Interaction features for prediction of perceptual segmentation:Effects of musicianship and experimental task

As music unfolds in time, structure is recognised and understood by listeners, regardless of their level of musical expertise. A number of studies have found spectral and tonal changes to quite successfully model boundaries between structural sections. However, the effects of musical expertise and experimental task on computational modelling of structure are not yet well understood. These issues need to be addressed to better understand how listeners perceive the structure of music and to improve automatic segmentation algorithms. In this study, computational prediction of segmentation by listeners was investigated for six musical stimuli via a real-time task and an annotation (non real-time) task. The proposed approach involved computation of novelty curve interaction features and a prediction model of perceptual segmentation boundary density. We found that, compared to non-musicians’, musicians’ segmentation yielded lower prediction rates, and involved more features for prediction, particularly more interaction features; also non-musicians required a larger time shift for optimal segmentation modelling. Prediction of the annotation task exhibited higher rates, and involved more musical features than for the real-time task; in addition, the real-time task required time shifting of the segmentation data for its optimal modelling. We also found that annotation task models that were weighted according to boundary strength ratings exhibited improvements in segmentation prediction rates and involved more interaction features. In sum, musical training and experimental task seem to have an impact on prediction rates and on musical features involved in novelty-based segmentation models. Musical training is associated with higher presence of schematic knowledge, attention to more dimensions of musical change and more levels of the structural hierarchy, and higher speed of musical structure processing. Real-time segmentation is linked with higher response delays, less levels of structural hierarchy attended and higher data noisiness than annotation segmentation. In addition, boundary strength weighting of density was associated with more emphasis given to stark musical changes and to clearer representation of a hierarchy involving high-dimensional musical changes.peerReviewe

Naturalistic music and dance : Cortical phase synchrony in musicians and dancers

Expertise in music has been investigated for decades and the results have been applied not only in composition, performance and music education, but also in understanding brain plasticity in a larger context. Several studies have revealed a strong connection between auditory and motor processes and listening to and performing music, and music imagination. Recently, as a logical next step in music and movement, the cognitive and affective neuro-sciences have been directed towards expertise in dance. To understand the versatile and overlapping processes during artistic stimuli, such as music and dance, it is necessary to study them with continuous naturalistic stimuli. Thus, we used long excerpts from the contemporary dance piece Carmen presented with and without music to professional dancers, musicians, and laymen in an EEG laboratory. We were interested in the cortical phase synchrony within each participant group over several frequency bands during uni- and multimodal processing. Dancers had strengthened theta and gamma synchrony during music relative to silence and silent dance, whereas the presence of music decreased systematically the alpha and beta synchrony in musicians. Laymen were the only group of participants with significant results related to dance. Future studies are required to understand whether these results are related to some other factor (such as familiarity to the stimuli), or if our results reveal a new point of view to dance observation and expertise.Peer reviewe

Redefining groove

Groove is a popular and widely-used concept in the field of music. Yet its precise definition remains elusive. Upon closer inspection, groove appears to be used as an umbrella term with various connotations depending on the musical era, the musical context, and the individual using the term. Our aim in this paper was to explore different definitions and connotations of the term groove so as to reach a more detailed understanding of it. Consequently, in an online survey, 88 participants provided free-text descriptions of the term groove. A thematic analysis revealed that participants’ descriptions fit into three main categories: music-, experience-, and individual differences related aspects. Based upon this analysis, we propose a contemporary working definition of the term groove as used in the field of music psychology: “Groove is a participatory experience (related to immersion, movement, enjoyment, and social connection) resulting from subtle interaction of specific music- (such as time- and pitch-related features), performance- and/or individual difference-related factors.” Furthermore, we propose the terms perceived and induced groove to distinguish the different aspects of groove that are associated with its perceived musical features and induced effects on listeners. Importantly, this specification will permit further research with a common language to refer to distinct aspects of groove and thus create a more profound understanding in groove literature. Finally, we direct future studies to focus on the concept of groove under influence of different variables, such as the roles of individual differences (such as age, expertise and personality traits), execution of overt movements, or presences of others on listeners’ perceived and induced groove experiences. These will further elucidate our understanding of what groove actually is

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.Comment: 6 pages. 8 figures. Copyright (c) 2013 IEEE. Published at 2013 IEEE International Workshop on Machine Learning for Signal Processin

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Musical training predicts cerebello-hippocampal coupling during music listening.

