Subband Time-Frequency Image Texture Features for Robust Audio Surveillance

• Published in: IEEE transactions on information forensics and security 2015-12, Vol.10 (12), p.2605-2615
• Main Authors: Sharan, Roneel V., Moir, Tom J.
• Format: Article
• Language: English
• Subjects: Audio surveillance; Cepstral analysis; cochleagram; Feature extraction; Filter banks; gammatone filter; gray-level co-occurrence matrix; Noise; Spectrogram; support vector machines; Time-frequency analysis
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2015.2469254

In this paper, we utilize time-frequency image representations of sound signals for feature extraction in an audio surveillance application. Starting with the conventional spectrogram images, we consider a new feature which is based on image texture analysis. It utilizes the gray-level co-occurrence matrix, which captures the distribution of co-occurring values at a given offset. We refer this as the spectrogram image texture feature. Texture analysis is carried out in subbands and experimented on a sound database containing ten classes with each sound class containing multiple subclasses. The proposed feature was seen to be more noise robust than two commonly used cepstral features, mel-frequency cepstral coefficients and gammatone cepstral coefficients, the spectrogram image feature (SIF), where central moments are extracted as features, and a variation of SIF with reduced feature dimension. In addition, we achieved a significant improvement in classification accuracy for the three time-frequency image features by utilizing a gammatone filter-based time-frequency image, referred as cochleagram image, for feature extraction instead of the spectrogram image. A combination of cepstral and cochleagram image features also gave improvement in the classification performance.