Music Processing Research Articles

Effects of Mindfulness Meditation on Musical Aesthetic Emotion Processing.

Mindfulness meditation is a form of self-regulatory training for the mind and the body. The relationship between mindfulness meditation and musical aesthetic emotion processing (MAEP) remains unclear. This study aimed to explore the effect of temporary mindfulness meditation on MAEP while listening to Chinese classical folk instrumental musical works. A 2 [(groups: mindfulness meditation group (MMG); control group (CG)] × 3 (music emotions: calm music, happy music, and sad music) mixed experimental design and a convenience sample of university students were used to verify our hypotheses, which were based on the premise that temporary mindfulness meditation may affect MAEP (MMG vs. CG). Sixty-seven non-musically trained participants (65.7% female, age range: 18–22 years) were randomly assigned to two groups (MMG or CG). Participants in MMG were given a single 10-min recorded mindfulness meditation training before and when listening to music. The instruments for psychological measurement comprised of the Five Facet Mindfulness Questionnaire (FFMQ) and the Positive and Negative Affect Schedule (PANAS). Self-report results showed no significant between-group differences for PANAS and for the scores of four subscales of the FFMQ (p > 0.05 throughout), except for the non-judging of inner experience subscale. Results showed that temporary mindfulness meditation training decreased the negative emotional experiences of happy and sad music and the positive emotional experiences of calm music during recognition and experience and promoted beautiful musical experiences in individuals with no musical training. Maintaining a state of mindfulness while listening to music enhanced body awareness and led to experiencing a faster passage of musical time. In addition, it was found that Chinese classical folk instrumental musical works effectively induced aesthetic emotion and produced multidimensional aesthetic experiences among non-musically trained adults. This study provides new insights into the relationship between mindfulness and music emotion.

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

The Effect of Familiarity on Neural Representations of Music and Language.

We investigated how familiarity alters music and language processing in the brain. We used fMRI to measure brain responses before and after participants were familiarized with novel music and language stimuli. To manipulate the presence of language and music in the stimuli, there were four conditions: (1) whole music (music and words together), (2) instrumental music (no words), (3) a capella music (sung words, no instruments), and (4) spoken words. To manipulate participants' familiarity with the stimuli, we used novel stimuli and a familiarization paradigm designed to mimic "natural" exposure, while controlling for autobiographical memory confounds. Participants completed two fMRI scans that were separated by a stimulus training period. Behaviorally, participants learned the stimuli over the training period. However, there were no significant neural differences between the familiar and unfamiliar stimuli in either univariate or multivariate analyses. There were differences in neural activity in frontal and temporal regions based on the presence of language in the stimuli, and these differences replicated across the two scanning sessions. These results indicate that the way we engage with music is important for creating a memory of that music, and these aspects, over and above familiarity on its own, may be responsible for the robust nature of musical memory in the presence of neurodegenerative disorders such as Alzheimer disease.

Announcements

May 31 2022 Announcements Author and Article Information Online Issn: 1531-5169 Print Issn: 0148-9267 © 2022 Massachusetts Institute of Technology2022Massachusetts Institute of Technology Computer Music Journal (2022) 45 (2): 4. https://doi.org/10.1162/comj_e_00603 Cite Icon Cite Permissions Share Icon Share Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Announcements. Computer Music Journal 2022; 45 (2): 4. doi: https://doi.org/10.1162/comj_e_00603 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsComputer Music Journal Search Advanced Search The 23rd International Society for Music Information Retrieval (ISMIR) conference will be held in Bangalore, India, 4–8 December 2022. The annual conference is a leading research forum related to processing, analyzing, searching, organizing, and accessing music-related data. The 23rd ISMIR conference has welcomed contributions related to any aspect of music information research including foundations and theories for music processing, analysis, algorithms, evaluation, and applications. ISMIR aims to be an interdisciplinary research community involving researchers, developers, educators, librarians, students, and professionals from various disciplines such as computer science, electrical engineering, musicology, cognitive science, and library and information sciences. The ISMIR conference provides a venue for the exchange of ideas, issues, results, and perspectives among different profiles of people working with music and computing in a broad sense. In addition to established research topics and tasks, ISMIR has encouraged submissions that pose new ideas and challenges. Submissions of novel music-related topics, tasks,... You do not currently have access to this content.

