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The film image is the fusion of some components (image, music, sound effects, dialogue) made through editing, a means of expression that transfigures reality into an audiovisual image. The creating director gives attention to the sound throughout the film, finds the best visual adaptation, a way to create the subjective image that would fuse much better with the sound, enhancing the artistic impression. In both editing and mixing, we must take into account that the image-sound relationship consists in the fact that the sound complements the images, and the images, in turn, complement the sound. Otherwise, it is not about art, it is about a silent film with added sound.

How does the mind process linguistic and non-linguistic sounds? The current study assessed the different ways that spoken words (e.g., "dog") and characteristic sounds (e.g., ) provide access to phonological information (e.g., word-form of "dog") and semantic information (e.g., knowledge that a dog is associated with a leash). Using an eye-tracking paradigm, we found that listening to words prompted rapid phonological activation, which was then followed by semantic access. The opposite pattern emerged for sounds, with early semantic access followed by later retrieval of phonological information. Despite differences in the time courses of conceptual access, both words and sounds elicited robust activation of phonological and semantic knowledge. These findings inform models of auditory processing by revealing the pathways between speech and non-speech input and their corresponding word forms and concepts, which influence the speed, magnitude, and duration of linguistic and nonlinguistic activation. [ABSTRACT FROM AUTHOR]

For its versatility, Python has become one of the most popular programming languages. In spite of its possibility to straightforwardly link native code with powerful libraries for scientific computing, the use of Python for real-time sound applications development is often neglected in favor of alternative programming languages, which are tailored to the digital music domain. This article introduces Python as a real-time software programming tool to interested readers, including Python developers who are new to the real time or, conversely, sound programmers who have not yet taken this language into consideration. Cython and Numba are proposed as libraries supporting agile development of efficient software running at machine level. Moreover, it is shown that refactoring few critical parts of the program under these libraries can dramatically improve the performances of a sound algorithm. Such improvements can be directly benchmarked within Python, thanks to the existence of appropriate code parsing resources. After introducing a simple sound processing example, two algorithms that are known from the literature are coded to show how Python can be effectively employed to program sound software. Finally, issues of efficiency are mainly discussed in terms of latency of the resulting applications. Overall, such issues suggest that the use of real-time Python should be limited to the prototyping phase, where the benefits of language flexibility prevail on low latency requirements, for instance, needed during computer music live performances. [ABSTRACT FROM AUTHOR]

Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two core (A1 and R, representing primary areas) and two belt (Slow and Fast , representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in core areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the Fast area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the Slow stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the Fast stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in Slow area and temporally in Fast area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments. [ABSTRACT FROM AUTHOR]

Recently, signal processing methods are shown to be successful while diagnosing faults in mechanical systems, using noise or vibration data. In this study, two different faulty air disc brakes; noisy and less noisy ones are investigated using Wavelet Synchrosqueezed Transform on audio recordings. The difference between two types are shown in scalogram and also verified by a quantitative measure of entropy. The audio recording has been carried out by using two identical microphones sited on the brakes via data acquisition unit at a sampling rate of 20 kHz, 16-bit resolution and these data are analyzed in MATLAB software. The average of the entropy values of faulty and non-faulty brakes were found to be 0.98 and 0.65, respectively. Therefore, it has been concluded that, the entropy could be used as a distinguishing tool to discriminate the faults. [ABSTRACT FROM AUTHOR]

