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Master thesis

Clustering incrémental de signaux audio

Maxime Sirbu 1, 2, 3
1 MuTant - Synchronous Realtime Processing and Programming of Music Signals
Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, IRCAM - Institut de Recherche et Coordination Acoustique/Musique, CNRS - Centre National de la Recherche Scientifique
2 Repmus - Représentations musicales
STMS - Sciences et Technologies de la Musique et du Son
Abstract : This report aims to study different methods of online clustering, mainly applied to audio signals. We will first detail the state-of-the art algorithms for clustering, as well as the theory behind them. Then we will extend this methods to incremental clustering, and present different online algorithms. These algorithms are based on the hidden markov models, which are classic art representations of data and hidden states in signal processing, and hidden semi-markov models, which extend them to a semi-markov representation of the states. We present this within the context of audio segmentation – the task of segmenting audio sources in homogenous chunks – and classification – the task of identifying these chunks – applied to audio event detection. We will also set an experimental protocol, with a view to evaluate them and compare the result to a state-of-the art algorithm for the same task.
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Submitted on : Wednesday, September 9, 2015 - 6:09:41 PM
Last modification on : Wednesday, April 27, 2022 - 3:49:23 AM
Long-term archiving on: : Monday, December 28, 2015 - 11:24:43 PM


  • HAL Id : hal-01196455, version 1


Maxime Sirbu. Clustering incrémental de signaux audio. Apprentissage [cs.LG]. 2015. ⟨hal-01196455⟩



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