An Educational Guide through the FMP Notebooks for Teaching and Learning Fundamentals of Music Processing

This paper provides a guide through the FMP notebooks, a comprehensive collection of educational material for teaching and learning fundamentals of music processing (FMP) with a particular focus on the audio domain. Organized in nine parts that consist of more than 100 individual notebooks, this collection discusses well-established topics in music information retrieval (MIR) such as beat tracking, chord recognition, music synchronization, audio fingerprinting, music segmentation, and source separation, to name a few. These MIR tasks provide motivating and tangible examples that students can hold onto when studying technical aspects in signal processing, information retrieval, or pattern analysis. The FMP notebooks comprise detailed textbook-like explanations of central techniques and algorithms combined with Python code examples that illustrate how to implement the methods. All components, including the introductions of MIR scenarios, illustrations, sound examples, technical concepts, mathematical details, and code examples, are integrated into a unified framework based on Jupyter notebooks. Providing a platform with many baseline implementations, the FMP notebooks are suited for conducting experiments and generating educational material for lectures, thus addressing students, teachers, and researchers. While giving a guide through the notebooks, this paper’s objective is to yield concrete examples on how to use the FMP notebooks to create an enriching, interactive, and interdisciplinary supplement for studies in science, technology, engineering, and mathematics. The FMP notebooks (including HTML exports) are publicly accessible under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Neonatal Music Therapy and Cerebral Oxygenation in Extremely and Very Preterm Infants: a Pilot Study

Music therapy is a novel intervention that may minimize neonatal stress. The mechanism of action is still largely unknown. We hypothesized that one mechanism of action regards altered brain oxygenation (either due to altered cerebral perfusion or altered cerebral oxygen consumption). We measured cerebral oxygenation before, during and after music therapy sessions using Near-Infrared Spectroscopy (NIRS). We extracted data on cerebral oxygen saturation (rcSO2) and calculated cerebral fractional tissue oxygen extraction (cFTOE). In addition, we measured heart rate. We included 20 infants, receiving 44 music therapy sessions. Median gestational age was 27 weeks, the majority were males. We identified two distinct reactions: in one group rcSO2 increased and cFTOE decreased during therapy compared with before therapy, whereas in the other group rcSO2 decreased and cFTOE increased during therapy compared with before therapy. The first may indicate a sedative effect, whereas the second may reflect a hyperalert state. The observed changes in heart rate may contribute to these observations through altered cerebral perfusion. The clinical significance of these two distinct reactions for music processing and (future) neurological functioning in these infants warrants further investigation.

Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

Can Music Influence Patients With Disorders of Consciousness? An Event-Related Potential Study.

BackgroundLong-term disorders of consciousness (DOC) are a huge burden on both patients and their families. Previously, music intervention has been attempted as a potential therapy in DOC, with results indicating an enhancement of arousal and awareness; yet, to date, there are limited studies on music interventions in DOC with electroencephalogram monitoring. Meanwhile, prediction of awareness recovery is a challenge facing clinicians. The predictive value mismatch negativity (MMN), as a classical cognitive component in event-related potential, is still controversial. In this study, we use auditory event-related potential to probe the effect of music in DOC, and investigate whether music may improve the predictive value of MMN in awareness recovery.MethodsFourteen DOC patients were included in the prospective study. Auditory oddball electroencephalogram data were recorded twice with each patient, before and after 5 min of listening to a Chinese symphony that has joyful associations. The outcome was assessed 6 months later.ResultsSignificant differences of MMN amplitude were found between healthy controls and pre-music DOC patients (p < 0.001), but no significant differences were found between healthy controls and post-music DOC patients. The presence of MMN before music was not correlated with favorable outcome, and 50% of patients with MMN did not recover awareness. When MMN was absent, 50% of patients awoke. After listening to music, among the 11 patients who showed MMN, seven patients recovered awareness. When MMN was absent, no one recovered awareness.ConclusionsSome DOC patients, even those in a minimal consciousness state and those with unresponsive wakefulness syndrome (UWS), were affected by music. The MMN amplitude was elevated by the music to some extent. A single test of MMN did not have a good prognostic value of our study; however, retesting of MMN after stimulation with familiar music that has joyful associations might be valuable for observation and detection of possible recovery. The musical processing in DOC patients and the effect of musical therapeutic practices need further investigations.

Signal Processing and Machine Learning Approaches and Evaluation for Indian Classical Music

In the field of signal processing, a considerable amount of work has been done in the analysis and implementation of various techniques related to speech signal processing. When it comes to the Music signal processing, the techniques that are originally developed for speech signal processing have been applied to music signal with good results. In comparison with Indian classical music, lot of research work done in Western classical music on music audio analysis like information retrieval, singer/instrument identification, genre detection, etc.,. This paper provides an overview of some signal processing, and machine learning techniques that specifically applied to the Indian classical music for analyzing and identifying features such as tonic identification, genre classification, raga recognition, music transcription, rhythm, and timbre. We will look at how particular features of Indian Classical music signals impact and how these features are extracted.

