Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines
This work addresses environmental sound classification, which is an incremental improvement for audio processing applications.
The paper tackled environmental sound classification by proposing three feature extraction methods using log-Gabor filters on spectrograms, finding that the second method with an averaged bank of 12 filters was most efficient, achieving improved classification results.
This paper presents novel approaches for efficient feature extraction using environmental sound magnitude spectrogram. We propose approach based on the visual domain. This approach included three methods. The first method is based on extraction for each spectrogram a single log-Gabor filter followed by mutual information procedure. In the second method, the spectrogram is passed by the same steps of the first method but with an averaged bank of 12 log-Gabor filter. The third method consists of spectrogram segmentation into three patches, and after that for each spectrogram patch we applied the second method. The classification results prove that the second method is the most efficient in our environmental sound classification system.