A Semantics-Driven Approach to Lyrics Segmentation

Abstract-The purpose of this paper is describing a semanticsdriven approach to the automatic segmentation of song lyrics. The proposed algorithm takes into account the basic formatting commonly in use for lyrics on CD booklets and specialized Web sites, in order to extract basic semantic information, such as the organization in lines and sections. Then the algorithm applies simple rules to reconstruct lyrics structure, supporting tolerance margins as regards possible errors and encoding variants. The output is a sequence of sections labelled according to the similarity of their contents. The resulting segmenter is publicly available as a set of methods exposed via a Web application programming interface (API)

Probabilistic Segmentation of Folk Music Recordings

The paper presents a novel method for automatic segmentation of folk music field recordings. The method is based on a distance measure that uses dynamic time warping to cope with tempo variations and a dynamic programming approach to handle pitch drifting for finding similarities and estimating the length of repeating segment. A probabilistic framework based on HMM is used to find segment boundaries, searching for optimal match between the expected segment length, between-segment similarities, and likely locations of segment beginnings. Evaluation of several current state-of-the-art approaches for segmentation of commercial music is presented and their weaknesses when dealing with folk music are exposed, such as intolerance to pitch drift and variable tempo. The proposed method is evaluated and its performance analyzed on a collection of 206 folk songs of different ensemble types: solo, two- and three-voiced, choir, instrumental, and instrumental with singing. It outperforms current commercial music segmentation methods for noninstrumental music and is on a par with the best for instrumental recordings. The method is also comparable to a more specialized method for segmentation of solo singing folk music recordings

Musculoskeletal Load Exposure Estimation by Non-supervised Annotation of Events on Motion Data

There is a significant number of work pressures that promote the incidence of musculoskeletal disorders in industrial environments. As, unfortunately, many workplace conditions are subject to these biomechanical hazards, this has become an extensively common health disorder. To properly adjust intervention strategies, an ergonomic assessment through surveillance measurements is required. However, most measurements still depend on subjective assessment tools like self-reporting and expert observation. The ideal approach for this scenario would be to use direct measurements that use sensors to retrieve more precise/accurate information of how workers interact with their work environment. Following this approach, one of the major constraints would be that a systematic retrieval of data from a labor environment would require a tiresome process of analysis and manual annotation, deviating resources and requiring data analysts. Hence, this work proposes an unsupervised methodology able to automatically annotate relevant events from direct acquisitions, with the final intent of promoting this type of analysis. The event detection methodology proposes to detect three different event types: 1) work period transition; 2) work cycle transition; and 3) sub-sequence matching by query. To achieve this, the multivariate time series are represented as a Self-Similarity matrix built with the features extracted. This matrix is analysed for each event needed to be searched. The results were successful in the segmentation of Active and Non-active working periods and in the detection of points of transition between repetitive human motions, i.e. work cycles. A method of search-by-example is also presented, being that it allows for the user to detect specific motions of interest. Although this method could still be further optimized in future work, this approach has a very promising prospect as it proposes a strategy of similarity analysis that has not yet been deeply explored in the context of ergonomic acquisition. These advances are also significant given that the summarization of ergonomic data is still a subject in expansion.Num contexto industrial, são várias as tensões que promovem a incidência de distúrbios musculosqueléticos. Uma vez que a maioria das condições laborais estão sujeitas a estas propensões do foro biomecânico, os distúrbiosmusculosqueléticos tornaram-se patologias amplamente diagnosticadas na população ativa. Para desenhar estratégias de intervenção eficientes, é necessário proceder a uma avaliação ergonómica baseada em metododologias de vigilância. Não obstante o reconhecimento desta necessidade, a maioria das medidas ainda depende de ferramentas subjetivas como a auto-avaliação e a observação externa por parte de especialistas. A abordagem preferencial para esta problemática passaria pela aplicação de medições diretas que recorressem a sensores com vista a extrair informação exata e fidedigna do ambiente laboral. Uma das maiores limitações deste leque de soluções consiste no facto de um sistema de recolha de dados neste ambiente implicar um processo exaustivo de análise e anotação manual, o que consome recursos e requer os serviços de analistas de dados. Assim, este trabalho propõe uma metodologia capaz de anotar automaticamente eventos relevantes provenientes de aquisições diretas, com o objetivo final de promover este tipo de análises mais eficientes. A metodologia de deteção de eventos proposta foca-se em três diferentes tipos de eventos: 1) transições entre tarefas; 2) transições entre ciclos de trabalho; e 3) procura de movimentos-exemplo em amostras segmentadas. Para concretizar este trabalho, realizou-se um estudo de matrizes de auto-semelhança. Os resultados provaram-se, na sua maioria, bem-sucedidos no caso da segmentação de períodos Ativos e Não-ativos e na deteção de momentos de transição entre movimentos repetitivos, isto é, ciclos de trabalho. É ainda apresentado um método de procura-porexemplo que permite ao utilizador detetar movimentos-exemplo do seu interesse. Embora este método possa ainda ser otimizado em trabalhos futuros, reflete uma abordagem promissora uma vez que propõe uma estratégia de análise de similaridade que não foi ainda especialmente explorada no contexto dos estudos ergonómicos. Estes avanços são ainda significantes na perspetiva de que a sumarização de dados ergonómicos é uma linha de investigação ainda em expansão