Oscillatory Entrainment of the Frequency-following Response in Auditory Cortical and Subcortical Structures.

There is much debate about the existence and function of neural oscillatory mechanisms in the auditory system. The frequency-following response (FFR) is an index of neural periodicity encoding that can provide a vehicle to study entrainment in frequency ranges relevant to speech and music processing. Criteria for entrainment include the presence of poststimulus oscillations and phase alignment between stimulus and endogenous activity. To test the hypothesis of entrainment, in experiment 1 we collected FFR data for a repeated syllable using magnetoencephalography (MEG) and electroencephalography in 20 male and female human adults. We observed significant oscillatory activity after stimulus offset in auditory cortex and subcortical auditory nuclei, consistent with entrainment. In these structures, the FFR fundamental frequency converged from a lower value over 100 ms to the stimulus frequency, consistent with phase alignment, and diverged to a lower value after offset, consistent with relaxation to a preferred frequency. In experiment 2, we tested how transitions between stimulus frequencies affected the MEG FFR to a train of tone pairs in 30 people. We found that the FFR was affected by the frequency of the preceding tone for up to 40 ms at subcortical levels, and even longer durations at cortical levels. Our results suggest that oscillatory entrainment may be an integral part of periodic sound representation throughout the auditory neuraxis. The functional role of this mechanism is unknown, but it could serve as a fine-scale temporal predictor for frequency information, enhancing stability and reducing susceptibility to degradation that could be useful in real-life noisy environments.SIGNIFICANCE STATEMENT Neural oscillations are proposed to be a ubiquitous aspect of neural function, but their contribution to auditory encoding is not clear, particularly at higher frequencies associated with pitch encoding. In a magnetoencephalography experiment, we found converging evidence that the frequency-following response has an oscillatory component according to established criteria: poststimulus resonance, progressive entrainment of the neural frequency to the stimulus frequency, and relaxation toward the original state on stimulus offset. In a second experiment, we found that the frequency and amplitude of the frequency-following response to tones are affected by preceding stimuli. These findings support the contribution of intrinsic oscillations to the encoding of sound, and raise new questions about their functional roles, possibly including stabilization and low-level predictive coding.

The Role of Canalization and Plasticity in the Evolution of Musical Creativity.

Creativity is defined as the ability to generate something new and valuable. From a biological point of view this can be seen as an adaptation in response to environmental challenges. Although music is such a diverse phenomenon, all people possess a set of abilities that are claimed to be the products of biological evolution, which allow us to produce and listen to music according to both universal and culture-specific rules. On the one hand, musical creativity is restricted by the tacit rules that reflect the developmental interplay between genetic, epigenetic and cultural information. On the other hand, musical innovations seem to be desirable elements present in every musical culture which suggests some biological importance. If our musical activity is driven by biological needs, then it is important for us to understand the function of musical creativity in satisfying those needs, and also how human beings have become so creative in the domain of music. The aim of this paper is to propose that musical creativity has become an indispensable part of the gene-culture coevolution of our musicality. It is suggested that the two main forces of canalization and plasticity have been crucial in this process. Canalization is an evolutionary process in which phenotypes take relatively constant forms regardless of environmental and genetic perturbations. Plasticity is defined as the ability of a phenotype to generate an adaptive response to environmental challenges. It is proposed that human musicality is composed of evolutionary innovations generated by the gradual canalization of developmental pathways leading to musical behavior. Within this process, the unstable cultural environment serves as the selective pressure for musical creativity. It is hypothesized that the connections between cortical and subcortical areas, which constitute cortico-subcortical circuits involved in music processing, are the products of canalization, whereas plasticity is achieved by the means of neurological variability. This variability is present both at the level of an individual structure’s enlargement in response to practicing (e.g., the planum temporale) and within the involvement of neurological structures that are not music-specific (e.g., the default mode network) in music processing.

