Real-time Audio Classification based on Mixture Models

Maxime Baelde; Christophe Biernacki; Raphaël Greff

Poster Communications Year : 2017

Real-time Audio Classification based on Mixture Models

(1, 2, 3) , (2, 3) , (1)

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Maxime Baelde

Function : Author

A-Volute [Roubaix]

MOdel for Data Analysis and Learning

Laboratoire Paul Painlevé - UMR 8524

Christophe Biernacki

Function : Author

MOdel for Data Analysis and Learning

Laboratoire Paul Painlevé - UMR 8524

Raphaël Greff

Function : Author

A-Volute [Roubaix]

Abstract

This is the poster related to the conference paper "A mixture model-based real-time audio sources classification method", Baelde et al, ICASSP 2017 (hal-01420677v2).

Keywords

Learning Mixture model Real-time Audio identification Statistical Sound classification Machine learning

Domains

Machine Learning [stat.ML] Methodology [stat.ME] Statistics [stat]

Fichier principal

Poster ICASSP 2017 BAELDE.pdf (254.45 Ko)

Origin	Files produced by the author(s)

Maxime Baelde : Connect in order to contact the contributor

https://hal.science/hal-01481934

Submitted on : Friday, March 3, 2017-9:42:06 AM

Last modification on : Tuesday, June 4, 2024-12:18:04 PM

Long-term archiving on : Tuesday, June 6, 2017-12:10:09 PM

Dates and versions

hal-01481934 , version 1 (03-03-2017)

Identifiers

HAL Id : hal-01481934 , version 1

Cite

Maxime Baelde, Christophe Biernacki, Raphaël Greff. Real-time Audio Classification based on Mixture Models. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017), Mar 2017, La Nouvelle-Orléans, United States. . ⟨hal-01481934⟩

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Real-time Audio Classification based on Mixture Models

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