used concept in the field of music. Yet, its precise definition remains elusive. Upon closer inspection, groove appears to be used as an umbrella term with various connotations depending on the musical era, the musical context, and the individual using the term. Our aim in this article was to explore different definitions and connotations of the term groove so as to reach a more detailed understanding of it. Consequently, in an online survey, 88 participants provided free-text descriptions of the term groove. A thematic analysis revealed that groove is a multifaceted phenomenon, and participants' descriptions fit into two main categories: music- and experience- related aspects. Based on this analysis, we propose a contemporary working definition of the term groove as used in the field of music psychology: "Groove is a participatory experience (related to immersion, movement, positive affect, and social connection) resulting from subtle interaction of specific music- (such as time- and pitch-related features), performance-, and/or individual-related factors." Importantly, this proposed definition highlights the participatory aspect of the groove experience, which participants frequently mentioned, for example describing it as an urge to be "involved in" the music physically and/or psychologically. Furthermore, we propose that being immersed in music might be a prerequisite for other experiential qualities of groove, whereas the social aspect could be a secondary quality that comes into play as a consequence of musical activity. Overall, we anticipate that these findings will encourage a greater variety of research on this significant yet still not fully elucidated aspect of the musical experience. [ABSTRACT FROM AUTHOR]

Traditionally, various Hindustani (North Indian) ragas have been performed at specific times of day, such as dawn, dusk, midday, and evening. Human physiology also exhibits common circadian patterns, with reduced arousal at night, rising during the morning, culminating in peak arousal, and then declining arousal towards the end of the day. This raises the question of how and whether the musical features of ragas for each time of day are related to these circadian patterns of arousal. We formally examined associations between traditionally designated time-of-day classifications and musical features from 65 Hindustani raga performances. Our results showed that only pitch-related features are predictive of time-of-day classifications. Surprisingly, non-pitch factors known to correlate with arousal, such as tempo, did not covary with raga time-of-day practices. In general, the results are consistent with rules for North Indian raga performances described by Vishnu Narayan Bhatkhande (1860–1936) that emphasize the presence or prevalence of particular tones in the raga. The results point to a combination of enculturated and embodied influences in conveying musical arousal. Specifically, they suggest that while time-of-day-related raga listening practices may have been initially influenced by embodied processes, they have ultimately been reshaped by pitch-related cultural norms. [ABSTRACT FROM AUTHOR]

Affective music composition systems are known to trigger emotions in humans. However, the design of such systems to stimulate users? emotions continues to be a challenge because, studies that aggregate existing literature in the domain to help advance research and knowledge is limited. This study presents a systematic literature review on affective algorithmic composition systems. Eighteen primary studies were selected from IEEE Xplore, ACM Digital Library, SpringerLink, PubMed, ScienceDirect, and Google Scholar databases following a systematic review protocol. The findings revealed that there is a lack of a unique definition that encapsulates the various types of affective algorithmic composition systems. Accordingly, a unique definition is provided. The findings also show that most affective algorithmic composition systems are designed for games to provide background music. The generative composition method was the most used compositional approach. Overall, there was rather a low amount of research in the domain. Possible reasons for these trends are the lack of a common definition for affective music composition systems and also the lack of detailed documentation of the design, implementation and evaluation of the existing systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose an emotion-based music classification system using features from physiological signals. The proposed system integrates two functions; the first uses physiological sensors to recognize the emotions of users listening to music, and the second classifies music according to the feelings evoked in the listeners, without using physiological sensors. Moreover, to directly predict the user’s emotions from sensor data acquired through wearable physiological sensors, we developed and implemented a hierarchical inner attention-mechanism-based deep neural network. To relieve the discomfort of users wearing physiological sensors every time to receive content recommendations, the relation between emotion-specific features that are extracted from previously generated physiological signals, and musical features that are extracted from music is learned through a regression neural network. Based on these models, the proposed system classifies input music automatically according to users’ emotional reactions without measuring human physiological signals. The experimental results not only demonstrate the accuracy of the proposed automatic music classification framework, but also provide a new perspective in which human experience-based characteristics related to emotion are applied to artificial-intelligence-based content classification. [ABSTRACT FROM AUTHOR]

