Environmental sound classification using log-Gabor filter

Abstract

This paper presents novel approaches for efficient feature extraction using environmental sound magnitude spectrogram. We propose approaches based on the visual domain, the spectrogram is passed through a bank of 12 logGabor filters, followed by an averaged operation and passed through an optimal feature selection procedure based on mutual information. The proposed methods were tested on a database of 10 sound classes. The evaluation system is realized by using the multiclass support vector machines (SVM's) that gave rise to a recognition rate of the order 89.62 %.

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

“…A common recent strategy in the definition of computational models of perception is to use Gabor filters as low-level visual primitives. Inspired by visual perception, some recent methods in acoustic processing use Gabor-filter banks on the sound spectra as a preprocessing step, achieving accurate results on acoustic processing tasks, such as the classification of environmental sounds [ 41 ], music genre recognition [ 42 ], and speech analysis [ 43 , 44 ], to cite a few.…”

Environmental Sound Recognition Using Spectrogram Image Features

“…Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR), Spectral Centroid are some of the most widely used features for audio analysis. Many previous efforts have also been made in classifying audio signals using other features such as MPEG-7 descriptors [1,2] , Linear Prediction coefficients [3] , features derived from statistics of spectrogram image of an audio [4] and Log-Gabor Filters [5] . The bag of phrases approach is introduced in [6] , where a codebook is generated using Gaussian Mixture Model and then the codebook is used to obtain a new set of features for the classification.…”