[Display omitted] • The sound of a knee swing comprises the features of osteoarthritis. • Osteoarthritis can be diagnosed through a machine learning approach using the sound of the knee swing. • The approach is easy to use and affordable to perform anywhere. • In the diagnosis process, it is possible to develop a mobile application. Arthritis, the most common form of osteoarthritis, affects millions of individuals worldwide. While it can affect any joint in the body, it predominantly affects the hands, neck, back, knees, and hips. Traditional imaging technologies like X-rays are impractical for diagnosing knee pathology and are insufficient for detecting disease symptoms, especially in the early stages of damage. Invasive procedures such as arthroscopy are often required to identify changes, but they are unsuitable for repeated assessments, follow-ups, or monitoring. Vibro-acoustography (VAG), which utilizes vibration and acoustic signals from the human knee during movement, has emerged as a non-invasive diagnostic tool. However, muscle-induced low-frequency irregularities triggered by knee movement significantly impact the accuracy of VAG signals. In this study, a novel machine learning-based approach is introduced for detecting knee osteoarthritis by processing sound signals recorded by a condenser microphone. Knee sounds from both healthy and osteoarthritic patients were captured during knee swing motions (flexion and extension). Three features were extracted from the knee sounds and classified using the Perceptron, MLP, SVM, GRNN, and RBF classifiers. The performance of different features and classifiers was compared, revealing that one feature demonstrated robust results with a high accuracy of 87% and an F1-score of 0.86. Compared to other conventional methods, the proposed approach is characterized by its speed, affordability, portability, and user-friendliness, with the potential for easy development into a mobile application. [ABSTRACT FROM AUTHOR]

The LIM homeodomain transcription factor ISL1 is essential for the different aspects of neuronal development and maintenance. In order to study the role of ISL1 in the auditory system, we generated a transgenic mouse (Tg) expressing Isl1 under the Pax2 promoter control. We previously reported a progressive age-related decline in hearing and abnormalities in the inner ear, medial olivocochlear system, and auditory midbrain of these Tg mice. In this study, we investigated how Isl1 overexpression affects sound processing by the neurons of the inferior colliculus (IC). We recorded extracellular neuronal activity and analyzed the responses of IC neurons to broadband noise, clicks, pure tones, two-tone stimulation and frequency-modulated sounds. We found that Tg animals showed a higher inhibition as displayed by two-tone stimulation; they exhibited a wider dynamic range, lower spontaneous firing rate, longer first spike latency and, in the processing of frequency modulated sounds, showed a prevalence of high-frequency inhibition. Functional changes were accompanied by a decreased number of calretinin and parvalbumin positive neurons, and an increased expression of vesicular GABA/glycine transporter and calbindin in the IC of Tg mice, compared to wild type animals. The results further characterize abnormal sound processing in the IC of Tg mice and demonstrate that major changes occur on the side of inhibition.

Almost every moving vehicle produces a kind of sound. Automobile engine and powertrain, tires, exhaust, gearbox and suspension and other. sounds constitute significant sound sources in vehicles. These sounds, which are formed in vehicles, involve identifiable features that distinguish automobiles from each other like signatures belonging to the person. In this study, it was aimed to identify and different types of vehicles by using their engine sounds. These sounds were processed and then these sounds were classified by using an artificial neural networks classification method. As a result, 99.2% success was achieved and the models of the vehicles were recognized. [ABSTRACT FROM AUTHOR]

Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%.

For its versatility, Python has become one of the most popular programming languages. In spite of its possibility to straightforwardly link native code with powerful libraries for scientific computing, the use of Python for real-time sound applications development is often neglected in favor of alternative programming languages, which are tailored to the digital music domain. This article introduces Python as a real-time software programming tool to interested readers, including Python developers who are new to the real time or, conversely, sound programmers who have not yet taken this language into consideration. Cython and Numba are proposed as libraries supporting agile development of efficient software running at machine level. Moreover, it is shown that refactoring few critical parts of the program under these libraries can dramatically improve the performances of a sound algorithm. Such improvements can be directly benchmarked within Python, thanks to the existence of appropriate code parsing resources. After introducing a simple sound processing example, two algorithms that are known from the literature are coded to show how Python can be effectively employed to program sound software. Finally, issues of efficiency are mainly discussed in terms of latency of the resulting applications. Overall, such issues suggest that the use of real-time Python should be limited to the prototyping phase, where the benefits of language flexibility prevail on low latency requirements, for instance, needed during computer music live performances.