Brain responses are often studied under strictly experimental conditions in which electroencephalograms (EEGs) are recorded to reflect reactions to short and repetitive stimuli. However, in real life, aural stimuli are continuously mixed and cannot be found isolated, such as when listening to music. In this audio context, the acoustic features in music related to brightness, loudness, noise, and spectral flux, among others, change continuously; thus, significant values of these features can occur nearly simultaneously. Such situations are expected to give rise to increased brain reaction with respect to a case in which they would appear in isolation. In order to assert this, EEG signals recorded while listening to a tango piece were considered. The focus was on the amplitude and time of the negative deflation (N100) and positive deflation (P200) after the stimuli, which was defined on the basis of the selected music feature saliences, in order to perform a statistical analysis intended to test the initial hypothesis. Differences in brain reactions can be identified depending on the concurrence (or not) of such significant values of different features, proving that coterminous increments in several qualities of music influence and modulate the strength of brain responses. [ABSTRACT FROM AUTHOR]

Brain responses are often studied under strictly experimental conditions in which electroencephalograms (EEGs) are recorded to reflect reactions to short and repetitive stimuli. However, in real life, aural stimuli are continuously mixed and cannot be found isolated, such as when listening to music. In this audio context, the acoustic features in music related to brightness, loudness, noise, and spectral flux, among others, change continuously; thus, significant values of these features can occur nearly simultaneously. Such situations are expected to give rise to increased brain reaction with respect to a case in which they would appear in isolation. In order to assert this, EEG signals recorded while listening to a tango piece were considered. The focus was on the amplitude and time of the negative deflation (N100) and positive deflation (P200) after the stimuli, which was defined on the basis of the selected music feature saliences, in order to perform a statistical analysis intended to test the initial hypothesis. Differences in brain reactions can be identified depending on the concurrence (or not) of such significant values of different features, proving that coterminous increments in several qualities of music influence and modulate the strength of brain responses.

Background There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. New method fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. Results The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Comparison with existing method Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Conclusions Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. [ABSTRACT FROM AUTHOR]

This paper deals with the study of Carnatic raga identification using musical features. In Carnatic music, there are 72 melakartha ragas. Each raga is denoted by musical notes. The musical features of 72 main ragas are extracted. A number of features such as pitch, timbre, tonal, rhythmic features have been discussed with reference to their ability to distinguish different ragas. Due to the intricate nature of Carnatic music, the concept of neutrosophic logic is used to identify each raga. This is because the concept of neutrosophic logic lies in the neutralities present in between truth and false. This creates a component of indeterminacy, which will make raga identification more accurate and smooth. Neutrosophic Cognitive Maps (NCMs) are drawn based on the musical features and solved. Using neutrosophic logic, a reduced set of musical features is arrived for each raga which can be thought of features characterizing the raga. Each raga is classified using a set of musical features which are solutions of NCMs. This paper represents one of the first attempts to classify all 72 melakartha ragas of using neutrosophic logic. [ABSTRACT FROM AUTHOR]

THIS EXPERIMENT WAS DESIGNED TO ADDRESS factors that make repetition of musical themes within a piece recognizable, and to explore the relationship between internal repetition and musical interest. Thirty-seven participants of varied levels of music training listened to Stravinsky's Symphonies of Wind Instruments twice and responded to the music in real time. During the first listening, they continuously rated their level of interest and at the same time mentally identified the major themes. During the second listening, they indicated when they heard the major themes repeating. One theme was especially well recognized when repeated. It was relatively short, slow, began and ended with a predictable pattern, occurred relatively early in the piece, and was interspersed with other themes. Another theme stood out in the interest ratings, which was relatively long, fast, sometimes repeated immediately with a build-up of instrumentation and dynamics, and occurred later in the piece. In general, themes judged interesting were not those that were easily identified when repeated, suggesting these are independent aspects of this composition. No effect of music training was found. Extensive analyses of Stravinsky's Symphonies consider how the themes are repeated and interwoven. The experimental results confirmed the musical attributes considered in these analyses. [ABSTRACT FROM AUTHOR]

A well-known tradition in the study of visual aesthetics holds that the experience of visual beauty is grounded in global computational or statistical properties of the stimulus, for example, scale-invariant Fourier spectrum or self-similarity. Some approaches rely on neural mechanisms, such as efficient computation, processing fluency, or the responsiveness of the cells in the primary visual cortex. These proposals are united by the fact that the contributing factors are hypothesized to be global (i.e., they concern the percept as a whole), formal or non-conceptual (i.e., they concern form instead of content), computational and/or statistical, and based on relatively low-level sensory properties. Here we consider that the study of aesthetic responses to music could benefit from the same approach. Thus, along with local features such as pitch, tuning, consonance/dissonance, harmony, timbre, or beat, also global sonic properties could be viewed as contributing toward creating an aesthetic musical experience. Several such properties are discussed and their neural implementation is reviewed in the light of recent advances in neuroaesthetics. [ABSTRACT FROM AUTHOR]

