Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients

In this paper, we propose a system for environment recognition using selected MPEG-7 audio low level descriptors together with conventional mel-frequency cepstral coefficients (MFCC). The MPEG-7 descriptors are first ranked based on Fisher’s discriminant ratio. Then principal component analysis is applied on top ranked 30 MPEG-7 descriptors to obtain 13 features. These 13 features are appended with MFCC features to complete the feature set of the proposed system. Gaussian mixture models (GMMs) are used as classifier. The system is evaluated using ten different environment sounds. The experimental results show a significant improvement in recognition performance of the proposed system over MFCC or full MPEG-7 descriptor based systems. For example, the best performance is achieved in Restaurant environment where MFCC, full MPEG-7, and the proposed method give 90%, 94%, and 96% accuracy, respectively.

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