Real-Time Monophonic and Polyphonic Audio Classification from Power Spectra
Résumé
This work addresses the recurring challenge of real-time monophonic and poly-phonic audio source classification. The whole power spectrum 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 sound models, revealing a particularly efficient computation time and classification accuracy trade-off. Finally, it is shown that the resulting real-time audio classification method outperforms the state-of-the-art methods in the monophonic and polyphonic cases on benchmark and owned datasets, even in real-time situation.
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