Gabor-Based Nonuniform Scale-Frequency Map for Environmental Sound Classification in Home Automation

Abstract

This work presents a novel feature extraction approach called nonuniform scale-frequency map for environmental sound classification in home automation. For each audio frame, important atoms from the Gabor dictionary are selected by using the Matching Pursuit algorithm. After the system disregards phase and position information, the scale and frequency of the atoms are extracted to construct a scale-frequency map. Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are then applied to the scale-frequency map, subsequently generating the proposed feature. During the classification phase, a segment-level multiclass Support Vector Machine (SVM) is operated. Experiments on a 17-class sound database indicate that the proposed approach can achieve an accuracy rate of 86.21%. Furthermore, a comparison reveals that the proposed approach is superior to the other time-frequency methods.