Musical Beat Research Articles

Links between musical beat perception and phonological skills for autistic children

ABSTRACT Exploring non-linguistic predictors of phonological awareness, such as musical beat perception, is valuable for children who present with language difficulties and diverse support needs. Studies on the musical abilities of children on the autism spectrum show that they have average or above-average musical production and auditory processing abilities. This study aimed to explore the relationship between musical beat perception and phonological awareness skills of children on the autism spectrum with a wide range of cognitive abilities. A total of 21 autistic children between the ages of 6 to 11 years old (M = 8.9, SD = 1.5) with full scale IQs ranging from 52 to 105 (M = 74, SD = 16) completed a beat perception and a phonological awareness task. Results revealed that phonological awareness and beat perception are positively correlated for children on the autism spectrum. Findings lend support to the potential use of beat and rhythm perception as a screening tool for early literacy skills, specifically for phonological awareness, for children with diverse support needs as an alternative to traditional verbal tasks that tend to underestimate the potential of children on the autism spectrum.

Language experience predicts music processing in a half-million speakers of fifty-four languages

Tonal languages differ from other languages in their use of pitch (tones) to distinguish words. Lifelong experience speaking and hearing tonal languages has been argued to shape auditory processing in ways that generalize beyond the perception of linguistic pitch to the perception of pitch in other domains like music. We conducted a meta-analysis of prior studies testing this idea, finding moderate evidence supporting it. But prior studies were limited by mostly small sample sizes representing a small number of languages and countries, making it challenging to disentangle the effects of linguistic experience from variability in music training, cultural differences, and other potential confounds. To address these issues, we used web-based citizen science to assess music perception skill on a global scale in 34,034 native speakers of 19 tonal languages (e.g., Mandarin, Yoruba). We compared their performance to 459,066 native speakers of other languages, including 6 pitch-accented (e.g., Japanese) and 29 non-tonal languages (e.g., Hungarian). Whether or not participants had taken music lessons, native speakers of all 19 tonal languages had an improved ability to discriminate musical melodies on average, relative to speakers of non-tonal languages. But this improvement came with a trade-off: tonal language speakers were also worse at processing the musical beat. The results, which held across native speakers of many diverse languages and were robust to geographic and demographic variation, demonstrate that linguistic experience shapes music perception, with implications for relations between music, language, and culture in the human mind.

Caressed by music: Related preferences for velocity of touch and tempo of music?

Given that both hearing and touch are 'mechanical senses' that respond to physical pressure or mechanical energy and that individuals appear to have a characteristic internal or spontaneous tempo, individual preferences in musical and touch rhythms might be related. We explored this in two experiments probing individual preferences for tempo in the tactile and auditory modalities. Study 1 collected ratings of received stroking on the forearm and measured the velocity the participants used for stroking a fur. Music tempo preferences were assessed as mean beats per minute of individually selected music pieces and via the adjustment of experimenter-selected music to a preferred tempo. Heart rate was recorded to measure levels of physiological arousal. We found that the preferred tempo of favorite (self-selected) music correlated positively with the velocity with which each individual liked to be touched. In Study 2, participants rated videos of repeated touch on someone else's arm and videos of a drummer playing with brushes on a snare drum, both at a variety of tempos. We found that participants with similar rating patterns for the different stroking speeds did not show similar rating patterns for the different music beats. The results suggest that there may be a correspondence between preferences for favorite music and felt touch, but this is either weak or it cannot be evoked effectively with vicarious touch and/or mere drum beats. Thus, if preferences for touch and music are related, this is likely to be dependent on the specific type of stimulation.

Musical Beat Perception Skills of Autistic and Neurotypical Children.