Non-local musical statistics as guides for audio-to-score piano transcription

We present an automatic piano transcription system that converts polyphonic audio recordings into musical scores. This has been a long-standing problem of music information processing, and recent studies have made remarkable progress in the two main component techniques: multipitch detection and rhythm quantization. Given this situation, we study a method integrating deep-neural-network-based multipitch detection and statistical-model-based rhythm quantization. In the first part, we conducted systematic evaluations and found that while the present method achieved high transcription accuracies at the note level, some global characteristics of music, such as tempo scale, metre (time signature), and bar line positions, were often incorrectly estimated. In the second part, we formulated non-local statistics of pitch and rhythmic contents that are derived from musical knowledge and studied their effects in inferring those global characteristics. We found that these statistics are markedly effective for improving the transcription results and that their optimal combination includes statistics obtained from separated hand parts. The integrated method had an overall transcription error rate of 7.1% and a downbeat F-measure of 85.6% on a dataset of popular piano music, and the generated transcriptions can be partially used for music performance and assisting human transcribers, thus demonstrating the potential for practical applications.

Stylistic Composition of Melodies Based on a Brain-Inspired Spiking Neural Network.

Current neural network based algorithmic composition methods are very different compared to human brain's composition process, while the biological plausibility of composition and generative models are essential for the future of Artificial Intelligence. To explore this problem, this paper presents a spiking neural network based on the inspiration from brain structures and musical information processing mechanisms at multiple scales. Unlike previous methods, our model has three novel characteristics: (1) Inspired by brain structures, multiple brain regions with different cognitive functions, including musical memory and knowledge learning, are simulated and cooperated to generate stylistic melodies. A hierarchical neural network is constructed to formulate musical knowledge. (2) Biologically plausible neural model is employed to construct the network and synaptic connections are modulated using spike-timing-dependent plasticity (STDP) learning rule. Besides, brain oscillation activities with different frequencies perform importantly during the learning and generating process. (3) Based on significant musical memory and knowledge learning, genre-based and composer-based melody composition can be achieved by different neural circuits, the experiments show that the model can compose melodies with different styles of composers or genres.

Emotion Recognition and Regulation Based on Stacked Sparse Auto-Encoder Network and Personalized Reconfigurable Music

Music can regulate and improve the emotions of the brain. Traditional emotional regulation approaches often adopt complete music. As is well-known, complete music may vary in pitch, volume, and other ups and downs. An individual’s emotions may also adopt multiple states, and music preference varies from person to person. Therefore, traditional music regulation methods have problems, such as long duration, variable emotional states, and poor adaptability. In view of these problems, we use different music processing methods and stacked sparse auto-encoder neural networks to identify and regulate the emotional state of the brain in this paper. We construct a multi-channel EEG sensor network, divide brainwave signals and the corresponding music separately, and build a personalized reconfigurable music-EEG library. The 17 features in the EEG signal are extracted as joint features, and the stacked sparse auto-encoder neural network is used to classify the emotions, in order to establish a music emotion evaluation index. According to the goal of emotional regulation, music fragments are selected from the personalized reconfigurable music-EEG library, then reconstructed and combined for emotional adjustment. The results show that, compared with complete music, the reconfigurable combined music was less time-consuming for emotional regulation (76.29% less), and the number of irrelevant emotional states was reduced by 69.92%. In terms of adaptability to different participants, the reconfigurable music improved the recognition rate of emotional states by 31.32%.

Resting-State Network Plasticity Induced by Music Therapy after Traumatic Brain Injury.

Traumatic brain injury (TBI) is characterized by a complex pattern of abnormalities in resting-state functional connectivity (rsFC) and network dysfunction, which can potentially be ameliorated by rehabilitation. In our previous randomized controlled trial, we found that a 3-month neurological music therapy intervention enhanced executive function (EF) and increased grey matter volume in the right inferior frontal gyrus (IFG) in patients with moderate-to-severe TBI (N = 40). Extending this study, we performed longitudinal rsFC analyses of resting-state fMRI data using a ROI-to-ROI approach assessing within-network and between-network rsFC in the frontoparietal (FPN), dorsal attention (DAN), default mode (DMN), and salience (SAL) networks, which all have been associated with cognitive impairment after TBI. We also performed a seed-based connectivity analysis between the right IFG and whole-brain rsFC. The results showed that neurological music therapy increased the coupling between the FPN and DAN as well as between these networks and primary sensory networks. By contrast, the DMN was less connected with sensory networks after the intervention. Similarly, there was a shift towards a less connected state within the FPN and SAL networks, which are typically hyperconnected following TBI. Improvements in EF were correlated with rsFC within the FPN and between the DMN and sensorimotor networks. Finally, in the seed-based connectivity analysis, the right IFG showed increased rsFC with the right inferior parietal and left frontoparietal (Rolandic operculum) regions. Together, these results indicate that the rehabilitative effects of neurological music therapy after TBI are underpinned by a pattern of within- and between-network connectivity changes in cognitive networks as well as increased connectivity between frontal and parietal regions associated with music processing.

