Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification
Wenjun Yang, Sridhar Krishnan · IEEE/ACM Transactions on Audio Speech and Language Processing · 2017
The popular frequency-domain features Mel-frequency cepstral coefficients (MFCCs) have been widely used for the task of acoustic scene classification (ASC). The MFCC feature vector describes only the power spectral envelope of a single frame, but it seems like environmental audio signal would benefit from information in the temporal dynamics. However, the classic approach of integrating them would lose this important information. Here, we adopt local binary pattern (LBP) as a tool to characterize the latent information on the temporal dynamics. The frame-level MFCC features are viewed as a 2-D image, where we use LBP to encode the evolution process. Besides, some complementary spectral features such as spectral centroid (SC), spectral bandwidth (SBW) is utilized to further improve the ASC performance. The proposed features are then fed into an ensemble classifier called D3C for recognizing environmental sounds. The results show that the proposed method was able to achieve a classification improvement of 8% compared to the baseline system. Our work presented a new method for combing the temporal features, demonstrating the significance of the temporal evolution features for characterizing the environmental sound.