Abstract
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.
Original language | English |
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Article number | 7206602 |
Pages (from-to) | 2605-2615 |
Number of pages | 11 |
Journal | IEEE Transactions on Information Forensics and Security |
Volume | 10 |
Issue number | 12 |
DOIs | |
Publication status | Published - 1 Dec 2015 |
Externally published | Yes |
Keywords
- Audio surveillance
- cochleagram
- gammatone filter
- gray-level co-occurrence matrix
- spectrogram
- support vector machines