Autism spectrum disorder (ASD) is diverse, manifesting in a wide array of phenotypes. However, a consistent theme is reduced communicative and social abilities. Auditory processing deficits have been shown in individuals with ASD—these deficits may play a role in the communication difficulties ASD individuals experience. Specifically, children with ASD have delayed neural timing and poorer tracking of a changing pitch relative to their typically developing peers. Given that accurate processing of sound requires highly coordinated and consistent neural activity, we hypothesized that these auditory processing deficits stem from a failure to respond to sound in a consistent manner. Therefore, we predicted that individuals with ASD have reduced neural stability in response to sound. We recorded the frequency-following response (FFR), an evoked response that mirrors the acoustic features of its stimulus, of high-functioning children with ASD age 7–13 years. Evident across multiple speech stimuli, children with ASD have less stable FFRs to speech sounds relative to their typically developing peers. This reduced auditory stability could contribute to the language and communication profiles observed in individuals with ASD. [ABSTRACT FROM AUTHOR]

Background: MDMA has been shown to induce feelings of sociability, a positive emotional bias and enhanced empathy. While previous research has used only visual emotional stimuli, communication entails more than that single dimension and it is known that auditory information is also crucial in this process. In addition, it is, however, unclear what the neurobiological mechanism underlying these MDMA effects on social behaviour is. Previously, studies have shown that MDMA-induced emotional excitability and positive mood are linked to the action on the serotonin (5-HT) 2A receptor.Aim: The present study aimed at investigating the effect of MDMA on processing of sounds (Processing of Affective Sounds Task (PAST)) and cognitive biases (Approach-Avoidance Task (AAT)) towards emotional and social stimuli and the role of 5-HT2A receptor in these effects.Methods: Twenty healthy recreational users entered a 2 × 2, placebo-controlled, within-subject study with ketanserin (40 mg) as pre-treatment and MDMA (75 mg) as treatment. Behavioural (PAST, AAT) measures were conducted 90 min after treatment with MDMA, respectively, 120 min after ketanserin. Self-report mood measures and oxytocin concentrations were taken at baseline and before and after behavioural tests.Results: Findings showed that MDMA reduced arousal elicited by negative sounds. This effect was counteracted by ketanserin pre-treatment, indicating involvement of the 5-HT2 receptor in this process. MDMA did not seem to induce a bias towards emotional and social stimuli. It increased positive and negative mood ratings and elevated oxytocin plasma concentrations. The reduction in arousal levels when listening to negative sounds was not related to the elevated subjective arousal.Conclusion: It is suggested that this decrease in arousal to negative stimuli reflects potentially a lowering of defences, a process that might play a role in the therapeutic process. [ABSTRACT FROM AUTHOR]

Certain neurons in the auditory cortex release zinc to influence how the brain processes sounds.

In many excitatory synapses, mobile zinc is found within glutamatergic vesicles and is coreleased with glutamate. Ex vivo studies established that synaptically released (synaptic) zinc inhibits excitatory neurotransmission at lower frequencies of synaptic activity but enhances steady state synaptic responses during higher frequencies of activity. However, it remains unknown how synaptic zinc affects neuronal processing in vivo. Here, we imaged the sound-evoked neuronal activity of the primary auditory cortex in awake mice. We discovered that synaptic zinc enhanced the gain of sound-evoked responses in CaMKII-expressing principal neurons, but it reduced the gain of parvalbumin- and somatostatin-expressing interneurons. This modulation was sound intensity-dependent and, in part, NMDA receptor-independent. By establishing a previously unknown link between synaptic zinc and gain control of auditory cortical processing, our findings advance understanding about cortical synaptic mechanisms and create a new framework for approaching and interpreting the role of the auditory cortex in sound processing.