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

An Efficient Hidden Markov Model with Periodic Recurrent Neural Network Observer for Music Beat Tracking

In music information retrieval (MIR), beat tracking is one of the most fundamental tasks. To obtain this critical component from rhythmic music signals, a previous beat tracking system of hidden Markov model (HMM) with a recurrent neural network (RNN) observer was developed. Although the frequency of music beat is quite stable, existing HMM based methods do not take this feature into account. Accordingly, most of hidden states in these HMM-based methods are redundant, which is a disadvantage for time efficiency. In this paper, we proposed an efficient HMM using hidden states by exploiting the frequency contents of the neural network’s observation with Fourier transform, which extremely reduces the computational complexity. Observers that previous works used, such as bi-directional recurrent neural network (Bi-RNN) and temporal convolutional network (TCN), cannot perceive the frequency of music beat. To obtain more reliable frequencies from music, a periodic recurrent neural network (PRNN) based on attention mechanism is proposed as well, which is used as the observer in HMM. Experimental results on open source music datasets, such as GTZAN, Hainsworth, SMC, and Ballroom, show that our efficient HMM with PRNN is competitive to the state-of-the-art methods and has lower computational cost.

How we speak when we speak to a beat: The influence of temporal coupling on phonetic enhancement

Stressed syllables in languages which have them tend to show two interesting properties: They show patterns of phonetic ‘enhancement’ at the articulatory and acoustic levels, and they also show coordinative properties. They typically play a key role in coordinating speech with co-speech gesture, in coordination with a musical beat, and in other sensorimotor synchronization tasks such as speech-coordinated beat tapping and metronome timing. While various phonological theories have considered stress from both of these perspectives, there is as yet no clear explanation as to how these properties relate to one another. The present work tests the hypothesis that aspects of phonetic enhancement may in fact be driven by coordination itself by observing how phonetic patterns produced by speakers of two prosodically-distinct languages—English and Medʉmba (Grassfields Bantu)—vary as a function of timing relations with an imaginary metronome beat. Results indicate that production of syllables in time (versus on the ‘offbeat’) with the imaginary beat led to increased duration and first formant frequency—two widely observed correlates of syllable stress—for speakers of both languages. These results support the idea that some patterns of phonetic enhancement may have their roots in coordinative practices. 

Retracted: Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Retracted: Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

변칙 리듬의 그루핑 구조 활용 방법 연구 - 알 자로의 리듬 표현기법 분석을 중심으로 -

This paper, a study on how to use anomalous rhythm in vocals. The purpose of this study is to suggest a method so that musicians, such as composers, lyricists, arrangers,producers, and vocalists, who make songs that include songs can utilize more diverse and original vocal rhythms. The research method is to compare the melodies and scat of anomalous rhythms used in the album and live performances of representative fusion jazz vocalist Al Jarreau, and compare the structure of the music beat and the modified beat derived from the structural point of view of grouping at a glance. made it recognizable. As a result of the study, <Spain>, <Don't You Worry' 'bout a Thing> and <Step By Step> were performed in 4 A grouping structure that combines the lengths of quarter notes and 8th notes was used, and <Last Night> uses a grouping structure that combines the lengths of quarter note and 16th note for vocals. It was derived that unique songs and scat were created by applying anomalous grouping to the rhythm played by all instruments except for four beats per word.

Musical Meter Induces Interbrain Synchronization during Interpersonal Coordination.

Music induces people to coordinate with one another. Here, we conduct two experiments to examine the underlying mechanism of the interbrain synchronization (IBS) that is induced by interpersonal coordination when people are exposed to musical beat and meter. In experiment 1, brain signals at the frontal cortex were recorded simultaneously from two participants of a dyad by using functional near-infrared spectroscopy (fNIRS) hyperscanning, while each tapped their fingers to aural feedback from their partner (coordination task) or from themselves (independence task) with and without the musical meter. The results showed enhanced IBS at the left-middle frontal cortex in case of the coordination task with musical beat and meter. The IBS was significantly correlated with the participants performance in terms of coordination. In experiment 2, we further examined the IBS while the participants coordinated their behaviors in various metrical contexts, such as strong and weak meters (i.e., high/low loudness of acoustically accenting beats). The results showed that strong meters elicited higher IBS at the middle frontal cortex than weak meters. These findings reveal that the musical beat and meter can affect brain-to-brain coupling in action coordination between people, and provide insights into the interbrain mechanism underlying the effects of music on cooperation.

