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Saliency-based modeling of acoustic scenes using sparse non-negative matrix factorization

Benjamin Cauchi 1, * Mathieu Lagrange 2 Nicolas Misdariis 3 Arshia Cont 3, 4
* Corresponding author
4 MuTant - Synchronous Realtime Processing and Programming of Music Signals
IRCAM - Institut de Recherche et Coordination Acoustique/Musique, Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, CNRS - Centre National de la Recherche Scientifique
Abstract : The modelling of auditory scenes is a challenging task in Computational Auditory Scene Analysis. A method based on sparse Non-negative Matrix Factorization that can be used with no prior knowledge of the audio content to establish the similarity between scenes is proposed. The method is evaluated on a corpus of soundscapes of train stations issued from a perceptual study and results are compared with the human perception. The proposed method, by being able to focus on salient events within the scene, achieves better performances than a state-of-the-art Bag-of-Frames approach though not reaching the human performances.
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https://hal.inria.fr/hal-00940075
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Last modification on : Tuesday, October 19, 2021 - 12:49:44 PM
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Benjamin Cauchi, Mathieu Lagrange, Nicolas Misdariis, Arshia Cont. Saliency-based modeling of acoustic scenes using sparse non-negative matrix factorization. Workshop on Image and Audio Analysis for Multimedia Interactive, Jul 2013, Paris, France. ⟨hal-00940075⟩

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