References of "Osmalsky, Julien"
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See detailCover Songs Retrieval and Identification
Osmalsky, Julien ULg

Conference (2015, February 24)

Cover songs retrieval is a MIR task that has been widely studied in the recent years. The task, in its general sense, consists in retrieving covers for a given audio query. In this PhD, we focus on the ... [more ▼]

Cover songs retrieval is a MIR task that has been widely studied in the recent years. The task, in its general sense, consists in retrieving covers for a given audio query. In this PhD, we focus on the more specific task of cover songs *identification*. Given an unknown cover song query, we want to find relevant information about the original song, such as the title, the artist etc. Applications are live music recognition, plagiarism detections, query by examples, etc. Our approach is based on a pruning algorithm, whose role is to discard as quickly as possible as many tracks as possible from a search database in order to keep a smaller subset that should contain the tracks relevant to the query. We introduce the concept of *rejectors* for the pruning algorithm, which are single or combined classifier using multiple audio features for selecting relevant tracks. [less ▲]

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See detailPerformances of low-level audio classifiers for large-scale music similarity
Osmalsky, Julien ULg; Van Droogenbroeck, Marc ULg; Embrechts, Jean-Jacques ULg

in International Conference on Systems, Signals and Image Processing (2014, May)

This paper proposes a survey of the performances of binary classifiers based on low-level audio features, for music similarity in large-scale databases. Various low-level descriptors are used individually ... [more ▼]

This paper proposes a survey of the performances of binary classifiers based on low-level audio features, for music similarity in large-scale databases. Various low-level descriptors are used individually and then combined using several fusion schemes in a content-based audio retrieval system. We show the performances of the classifiers in terms of pruning and loss and we demonstrate that some combination schemes achieve a better performance at a minimum computational cost. [less ▲]

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See detailEfficient database pruning for large-scale cover song recognition
Osmalsky, Julien ULg; Pierard, Sébastien ULg; Van Droogenbroeck, Marc ULg et al

in International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2013, May)

This paper focuses on cover song recognition over a large dataset, potentially containing millions of songs. At this time, the problem of cover song recognition is still challenging and only few methods ... [more ▼]

This paper focuses on cover song recognition over a large dataset, potentially containing millions of songs. At this time, the problem of cover song recognition is still challenging and only few methods have been proposed on large scale databases. We present an efficient method for quickly extracting a small subset from a large database in which a correspondence to an audio query should be found. We make use of fast rejectors based on independent audio features. Our method mixes independent rejectors together to build composite ones. We evaluate our system with the Million Song Dataset and we present composite rejectors offering a good trade-off between the percentage of pruning and the percentage of loss. [less ▲]

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See detailNeural networks for musical chords recognition
Osmalsky, Julien ULg; Embrechts, Jean-Jacques ULg; Van Droogenbroeck, Marc ULg et al

in Journées d'informatique musicale (2012, May)

In this paper, we consider the challenging problem of music recognition and present an effective machine learning based method using a feed-forward neural network for chord recognition. The method uses ... [more ▼]

In this paper, we consider the challenging problem of music recognition and present an effective machine learning based method using a feed-forward neural network for chord recognition. The method uses the known feature vector for automatic chord recognition called the Pitch Class Profile (PCP). Although the PCP vector only provides attributes corresponding to 12 semi-tone values, we show that it is adequate for chord recognition. Part of our work also relates to the design of a database of chords. Our database is primarily designed for chords typical of Western Europe music. In particular, we have built a large dataset filled with recorded guitar chords under different acquisition conditions (instruments, microphones, etc), but also with samples obtained with other instruments. Our experiments establish a twofold result: (1) the PCP is well suited for describing chords in a machine learning context, and (2) the algorithm is also capable to recognize chords played with other instruments, even unknown from the training phase. [less ▲]

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