Emotions can be conveyed through a variety of channels in the auditory domain, be it via music, non-linguistic vocalizations, or speech prosody. Moreover, recent studies suggest that expertise in one sound category can impact the processing of emotional sounds in other sound categories as they found that musicians process more efficiently emotional musical and vocal sounds than non-musicians. However, the neural correlates of these modulations, especially their time course, are not very well understood. Consequently, we focused here on how the neural processing of emotional information varies as a function of sound category and expertise of participants. Electroencephalogram (EEG) of 20 non-musicians and 17 musicians was recorded while they listened to vocal (speech and vocalizations) and musical sounds. The amplitude of EEG-oscillatory activity in the theta, alpha, beta, and gamma band was quantified and Independent Component Analysis (ICA) was used to identify underlying components of brain activity in each band. Category differences were found in theta and alpha bands, due to larger responses to music and speech than to vocalizations, and in posterior beta, mainly due to differential processing of speech. In addition, we observed greater activation in frontal theta and alpha for musicians than for non-musicians, as well as an interaction between expertise and emotional content of sounds in frontal alpha. The results reflect musicians’ expertise in recognition of emotion-conveying music, which seems to also generalize to emotional expressions conveyed by the human voice, in line with previous accounts of effects of expertise on musical and vocal sounds processing. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach to sound localization for moving sound sources. We use four conventional microphones and a multi-channel sound recording device for capturing sound signals. A database with different types of moving sound source scenarios is created and one of the scenarios is analysed in this paper. This scenario involves walking of a person and creating sounds simultaneously at certain positions in a room, and our aim is to determine the location of the person as three dimensions. We use time difference of arrival for 3D sound source localization. The time delays between sounds channels are obtained by excitation source information based time-delay estimation algorithm. In addition, signal processing methods are used for increasing the success of moving sound source localization. Savitzky-Golay and threshold filters are utilized to pre-filter the recorded sounds and removing the background noise signals, respectively. The non-linear equation of the sound localization is solved by using the modified Levenberg-Marquardt Algorithm (LMA) on the Time Difference of Arrival (TDOA) process. The results for the 3D localization of the moving sound source are obtained by measuring the differences between the real and estimated positions of the person. [ABSTRACT FROM AUTHOR]

Dyslexia is a reading disorder with strong associations with KIAA0319 and DCDC2 . Both genes play a functional role in spike time precision of neurons. Strikingly, poor readers show an imprecise encoding of fast transients of speech in the auditory brainstem. Whether dyslexia risk genes are related to the quality of sound encoding in the auditory brainstem remains to be investigated. Here, we quantified the response consistency of speech-evoked brainstem responses to the acoustically presented syllable [da] in 159 genotyped, literate and preliterate children. When controlling for age, sex, familial risk and intelligence, partial correlation analyses associated a higher dyslexia risk loading with KIAA0319 with noisier responses. In contrast, a higher risk loading with DCDC2 was associated with a trend towards more stable responses. These results suggest that unstable representation of sound, and thus, reduced neural discrimination ability of stop consonants, occurred in genotypes carrying a higher amount of KIAA0319 risk alleles. Current data provide the first evidence that the dyslexia-associated gene KIAA0319 can alter brainstem responses and impair phoneme processing in the auditory brainstem. This brain-gene relationship provides insight into the complex relationships between phenotype and genotype thereby improving the understanding of the dyslexia-inherent complex multifactorial condition. [ABSTRACT FROM AUTHOR]