WHILE LISTENING TO MUSIC , PEOPLE OFTEN unwittingly break down musical pieces into constituent chunks such as verses and choruses. Music segmentation studies have suggested that some consensus regarding boundary perception exists, despite individual differences. However, neither the effects of experimental task (i.e., real-time vs. annotated segmentation), nor of musicianship on boundary perception are clear. Our study assesses musicianship effects and differences between segmentation tasks. We conducted a real-time experiment to collect segmentations by musicians and nonmusicians from nine musical pieces. In a second experiment on non-real-time segmentation, musicians indicated boundaries and their strength for six examples. Kernel density estimation was used to develop multi-scale segmentation models. Contrary to previous research, no relationship was found between boundary strength and boundary indication density, although this might be contingent on stimuli and other factors. In line with other studies, no musicianship effects were found: our results showed high agreement between groups and similar inter-subject correlations. Also consistent with previous work, time scales between one and two seconds were optimal for combining boundary indications. In addition, we found effects of task on number of indications, and a time lag between tasks dependent on beat length. Also, the optimal time scale for combining responses increased when the pulse clarity or event density decreased. Implications for future segmentation studies are raised concerning the selection of time scales for modelling boundary density, and time alignment between models. [ABSTRACT FROM AUTHOR]

Brain responses to discrete short sounds have been studied intensively using the event-related potential (ERP) method, in which the electroencephalogram (EEG) signal is divided into epochs time-locked to stimuli of interest. Here we introduce and apply a novel technique which enables one to isolate ERPs in human elicited by continuous music. The ERPs were recorded during listening to a Tango Nuevo piece, a deep techno track and an acoustic lullaby. Acoustic features related to timbre, harmony, and dynamics of the audio signal were computationally extracted from the musical pieces. Negative deflation occurring around 100 milliseconds after the stimulus onset (N100) and positive deflation occurring around 200 milliseconds after the stimulus onset (P200) ERP responses to peak changes in the acoustic features were distinguishable and were often largest for Tango Nuevo. In addition to large changes in these musical features, long phases of low values that precede a rapid increase – and that we will call Preceding Low-Feature Phases – followed by a rapid increase enhanced the amplitudes of N100 and P200 responses. These ERP responses resembled those to simpler sounds, making it possible to utilize the tradition of ERP research with naturalistic paradigms. [ABSTRACT FROM AUTHOR]

Low-level (timbral) and high-level (tonal and rhythmical) musical features during continuous listening to music, studied by functional magnetic resonance imaging (fMRI), have been shown to elicit large-scale responses in cognitive, motor, and limbic brain networks. Using a similar methodological approach and a similar group of participants, we aimed to study the replicability of previous findings. Participants’ fMRI responses during continuous listening of a tango Nuevo piece were correlated voxelwise against the time series of a set of perceptually validated musical features computationally extracted from the music. The replicability of previous results and the present study was assessed by two approaches: (a) correlating the respective activation maps, and (b) computing the overlap of active voxels between datasets at variable levels of ranked significance. Activity elicited by timbral features was better replicable than activity elicited by tonal and rhythmical ones. These results indicate more reliable processing mechanisms for low-level musical features as compared to more high-level features. The processing of such high-level features is probably more sensitive to the state and traits of the listeners, as well as of their background in music. [ABSTRACT FROM AUTHOR]

To study emotional reactions to music, it is important to consider the temporal dynamics of both affective responses and underlying brain activity. Here, we investigated emotions induced by music using functional magnetic resonance imaging (fMRI) with a data-driven approach based on intersubject correlations (ISC). This method allowed us to identify moments in the music that produced similar brain activity (i.e. synchrony) among listeners under relatively natural listening conditions. Continuous ratings of subjective pleasantness and arousal elicited by the music were also obtained for the music outside of the scanner. Our results reveal synchronous activations in left amygdala, left insula and right caudate nucleus that were associated with higher arousal, whereas positive valence ratings correlated with decreases in amygdala and caudate activity. Additional analyses showed that synchronous amygdala responses were driven by energy-related features in the music such as root mean square and dissonance, while synchrony in insula was additionally sensitive to acoustic event density. Intersubject synchrony also occurred in the left nucleus accumbens, a region critically implicated in reward processing. Our study demonstrates the feasibility and usefulness of an approach based on ISC to explore the temporal dynamics of music perception and emotion in naturalistic conditions. [ABSTRACT FROM AUTHOR]

