Real-Time Monophonic and Polyphonic Audio ...
Document type :
Article dans une revue scientifique
Permalink :
Title :
Real-Time Monophonic and Polyphonic Audio Classification from Power Spectra
Author(s) :
Baelde, Maxime [Auteur]
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé [LPP]
Biernacki, Christophe [Auteur]
Greff, Raphaël [Auteur]
Laboratoire Paul Painlevé - UMR 8524
Laboratoire Paul Painlevé [LPP]
Biernacki, Christophe [Auteur]

Greff, Raphaël [Auteur]
Journal title :
Pattern Recognition
Volume number :
92
Pages :
82-92
Publisher :
Elsevier
Publication date :
2019-08-01
ISSN :
0031-3203
Keyword(s) :
Real-time
Audio classification
Generative model
Polyphonic
Nonparametric estimation
Monophonic
Machine learning
Audio classification
Generative model
Polyphonic
Nonparametric estimation
Monophonic
Machine learning
HAL domain(s) :
Statistiques [stat]/Machine Learning [stat.ML]
English abstract : [en]
This work addresses the recurring challenge of real-time monophonic and polyphonic audio source classification. The whole normalized power spectrum (NPS) is directly involved in the proposed process, avoiding complex and ...
Show more >This work addresses the recurring challenge of real-time monophonic and polyphonic audio source classification. The whole normalized power spectrum (NPS) is directly involved in the proposed process, avoiding complex and hazardous traditional feature extraction. It is also a natural candidate for polyphonic events thanks to its additive property in such cases. The classification task is performed through a nonparametric kernel-based generative modeling of the power spectrum. Advantage of this model is twofold: it is almost hypothesis free and it allows to straightforwardly obtain the maximum a posteriori classification rule of online signals. Moreover it makes use of the monophonic dataset to build the polyphonic one. Then, to reach the real-time target, the complexity of the method can be tuned by using a standard hierarchical clustering preprocessing of the prototypes, revealing a particularly efficient computation time and classification accuracy trade-off. The proposed method, called RARE (for Real-time Audio Recognition Engine) reveals encouraging results both in monophonic and polyphonic classification tasks on benchmark and owned datasets, including also the targeted real-time situation. In particular, this method benefits from several advantages compared to the state-of-the-art methods including a reduced training time, no feature extraction, the ability to control the computation - accuracy trade-off and no training on already mixed sounds for polyphonic classification.Show less >
Show more >This work addresses the recurring challenge of real-time monophonic and polyphonic audio source classification. The whole normalized power spectrum (NPS) is directly involved in the proposed process, avoiding complex and hazardous traditional feature extraction. It is also a natural candidate for polyphonic events thanks to its additive property in such cases. The classification task is performed through a nonparametric kernel-based generative modeling of the power spectrum. Advantage of this model is twofold: it is almost hypothesis free and it allows to straightforwardly obtain the maximum a posteriori classification rule of online signals. Moreover it makes use of the monophonic dataset to build the polyphonic one. Then, to reach the real-time target, the complexity of the method can be tuned by using a standard hierarchical clustering preprocessing of the prototypes, revealing a particularly efficient computation time and classification accuracy trade-off. The proposed method, called RARE (for Real-time Audio Recognition Engine) reveals encouraging results both in monophonic and polyphonic classification tasks on benchmark and owned datasets, including also the targeted real-time situation. In particular, this method benefits from several advantages compared to the state-of-the-art methods including a reduced training time, no feature extraction, the ability to control the computation - accuracy trade-off and no training on already mixed sounds for polyphonic classification.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:10:25Z
2020-06-09T08:54:08Z
2020-06-09T08:54:08Z
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