• MEG recordings in younger and older adults listening to narrow-band noises. • Sound's amplitude, frequency, and both concurrently were modulated at ∼4 Hz. • Neural synchronization was enhanced in older adults for all temporal modulations. • Sustained neural response was reduced in older adults for frequency modulations. • Neural sensitivity to features in speech differs substantially between age groups. Perception of speech requires sensitivity to features, such as amplitude and frequency modulations, that are often temporally regular. Previous work suggests age-related changes in neural responses to temporally regular features, but little work has focused on age differences for different types of modulations. We recorded magnetoencephalography in younger (21–33 years) and older adults (53–73 years) to investigate age differences in neural responses to slow (2–6 Hz sinusoidal and non-sinusoidal) modulations in amplitude, frequency, or combined amplitude and frequency. Audiometric pure-tone average thresholds were elevated in older compared to younger adults, indicating subclinical hearing impairment in the recruited older-adult sample. Neural responses to sound onset (independent of temporal modulations) were increased in magnitude in older compared to younger adults, suggesting hyperresponsivity and a loss of inhibition in the aged auditory system. Analyses of neural activity to modulations revealed greater neural synchronization with amplitude, frequency, and combined amplitude-frequency modulations for older compared to younger adults. This potentiated response generalized across different degrees of temporal regularity (sinusoidal and non-sinusoidal), although neural synchronization was generally lower for non-sinusoidal modulation. Despite greater synchronization, sustained neural activity was reduced in older compared to younger adults for sounds modulated both sinusoidally and non-sinusoidally in frequency. Our results suggest age differences in the sensitivity of the auditory system to features present in speech and other natural sounds. [ABSTRACT FROM AUTHOR]

Past work has shown that when a peripheral sound captures our attention, it activates the contralateral visual cortex as revealed by an event-related potential component labelled the auditory-evoked contralateral occipital positivity (ACOP). This cross-modal activation of the visual cortex has been observed even when the sounds were not relevant to the ongoing task (visual or auditory), suggesting that peripheral sounds automatically activate the visual cortex. However, it is unclear whether top-down factors such as visual working memory (VWM) load and endogenous attention, which modulate the impact of task-irrelevant information, may modulate this spatially-specific component. Here, we asked participants to perform a lateralized VWM task (change detection), whose performance is supported by both endogenous spatial attention and VWM storage. A peripheral sound that was unrelated to the ongoing task was delivered during the retention interval. The amplitude of sound-elicited ACOP was analyzed as a function of the spatial correspondence with the cued hemifield, and of the memory array set-size. The typical ACOP modulation was observed over parieto-occipital sites in the 280–500 ms time window after sound onset. Its amplitude was not affected by VWM load but was modulated when the location of the sound did not correspond to the hemifield (right or left) that was cued for the change detection task. Our results suggest that sound-elicited activation of visual cortices, as reflected in the ACOP modulation, is unaffected by visual working memory load. However, endogenous spatial attention affects the ACOP, challenging the hypothesis that it reflects an automatic process. • Endogenous spatial attention plays a role in modulating the cross-modal processing of distracting sounds. • Sound-elicited activation of visual cortices is unaffected by visual working memory (VWM) load. • The cognitive system remains receptive to distractors even when attention is directed in the opposite spatial position. [ABSTRACT FROM AUTHOR]

The brain’s fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they will enter at optimal processing phases of the spontaneously occurring rhythmic excitability fluctuations. However, some contexts have a more predictable temporal structure than others. Here, we tested the hypothesis that the processing of rhythmic sounds is more efficient than the processing of irregularly timed sounds. To do this, we simultaneously measured functional magnetic resonance imaging (fMRI) and electro-encephalograms (EEG) while participants detected oddball target sounds in alternating blocks of rhythmic (e.g. with equal inter-stimulus intervals) or random (e.g. with randomly varied inter-stimulus intervals) tone sequences. Behaviorally, participants detected target sounds faster and more accurately when embedded in rhythmic streams. The fMRI response in the auditory cortex was stronger during random compared to random tone sequence processing. Simultaneously recorded N1 responses showed larger peak amplitudes and shorter latencies for tones in the random (vs. the rhythmic) streams. These results reveal complementary evidence for more efficient neural and perceptual processing during temporally predictable sensory contexts.

