Retentional Syntagmatic Network, and its Use in Motivic Analysis of Maqam Improvisation

In this paper is defined a concept of Retentional Syntagmatic Network (RSN), which models the connectivity between temporally closed notes. The RSN formalizes the Schenkerian notion of pitch prolongation as a concept of syntagmatic retention, whose characteristics are dependent on the underlying modal context. This framework enables to formalize the syntagmatic role of ornamentation, and allows an automation of motivic analysis that takes into account melodic transformations. The model is applied to the analysis of a maqam improvisation. The RSN is also proposed as a way to surpass strict hierarchical segmentation models, which in our view cannot sufficiently describe the richness of musical structure. Instead of separability, we propose to focus instead on the connectivity between notes, modeled with the help of RSNs

Discovering Musical Pattern through Perceptive Heuristics.

This paper defends the view that the intricate difficulties challenging the emerging domain of Musical Pattern Discovery, which is dedicated to the automation of motivic analysis, will be overcome only through a thorough taking into account of the specificity of music as a perceptive object. Actual musical patterns, although constantly transformed, are nevertheless perceived by the listener as musical identities. Such dynamical properties of human perception, not reducible to geometrical models, will only be explained with the notions of contexts and expectations. This paper sketches the general principles of a new approach that attempts to build such a general perceptual system. On a sub-cognitive level, patterns are discovered through the detection, by an associative memory, of local similarities. On a cognitive level, patterns are managed by a general logical framework that avoids irrelevant inferences and combinatorial explosion. In this way, actual musical patterns that convey musical significance are discovered. This approach, offering promising results, is a first step toward a complete system of automated music analysis and an explicit modeling of basic mechanisms for music understanding

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

Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation

This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis

L’ethnomusicologie computationnelle : pour un renouveau de la discipline

Près de dix ans après la publication de l’article Computational Ethnomusicology (2007), l’ethnomusicologie computationnelle s’est largement développée et a conquis une légitimité institutionnelle et académique. Pourtant, peu d’ethnomusicologues – au sens traditionnel du terme – investissent ce champ de recherche. A la lumière de collaborations menées ces dernières années sur des problématiques diverses (le timbre instrumental de cordophones indiens ou la construction motivique de chants éthiopiens), nous examinons la nature des différentes approches de l’ethnomusicologie computationnelle, les obstacles qui se dressent lorsqu’on aborde cette discipline et enfin en quoi l’approche computationnelle peut apporter un renouveau épistémologique et méthodologique, et constituer une véritable opportunité de renouveau (inter)disciplinaire pour l’ethnomusicologie