SuPP & MaPP: Adaptable Structure-Based Representations For Mir Tasks

Accurate and flexible representations of music data are paramount to addressing MIR tasks, yet many of the existing approaches are difficult to interpret or rigid in nature. This work introduces two new song representations for structure-based retrieval methods: Surface Pattern Preservation (SuPP), a continuous song representation, and Matrix Pattern Preservation (MaPP), SuPP’s discrete counterpart. These representations come equipped with several user-defined parameters so that they are adaptable for a range of MIR tasks. Experimental results show MaPP as successful in addressing the cover song task on a set of Mazurka scores, with a mean precision of 0.965 and recall of 0.776. SuPP and MaPP also show promise in other MIR applications, such as novel-segment detection and genre classification, the latter of which demonstrates their suitability as inputs for machine learning problems

Explaining Listener Differences in the Perception of Musical Structure

PhDState-of-the-art models for the perception of grouping structure in music do not attempt to account for disagreements among listeners. But understanding these disagreements, sometimes regarded as noise in psychological studies, may be essential to fully understanding how listeners perceive grouping structure. Over the course of four studies in different disciplines, this thesis develops and presents evidence to support the hypothesis that attention is a key factor in accounting for listeners' perceptions of boundaries and groupings, and hence a key to explaining their disagreements. First, we conduct a case study of the disagreements between two listeners. By studying the justi cations each listener gave for their analyses, we argue that the disagreements arose directly from differences in attention, and indirectly from differences in information, expectation, and ontological commitments made in the opening moments. Second, in a large-scale corpus study, we study the extent to which acoustic novelty can account for the boundary perceptions of listeners. The results indicate that novelty is correlated with boundary salience, but that novelty is a necessary but not su cient condition for being perceived as a boundary. Third, we develop an algorithm that optimally reconstructs a listener's analysis in terms of the patterns of similarity within a piece of music. We demonstrate how the output can identify good justifications for an analysis and account for disagreements between two analyses. Finally, having introduced and developed the hypothesis that disagreements between listeners may be attributable to differences in attention, we test the hypothesis in a sequence of experiments. We find that by manipulating the attention of participants, we are able to influence the groupings and boundaries they find most salient. From the sum of this research, we conclude that a listener's attention is a crucial factor affecting how listeners perceive the grouping structure of music.Social Sciences and Humanities Research Council; a PhD studentship from Queen Mary University of London; a Provost's Ph.D. Fellowship from the University of Southern California. This material is also based in part on work supported by the National Science Foundation under Grant No. 0347988

Analyse de structures répétitives dans les séquences musicales

Cette thèse rend compte de travaux portant sur l inférence de structures répétitives à partir du signal audio à l aide d algorithmes du texte. Son objectif principal est de proposer et d évaluer des algorithmes d inférence à partir d une étude formelle des notions de similarité et de répétition musicale.Nous présentons d abord une méthode permettant d obtenir une représentation séquentielle à partir du signal audio. Nous introduisons des outils d alignement permettant d estimer la similarité entre de telles séquences musicales, et évaluons l application de ces outils pour l identi cation automatique de reprises. Nous adaptons alors une technique d indexation de séquences biologiques permettant une estimation e cace de la similarité musicale au sein de bases de données conséquentes.Nous introduisons ensuite plusieurs répétitions musicales caractéristiques et employons les outils d alignement pour identi er ces répétitions. Une première structure, la répétition d un segment choisi, est analysée et évaluée dans le cadre dela reconstruction de données manquantes. Une deuxième structure, la répétition majeure, est dé nie, analysée et évaluée par rapport à un ensemble d annotations d experts, puis en tant qu alternative d indexation pour l identi cation de reprises.Nous présentons en n la problématique d inférence de structures répétitives telle qu elle est traitée dans la littérature, et proposons notre propre formalisation du problème. Nous exposons alors notre modélisation et proposons un algorithme permettant d identi er une hiérarchie de répétitions. Nous montrons la pertinence de notre méthode à travers plusieurs exemples et en l évaluant par rapport à l état de l art.The work presented in this thesis deals with repetitive structure inference from audio signal using string matching techniques. It aims at proposing and evaluating inference algorithms from a formal study of notions of similarity and repetition in music.We rst present a method for representing audio signals by symbolic strings. We introduce alignment tools enabling similarity estimation between such musical strings, and evaluate the application of these tools for automatic cover song identi cation. We further adapt a bioinformatics indexing technique to allow e cient assessments of music similarity in large-scale datasets. We then introduce several speci c repetitive structures and use alignment tools to analyse these repetitions. A rst structure, namely the repetition of a chosen segment, is retrieved and evaluated in the context of automatic assignment of missingaudio data. A second structure, namely the major repetition, is de ned, retrieved and evaluated regarding expert annotations, and as an alternative indexing method for cover song identi cation.We nally present the problem of repetitive structure inference as addressed in literature, and propose our own problem statement. We further describe our model and propose an algorithm enabling the identi cation of a hierarchical music structure. We emphasize the relevance of our method through several examples and by comparing it to the state of the art.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF