Environmental Sounds Spectrogram Classification using Log-Gabor Filters and Multiclass Support Vector Machines

TL;DR

This paper introduces novel feature extraction techniques for environmental sound spectrogram classification using log-Gabor filters and multiclass SVMs, demonstrating improved efficiency over existing methods.

Contribution

It proposes three new methods for spectrogram feature extraction with log-Gabor filters, identifying the most effective approach for environmental sound classification.

Findings

Second method outperforms others in classification accuracy

Spectrogram segmentation enhances feature extraction

Log-Gabor filter bank improves feature robustness

Abstract

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.