Music multimodal experience and the transposition into movement, speech and visual codes is very strong among children following a spontaneous game. The aim of this work was to enable children playing an instrument to explore a sound's length, volume, energy and articulation by using voice, movement and finally graphic symbols. As a hypothesis I guessed that these integrated activities would improve the understanding of elements of sounds enabling the children to be more expressive on the instrument. The study was carried out with children aged 5 to 7. During violin lessons the children were offered music features, both emotional (cheerful/sad, firm/uncertain, energetic/delicate etc.) and the dynamic and rhythmic (long/short, heavy/light, fast/slow, legato/staccato, etc.). While the teacher was playing the instrument, children were encouraged to accompany the music with movement and voice. Afterwards they were invited to reproduce with the violin the features of sounds they had experienced themselves. Finally the children were asked to draw their own invented graphic notation indicating the sounds experienced during the various stages of the process and to perform it with the violin again. This study reveals that turning music into body movement and singing voice helped to improve the psychomotor awareness of the sounds they want to achieve. Hence, translating the sounds into body movements, singing voice and graphic representation can be a means to improve children's understanding of the sound features and its reexpressing/ re-performing with the instrument. [ABSTRACT FROM AUTHOR]

The aim of this study is to manipulate musical cues systematically to determine the aspects of music that contribute to emotional expression, and whether these cues operate in additive or interactive fashion, and whether the cue levels can be characterized as linear or non-linear. An optimized factorial design was used with six primary musical cues (mode, tempo, dynamics, articulation, timbre, and register) across four different music examples. Listeners rated 200 musical examples according to four perceived emotional characters (happy, sad, peaceful, and scary). The results exhibited robust effects for all cues and the ranked importance of these was established by multiple regression. The most important cue was mode followed by tempo, register, dynamics, articulation, and timbre, although the ranking varied across the emotions. The second main result suggested that most cue levels contributed to the emotions in a linear fashion, explaining 77–89% of variance in ratings. Quadratic encoding of cues did lead to minor but significant increases of the models (0–8%). Finally, the interactions between the cues were non-existent suggesting that the cues operate mostly in an additive fashion, corroborating recent findings on emotional expression in music (Juslin & Lindström, 2010).

Music information retrieval (MIR) has witnessed rapid advances in various tasks like musical similarity, music genre classification (MGC), etc. MGC and audio tagging are approached using various features through traditional machine learning and deep learning (DL) based techniques by many researchers. DL-based models require a large amount of data to generalize well on new data samples. Unfortunately, the lack of sizeable open music datasets makes the analyses of the robustness of musical features on DL models even more necessary. So, this paper assesses and compares the robustness of some commonly used musical and non-musical features on DL models for the MGC task by evaluating the performance of selected models on multiple employed features extracted from various datasets accounting for billions of segmented data samples. In our evaluation, Mel-Scale based features and Swaragram showed high robustness across the datasets over various DL models for the MGC task. • Robustness of musical features analyzed for music classification using DL models. • Mel-based features and Swaragram generally perform well across various music styles. • Large frame and small sliding window length envelopes are suitable for DL models. • Small DL models suffer from underfitting, whereas large overfit on musical features. [ABSTRACT FROM AUTHOR]

In this article we determine the role of different musical features for the human categorization of folk songs into tune families in a large collection of Dutch folk songs. Through an annotation study we investigate the relation between musical features, perceived similarity and human categorization in music. We introduce a newly developed annotation method which is used to create an annotation data set for 360 folk song melodies in 26 tune families. This dataset delivers valuable information on the contribution of musical features to the process of categorization which is based on assessing the similarity between melodies. The analysis of the annotation data set reveals that the importance of single musical features for assessing similarity varies both between and within tune families. In general, the recurrence of short characteristic motifs is most relevant for the perception of similarity between songs belonging to the same tune family. Global melodic features often used for the description of melodies (such as melodic contour) play a less important role. The annotation data set is a valuable resource for further research on melodic similarity and can be used as enriched “ground truth” to test various kinds of retrieval algorithms in Music Information Retrieval. Our annotation study exemplifies that assessing similarity is crucial for human categorization processes, which has been questioned within Cognitive Science in the context of rule-based approaches to categorization. [ABSTRACT FROM PUBLISHER]