Recognition of musical beat and style and applications in interactive humanoid robot.

The musical beat and style recognition have high application value in music information retrieval. However, the traditional methods mostly use a convolutional neural network (CNN) as the backbone and have poor performance. Accordingly, the present work chooses a recurrent neural network (RNN) in deep learning (DL) to identify musical beats and styles. The proposed model is applied to an interactive humanoid robot. First, DL-based musical beat and style recognition technologies are studied. On this basis, a note beat recognition method combining attention mechanism (AM) and independent RNN (IndRNN) [AM-IndRNN] is proposed. The AM-IndRNN can effectively avoid gradient vanishing and gradient exploding. Second, the audio music files are divided into multiple styles using the music signal's temporal features. A human dancing robot using a multimodal drive is constructed. Finally, the proposed method is tested. The results show that the proposed AM-IndRNN outperforms multiple parallel long short-term memory (LSTM) models and IndRNN in recognition accuracy (88.9%) and loss rate (0.0748). Therefore, the AM-optimized LSTM model has gained a higher recognition accuracy. The research results provide specific ideas for applying DL technology in musical beat and style recognition.

In search of the beat

Beat perception has received very little attention from either philosophers of mind or philosophers of music. This neglect is unfortunate, for the topic is rich with philosophical interest. This article addresses two questions. The first concerns the nature of our experience of musical beat. Here, we argue that experiences of beat are forms of auditory perception. The second question concerns the nature of musical beat itself: what are beats? We defend a form of anthropocentric realism about beats: beats are mind‐independent properties of sound sequences whose nature can be understood only in relation to our perceptual capacities.

DanceFormer: Music Conditioned 3D Dance Generation with Parametric Motion Transformer

Generating 3D dances from music is an emerged research task that benefits a lot of applications in vision and graphics. Previous works treat this task as sequence generation, however, it is challenging to render a music-aligned long-term sequence with high kinematic complexity and coherent movements. In this paper, we reformulate it by a two-stage process, i.e., a key pose generation and then an in-between parametric motion curve prediction, where the key poses are easier to be synchronized with the music beats and the parametric curves can be efficiently regressed to render fluent rhythm-aligned movements. We named the proposed method as DanceFormer, which includes two cascading kinematics-enhanced transformer-guided networks (called DanTrans) that tackle each stage, respectively. Furthermore, we propose a large-scale music conditioned 3D dance dataset, called PhantomDance, that is accurately labeled by experienced animators rather than reconstruction or motion capture. This dataset also encodes dances as key poses and parametric motion curves apart from pose sequences, thus benefiting the training of our DanceFormer. Extensive experiments demonstrate that the proposed method, even trained by existing datasets, can generate fluent, performative, and music-matched 3D dances that surpass previous works quantitatively and qualitatively. Moreover, the proposed DanceFormer, together with the PhantomDance dataset, are seamlessly compatible with industrial animation software, thus facilitating the adaptation for various downstream applications.

Genome-wide association study of musical beat synchronization demonstrates high polygenicity

Moving in synchrony to the beat is a fundamental component of musicality. Here we conducted a genome-wide association study to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with 69 loci reaching genome-wide significance (P < 5 × 10−8) and single-nucleotide-polymorphism-based heritability (on the liability scale) of 13%–16%. Heritability was enriched for genes expressed in brain tissues and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central-nervous-system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through separate experiments) and of the genome-wide association study (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank). Genetic correlations with breathing function, motor function, processing speed and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.

Optimization Simulation of Dance Technical Movements and Music Matching Based on Multifeature Fusion.

Music and dance videos have been popular among researchers in recent years. Music is one of the most important forms of human communication; it carries a wealth of emotional information, and it is studied using computer tools. In the feature engineering process, most present machine learning approaches suffer from information loss or insufficient extracted features despite the relevance of computer interface and multimedia technologies in sound and music matching tasks. Multifeature fusion is widely utilized in education, aerospace, intelligent transportation, biomedicine, and other fields, and it plays a critical part in how humans get information. In this research, we offer an effective simulation method for matching dance technique movements with music based on multifeature fusion. The initial step is to use music beat extraction theory to segment the synchronized dance movements and music data, then locate mutation points in the music, and dynamically update the pheromones based on the merits of the dance motions. The audio feature sequence is obtained by extracting audio features from the dancing video's accompanying music. Then, we combine the two sequences to create an entropy value sequence based on audio variations. By comparing the consistency of several approaches for optimizing dance movement simulation trials, the optimized simulation method described in this research has an average consistency of 87%, indicating a high consistency. As a result, even though the background and the subject are readily confused, the algorithm in this research can keep a consistent recognition rate for more complicated dance background music, and the approach in this study can still guarantee a certain accuracy rate.

Pose Estimation-Assisted Dance Tracking System Based on Convolutional Neural Network.

In the field of music-driven, computer-assisted dance movement generation, traditional music movement adaptations and statistical mapping models have the following problems: Firstly, the dance sequences generated by the model are not powerful enough to fit the music itself. Secondly, the integrity of the dance movements produced is not sufficient. Thirdly, it is necessary to improve the suppleness and rationality of long-term dance sequences. Fourthly, traditional models cannot produce new dance movements. How to create smooth and complete dance gesture sequences after music is a problem that needs to be investigated in this paper. To address these problems, we design a deep learning dance generation algorithm to extract the association between sound and movement characteristics. During the feature extraction phase, rhythmic features extracted from music and audio beat features are used as musical features, and coordinates of the main points of human bones extracted from dance videos are used for training as movement characteristics. During the model building phase, the model's generator module is used to achieve a basic mapping of music and dance movements and to generate gentle dance gestures. The identification module is used to achieve consistency between dance and music. The self-encoder module is used to make the audio function more representative. Experimental results on the DeepFashion dataset show that the generated model can synthesize the new view of the target person in any human posture of a given posture, complete the transformation of different postures of the same person, and retain the external features and clothing textures of the target person. Using a whole-to-detail generation strategy can improve the final video composition. For the problem of incoherent character movements in video synthesis, we propose to optimize the character movements by using a generative adversarial network, specifically by inserting generated motion compensation frames into the incoherent movement sequences to improve the smoothness of the synthesized video.

Music Beat Generation through AI

Music Plays a significant role in our mental health, but composing a song is difficult for composers. While listening to music, Humans can quickly detect and move to beats. Similarly to Language, Music can be represented as a sequence of discrete symbols that form a hierarchical syntax, with notes being roughly like characters and motifs of notes like a word. Several Theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat is a question of how we learn to generate and hold a beat. In this paper, we introduce a Music Beat Generator that will interpolate music rhythms and notes that can be used by music artists, Using Artificial intelligence and Machine Learning. In the context of this project, rhythms are one long measure sequence of notes and rest occurring on natural pulse subdivisions of a beat. Music generation using machine learning techniques has been a topic of interest for the past two decades. Music proves to be a different challenge compared to images, among three main dimensions: Firstly, music is temporal, with a hierarchical structure with dependencies across time. Secondly, music consists of interdependent instruments that unfold across time. Thirdly, music is grouped into chords, arpeggios, and melodies – hence each time -step may have multiple outputs. Keywords – Machine Learning; Artificial Intelligence; Music Beats; Algorithm; Chords; Interpolate; Rhythms.

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.

The effects of Parkinson's disease, music training, and dance training on beat perception and production abilities.