Influence of input data representations for time-dependent instrument recognition

Abstract An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. As additional phase representations, the product spectrum (PS), based on the modified group delay, and the frequency error (FE) matrix, related to the instantaneous frequency, are used. Training and evaluation processes are executed based on the MusicNet dataset, which enables the estimation of seven instruments. With a higher number of frequency bins in the input representations, an improved instrument recognition of about 2 % in F1-score can be achieved. Compared to the literature, frame-level instrument recognition can be improved for different input representations.

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.

Indonesian Music Classification on Folk and Dangdut Genre Based on Rolloff Spectral Feature Using Support Vector Machine (SVM) Algorithm

Music Genre Classification is one of the interesting digital music processing topics. Genre is a category of artistry, in this case, especially music, to characterize and categorize music is now available in various forms and sources. One of the applications is in determining the music genre classification on folk songs and dangdut songs.The main problem in the classification music genre is to find a combination of features and classifiers that can provide the best result in classifying music files into music genres. So we need to develop methods and algorithms that can classify genres appropriately. This problem can be solved by using the Support Vector Machine (SVM). The genre classification process begins by selecting the song file that will be classified by the genre, then the preprocessing process, the collection features by utilizing feature extraction, and the last process is Support Vector Machine (SVM) classification process to produce genre types from selected song files. The final result of this research is to classify Indonesian folk music genre and dangdut music genre along with the 83.3% accuracy values that indicate the level of system relevance to the results of music genre classification and to provide genre labels on music files as to facilitate the management and search of music files.

Influence of Background Musical Emotions on Attention in Congenital Amusia.

Congenital amusia in its most common form is a disorder characterized by a musical pitch processing deficit. Although pitch is involved in conveying emotion in music, the implications for pitch deficits on musical emotion judgements is still under debate. Relatedly, both limited and spared musical emotion recognition was reported in amusia in conditions where emotion cues were not determined by musical mode or dissonance. Additionally, assumed links between musical abilities and visuo-spatial attention processes need further investigation in congenital amusics. Hence, we here test to what extent musical emotions can influence attentional performance. Fifteen congenital amusic adults and fifteen healthy controls matched for age and education were assessed in three attentional conditions: executive control (distractor inhibition), alerting, and orienting (spatial shift) while music expressing either joy, tenderness, sadness, or tension was presented. Visual target detection was in the normal range for both accuracy and response times in the amusic relative to the control participants. Moreover, in both groups, music exposure produced facilitating effects on selective attention that appeared to be driven by the arousal dimension of musical emotional content, with faster correct target detection during joyful compared to sad music. These findings corroborate the idea that pitch processing deficits related to congenital amusia do not impede other cognitive domains, particularly visual attention. Furthermore, our study uncovers an intact influence of music and its emotional content on the attentional abilities of amusic individuals. The results highlight the domain-selectivity of the pitch disorder in congenital amusia, which largely spares the development of visual attention and affective systems.

Feasibility analysis of letting multi-channel surround sound system into college singing class

The multi-channel surround system used for multimedia teaching can bring more powerful auditory effects to the audience, and can fully mobilize students’ interest and enthusiasm in learning. Such as music arrangement, Yunnan musical instruments, the sound characteristics of national music and national musical instruments, and the sound processing knowledge of music processing. You can quickly master basic music arrangement, the sound characteristics of musical instruments, and the knowledge of mixing and processing music. Achieve better teaching results. Most multimedia devices on the market, with the music and sound effects, a high compression mono or stereo transmission. This mono or stereo transmission with a high compression ratio is very low quality. Since audio is far less powerful than visual impact, and the capacity of the disc used to store audio is small, people tend to focus on visual information processing, but ignore the importance of audio. Multi-channel surround sound technology has received more and more attention in the teaching practice of film and television colleges, but it is also affected by various factors such as venue facilities. This has caused bottlenecks in teaching in this field and has greatly affected the teaching effect. This paper designs and constructs a mobile multi-channel surround sound teaching system from a practical perspective. As part of a multimedia teaching device, try to use the best digital surround sound system for today’s audio reproduction. The feasibility of entering the digital surround sound system into the multimedia teaching classroom is analyzed. Better promote students’ correct understanding of audio and use modern high-tech digital technology to spread real art.