Cerebello-hippocampal interactions occur during accurate spatiotemporal prediction of movements. In the context of music listening, differences in cerebello-hippocampal functional connectivity may result from differences in predictive listening accuracy. Using functional MRI, we studied differences in this network between 18 musicians and 18 nonmusicians while they listened to music. Musicians possess a predictive listening advantage over nonmusicians, facilitated by strengthened coupling between produced and heard sounds through lifelong musical experience. Thus, we hypothesized that musicians would exhibit greater functional connectivity than nonmusicians as a marker of accurate online predictions during music listening. To this end, we estimated the functional connectivity between cerebellum and hippocampus as modulated by a perceptual measure of the predictability of the music. Results revealed increased predictability-driven functional connectivity in this network in musicians compared with nonmusicians, which was positively correlated with the length of musical training. Findings may be explained by musicians’ improved predictive listening accuracy. Our findings advance the understanding of cerebellar integrative function.Peer reviewe

Fractionating auditory priors : A neural dissociation between active and passive experience of musical sounds

Learning, attention and action play a crucial role in determining how stimulus predictions are formed, stored, and updated. Years-long experience with the specific repertoires of sounds of one or more musical styles is what characterizes professional musicians. Here we contrasted active experience with sounds, namely long-lasting motor practice, theoretical study and engaged listening to the acoustic features characterizing a musical style of choice in professional musicians with mainly passive experience of sounds in laypersons. We hypothesized that long-term active experience of sounds would influence the neural predictions of the stylistic features in professional musicians in a distinct way from the mainly passive experience of sounds in laypersons. Participants with different musical backgrounds were recruited: professional jazz and classical musicians, amateur musicians and non-musicians. They were presented with a musical multi-feature paradigm eliciting mismatch negativity (MMN), a prediction error signal to changes in six sound features for only 12 minutes of electroencephalography (EEG) and magnetoencephalography (MEG) recordings. We observed a generally larger MMN amplitudes-indicative of stronger automatic neural signals to violated priors-in jazz musicians (but not in classical musicians) as compared to non-musicians and amateurs. The specific MMN enhancements were found for spectral features (timbre, pitch, slide) and sound intensity. In participants who were not musicians, the higher preference for jazz music was associated with reduced MMN to pitch slide (a feature common in jazz music style). Our results suggest that long-lasting, active experience of a musical style is associated with accurate neural priors for the sound features of the preferred style, in contrast to passive listening.Peer reviewe

Early auditory processing in musicians and dancers during a contemporary dance piece

The neural responses to simple tones and short sound sequences have been studied extensively. However, in reality the sounds surrounding us are spectrally and temporally complex, dynamic and overlapping. Thus, research using natural sounds is crucial in understanding the operation of the brain in its natural environment. Music is an excellent example of natural stimulation which, in addition to sensory responses, elicits vast cognitive and emotional processes in the brain. Here we show that the preattentive P50 response evoked by rapid increases in timbral brightness during continuous music is enhanced in dancers when compared to musicians and laymen. In dance, fast changes in brightness are often emphasized with a significant change in movement. In addition, the auditory N100 and P200 responses are suppressed and sped up in dancers, musicians and laymen when music is accompanied with a dance choreography. These results were obtained with a novel event-related potential (ERP) method for natural music. They suggest that we can begin studying the brain with long pieces of natural music using the ERP method of electroencephalography (EEG) as has already been done with functional magnetic resonance (fMRI), these two brain imaging methods complementing each other.Peer reviewe