Estimation de la structure de morceaux de musique par analyse multi-critères et contrainte de régularité

Les récentes évolutions des technologies de l'information et de la communication font qu'il est aujourd'hui facile de consulter des catalogues de morceaux de musique conséquents. De nouvelles représentations et de nouveaux algorithmes doivent de ce fait être développés afin de disposer d'une vision représentative de ces catalogues et de naviguer avec agilité dans leurs contenus. Ceci nécessite une caractérisation efficace des morceaux de musique par l'intermédiaire de descriptions macroscopiques pertinentes. Dans cette thèse, nous nous focalisons sur l'estimation de la structure des morceaux de musique : il s'agit de produire pour chaque morceau une description de son organisation par une séquence de quelques dizaines de segments structurels, définis par leurs frontières (un instant de début et un instant de fin) et par une étiquette représentant leur contenu sonore.La notion de structure musicale peut correspondre à de multiples acceptions selon les propriétés musicales choisies et l'échelle temporelle considérée. Nous introduisons le concept de structure sémiotique" qui permet de définir une méthodologie d'annotation couvrant un vaste ensemble de styles musicaux. La détermination des segments structurels est fondée sur l'analyse des similarités entre segments au sein du morceau, sur la cohérence de leur organisation interne (modèle système-contraste") et sur les relations contextuelles qu'ils entretiennent les uns avec les autres. Un corpus de 383 morceaux a été annoté selon cette méthodologie et mis à disposition de la communauté scientifique.En termes de contributions algorithmiques, cette thèse se concentre en premier lieu sur l'estimation des frontières structurelles, en formulant le processus de segmentation comme l'optimisation d'un coût composé de deux termes~: le premier correspond à la caractérisation des segments structurels par des critères audio et le second reflète la régularité de la structure obtenue en référence à une pulsation structurelle". Dans le cadre de cette formulation, nous comparons plusieurs contraintes de régularité et nous étudions la combinaison de critères audio par fusion. L'estimation des étiquettes structurelles est pour sa part abordée sous l'angle d'un processus de sélection d'automates à états finis : nous proposons un critère auto-adaptatif de sélection de modèles probabilistes que nous appliquons à une description du contenu tonal. Nous présentons également une méthode d'étiquetage des segments dérivée du modèle système-contraste.Nous évaluons différents systèmes d'estimation automatique de structure musicale basés sur ces approches dans le cadre de campagnes d'évaluation nationales et internationales (Quaero, MIREX), et nous complétons cette étude par quelques éléments de diagnostic additionnels.Recent progress in information and communication technologies makes it easier to access large collections of digitized music. New representations and algorithms must be developed in order to get a representative overview of these collections, and to browse their content efficiently. It is therefore necessary to characterize music pieces through relevant macroscopic descriptions. In this thesis, we focus on the estimation of the structure of music pieces : the goal is to produce for each piece a description of its organization by means of a sequence of a few dozen structural segments, each of them defined by its boundaries (starting time and ending time) and a label reflecting its audio content.The notion of music structure corresponds to a wide range of meanings depending on the musical properties and the temporal scale under consideration. We introduce an annotation methodology based on the concept of semiotic structure" which covers a large variety of musical styles. Structural segments are determined through the analysis of their similarities within the music piece, the coherence of their inner organization ( system-contrast" model) and their contextual relationship. A corpus of 383 pieces has been annotated according to this methodology and released to the scientific community.In terms of algorithmic contributions, this thesis concentrates in the first place on the estimation of structural boundaries. We formulate the segmentation process as the optimization of a cost function which is composed of two terms. The first one corresponds to the characterization of structural segments by means of audio criteria. The second one relies on the regularity of the target structure with respect to a structural pulsation period". In this context, we compare several regularity constraints and study the combination of audio criteria through fusion.Secondly, we consider the estimation of structural labels as a probabilistic finite-state automaton selection process : in this scope, we propose an auto-adaptive criterion for model selection, applied to a description of the tonal content. We also propose a labeling method derived from the system-contrast model.We evaluate several systems for structural segmentation of music based on these approaches in the context of national and international evaluation campaigns (Quaero, MIREX). Additional diagnostic is finally presented to complement this work.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF

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