Copyright of ICT in Muzical Field / Tehnologii Informatice si de Comunicatie in Domeniul Muzical is the property of Tehnologii Informatice si de Comunicatie in Domeniul Muzical / ICT in Musical Field and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of ICT in Muzical Field / Tehnologii Informatice si de Comunicatie in Domeniul Muzical is the property of Tehnologii Informatice si de Comunicatie in Domeniul Muzical / ICT in Musical Field and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many sound sources emit signals in a predictable manner. The idea that predictability can be exploited to support the segregation of one source’s signal emissions from the overlapping signals of other sources has been expressed for a long time. Yet experimental evidence for a strong role of predictability within auditory scene analysis (ASA) has been scarce. Recently, there has been an upsurge in experimental and theoretical work on this topic resulting from fundamental changes in our perspective on how the brain extracts predictability from series of sensory events. Based on effortless predictive processing in the auditory system, it becomes more plausible that predictability would be available as a cue for sound source decomposition. In the present contribution, empirical evidence for such a role of predictability in ASA will be reviewed. It will be shown that predictability affects ASA both when it is present in the sound source of interest (perceptual foreground) and when it is present in other sound sources that the listener wishes to ignore (perceptual background). First evidence pointing towards age-related impairments in the latter capacity will be addressed. Moreover, it will be illustrated how effects of predictability can be shown by means of objective listening tests as well as by subjective report procedures, with the latter approach typically exploiting the multi-stable nature of auditory perception. Critical aspects of study design will be delineated to ensure that predictability effects can be unambiguously interpreted. Possible mechanisms for a functional role of predictability within ASA will be discussed, and an analogy with the old-plus-new heuristic for grouping simultaneous acoustic signals will be suggested.

The brain's fascinating ability to adapt its internal neural dynamics to the temporal structure of the sensory environment is becoming increasingly clear. It is thought to be metabolically beneficial to align ongoing oscillatory activity to the relevant inputs in a predictable stream, so that they will enter at optimal processing phases of the spontaneously occurring rhythmic excitability fluctuations. However, some contexts have a more predictable temporal structure than others. Here, we tested the hypothesis that the processing of rhythmic sounds is more efficient than the processing of irregularly timed sounds. To do this, we simultaneously measured functional magnetic resonance imaging (fMRI) and electro-encephalograms (EEG) while participants detected oddball target sounds in alternating blocks of rhythmic (e.g., with equal inter-stimulus intervals) or random (e.g., with randomly varied inter-stimulus intervals) tone sequences. Behaviorally, participants detected target sounds faster and more accurately when embedded in rhythmic streams. The fMRI response in the auditory cortex was stronger during random compared to random tone sequence processing. Simultaneously recorded N1 responses showed larger peak amplitudes and longer latencies for tones in the random (vs. the rhythmic) streams. These results reveal complementary evidence for more efficient neural and perceptual processing during temporally predictable sensory contexts. [ABSTRACT FROM AUTHOR]

We recorded from middle lateral belt (ML) and primary (A1) auditory cortical neurons while animals discriminated amplitude-modulated (AM) sounds and also while they sat passively. Engagement in AM discrimination improved ML and A1 neurons' ability to discriminate AM with both firing rate and phase-locking; however, task engagement affected neural AM discrimination differently in the two fields. The results suggest that these two areas utilize different AM coding schemes: a "single mode" in A1 that relies on increased activity for AM relative to unmodulated sounds and a "dual-polar mode" in ML that uses both increases and decreases in neural activity to encode modulation. In the dual-polar ML code, nonsynchronized responses might play a special role. The results are consistent with findings in the primary and secondary somatosensory cortices during discrimination of vibrotactile modulation frequency, implicating a common scheme in the hierarchical processing of temporal information among different modalities. The time course of activity differences between behaving and passive conditions was also distinct in A1 and ML and may have implications for auditory attention. At modulation depths ≥ 16% (approximately behavioral threshold), A1 neurons' improvement in distinguishing AM from unmodulated noise is relatively constant or improves slightly with increasing modulation depth. In ML, improvement during engagement is most pronounced near threshold and disappears at highly suprathreshold depths. This ML effect is evident later in the stimulus, and mainly in nonsynchronized responses. This suggests that attention-related increases in activity are stronger or longer-lasting for more difficult stimuli in ML. [ABSTRACT FROM AUTHOR]