Music structure estimation has recently emerged as a central topic within the field of Music Information Retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This article focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically here, we study how a constraint which favors structural segments of comparable size provides a better conditioning of the boundary estimation process. Firstly, we propose a formulation of the structural segmentation task as an optimization process which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011 and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the "MIREX10" dataset (100 J-Pop songs from the RWC database).

This article introduces a model called " System & Contrast" (S&C), which aims at describing the inner organization of structural segments within music pieces in terms of : (i) a carrier system, i.e. a sequence of morphological elements forming a multi-dimensional network of self-deducible syntagmatic relationships and (ii) a contrast, i.e. a substitutive element, usually the last one, which partly departs from the logic implied by the rest of the system. With a primary focus on pop music, the S&C model provides a framework to describe internal implication patterns in musical segments by encoding similarities and relations between its constitutive elements so as to minimize the complexity of the resulting description. It is applicable at several timescales and to a wide variety of musical dimensions in a polymorphous way, therefore offering an attractive meta-description of different types of musical contents. It has been used as a central component in the creation of a set of annotations for 380 pop songs (Bimbot, Sargent, Deruty, Guichaoua & Vincent, 2014).This article formalizes the S&C model, illustrates how it applies to music and establishes its filiation with Narmour's Implication-Realization model (Narmour 1990(Narmour , 1992

The indexing of broadcast TV archives is a current problem in multimedia research. As the size of these databases grows continuously, meaningful features are needed to describe and connect their elements efficiently, such as the identification of speaking faces. In this context, this paper focuses on two approaches for unsupervised person discovery. Initial tagging of speaking faces is provided by an OCR-based method, and these tags propagate through a graph model based on audiovisual relations between speaking faces. Two propagation methods are proposed, one based on random walks and the other based on a hierarchical approach. To better evaluate their performances, these methods were compared with two graph clustering baselines. We also study the impact of different modality fusions on the graph-based tag propagation scenario. From a quantitative analysis, we observed that the graph propagation techniques always outperform the baselines. Among all compared strategies, the methods based on hierarchical propagation with late fusion and random walk with score-fusion obtained the highest MAP values. Finally, even though these two methods produce highly equivalent results according to Kappa coefficient, the random walk method performs better according to a paired t-test, and the computing time for the hierarchical propagation is more than 4 times lower than the one for the random walk propagation.

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowledge requires accurate association of audio-visual cues and detected names. To this end, we present 3 different strategies to approach this problem: clustering-based naming, verification-based naming, and graph-based naming. Each of these strategies utilizes different recent advances in unsupervised face / speech representation, verification, and optimization. To have a better understanding of the approaches, this paper also provides a quantitative and qualitative comparative study of these approaches using the associated corpus of the Person Discovery challenge at MediaEval 2016. From the results of our experiments, we can observe the pros and cons of each approach, thus paving the way for future promising research directions.

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