A mixture model-based real-time audio sources classification method

Maxime Baelde (1, 2, 3) , Christophe Biernacki (1, 2) , Raphaël Greff (3)

1 LPP - Laboratoire Paul Painlevé - UMR 8524
2 MODAL - MOdel for Data Analysis and Learning
3 A-Volute [Roubaix]

Recent research on machine learning focuses on audio source identification in complex environments. They rely on extracting features from audio signals and use machine learning techniques to model the sound classes. However, such techniques are often not optimized for a real-time implementation and in multi-source conditions. We propose a new real-time audio single-source classification method based on a dictionary of sound models (that can be extended to a multi-source setting). The sound spectrums are modeled with mixture models and form a dictionary. The classification is based on a comparison with all the elements of the dictionary by computing likelihoods and the best match is used as a result. We found that this technique outperforms classic methods within a temporal horizon of 0.5s per decision (achieved 6% of errors on a database composed of 50 classes). Future works will focus on the multi-sources classification and reduce the computational load.

Mots clés: real-time; audio identification; statistical; learning; mixture models; sound classification; machine learn-

Domaines: Méthodologie [stat.ME]

Maxime Baelde, Christophe Biernacki, Raphaël Greff. A mixture model-based real-time audio sources classification method. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP2017, Mar 2017, New Orleans, United States. ⟨hal-01420677v2⟩