Noise-Robust environmental sound classification method based on combination of ICA and MP features

Reona Mogi, Hiroyuki Kasai


This paper presents an environmental sound classification method that is noise-robust against sounds recorded by mobile devices, and presents evaluation of its performance. This method is specifically designed to recognize higher semantics of context from environmental sound. Conventionally, sound classifications have used acoustic features in the frequency domain extracted from sound data using signal processing techniques. Although the most popular feature is Mel-frequency Cepstral Coefficients (MFCC), MFCC is inappropriate for mixture sound with noise. Independent Component Analysis (ICA) can extract sound characteristics even when the source is corrupted by noise because components within the source are assumed to be independent. In recent years, Matching Pursuit (MP) has been addressed to extract time-domain features. It has been applied to various applications. The feature is effective for recognizing and classifying environmental sounds that include time-variant sound such as birdsongs, alarms, and vehicle sounds. In this way, some innovative techniques have been proposed to recognize and classify environmental sounds recorded on mobile devices. However, we have not yet obtained a decisive method to attain a higher recognition and classification rate against environmental sounds with various noises such as unintended sounds and white noise. To address this problem, we propose a noise-robust classification method using a combination of Independent Component Analysis (ICA) and MP. It is possible to reduce noise effects for feature extraction. From performance evaluations, we confirmed that the proposed method can provide about 8% better classification than that of MFCC feature extraction.

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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