Humans naturally perceive and move to a musical beat, entraining body movements to auditory rhythms through clapping, tapping, and dancing. Yet the accuracy of this seemingly effortless behavior varies widely across individuals. Beat perception and production abilities can be improved by experience, such as music and dance training, and impaired by progressive neurological changes, such as in Parkinson's disease. In this study, we assessed the effects of music and dance experience on beat processing in young and older adults, as well as individuals with early-stage Parkinson's disease. We used the Beat Alignment Test (BAT) to assess beat perception and production in a convenience sample of 458 participants (278 healthy young adults, 139 healthy older adults, and 41 people with early-stage Parkinson's disease), with varying levels of music and dance training. In general, we found that participants with over three years of music training had more accurate beat perception than those with less training (p < .001). Interestingly, Parkinson's disease patients with music training had beat production abilities comparable to healthy adults while Parkinson's disease patients with minimal to no music training performed significantly worse. No effects were found in healthy adults for dance training, and too few Parkinson's disease patients had dance training to reliably assess its effects. The finding that musically trained Parkinson's disease patients performed similarly to healthy adults during a beat production task, while untrained patients did not, suggests music training may preserve certain rhythmic motor timing abilities in early-stage Parkinson's disease.

Using beat frequency in music to adjust running cadence in recreational runners: A randomized multiple baseline design

ABSTRACT Running with music has been shown to acutely change cadence. However, it is unclear if the increased cadence remains long-term when running without music in an in-field situation. The aim of this 12-week study was to investigate the effect of a 4-week music running program on cadence, speed and heartrate during and after the music running program. Seven recreational runners with a cadence of <170 steps per minute were randomly assigned to a baseline and post-intervention period of different durations. During the intervention phase, the participants ran with a musical beat that was 7.5–10% higher than their mean cadence at the start of the study. Cadence, heartrate and running speed were measured twice a week during a 5-kilometer run with a watch, and were analyzed using randomization tests and visual data inspection. Two participants dropped-out due to shortage of time (n = 1) and an acute calf injury (n = 1). Cadence significantly increased during the intervention period (+8.5%), and remained elevated during the post-intervention period (+7.9% (p = .001)) in comparison with the baseline period. Heartrate and running speed did not significantly change during any period. This study among five participants shows that four weeks of running with a musical beat that is 7.5–10% higher than the preferred cadence may be an effective and feasible intervention to increase running cadence. Importantly, the increased cadence occurred without simultaneous increases in running speed and heartrate, hereby potentially reducing mechanical loading without increasing metabolic load. Highlights Running with a musical rhythm that is higher than the preferred cadence leads to an increased running cadence, without increasing heartrate and running speed. This cadence remains elevated for at least three to five weeks after the music intervention period. All individuals showed a practically relevant increase in cadence during and after the intervention.

Test of Prosody via Syllable Emphasis ("TOPsy"): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception.

Prosody perception is fundamental to spoken language communication as it supports comprehension, pragmatics, morphosyntactic parsing of speech streams, and phonological awareness. A particular aspect of prosody: perceptual sensitivity to speech rhythm patterns in words (i.e., lexical stress sensitivity), is also a robust predictor of reading skills, though it has received much less attention than phonological awareness in the literature. Given the importance of prosody and reading in educational outcomes, reliable and valid tools are needed to conduct large-scale health and genetic investigations of individual differences in prosody, as groundwork for investigating the biological underpinnings of the relationship between prosody and reading. Motivated by this need, we present the Test of Prosody via Syllable Emphasis (“TOPsy”) and highlight its merits as a phenotyping tool to measure lexical stress sensitivity in as little as 10 min, in scalable internet-based cohorts. In this 28-item speech rhythm perception test [modeled after the stress identification test from Wade-Woolley (2016)], participants listen to multi-syllabic spoken words and are asked to identify lexical stress patterns. Psychometric analyses in a large internet-based sample shows excellent reliability, and predictive validity for self-reported difficulties with speech-language, reading, and musical beat synchronization. Further, items loaded onto two distinct factors corresponding to initially stressed vs. non-initially stressed words. These results are consistent with previous reports that speech rhythm perception abilities correlate with musical rhythm sensitivity and speech-language/reading skills, and are implicated in reading disorders (e.g., dyslexia). We conclude that TOPsy can serve as a useful tool for studying prosodic perception at large scales in a variety of different settings, and importantly can act as a validated brief phenotype for future investigations of the genetic architecture of prosodic perception, and its relationship to educational outcomes.