Using spectro-temporal features for Environmental Sounds

Abstract

The paper presents the task of recognizing environmental
sounds for audio surveillance and security applications.
A various characteristics have been proposed for audio
classification, including the popular Mel-frequency cepstral
coefficients (MFCCs) which give a description of the audio
spectral shape. However, it exist some temporal-domain
features. These last have been developed to characterize the
audio signals. Here, we make an empirical feature analysis
for environmental sounds classification and propose to use
the log-Gabor-filters algorithm to obtain effective timefrequency
characteristics.
The Log-Gabor filters-based method utilizes time-frequency
decomposition for feature extraction, resulting in a flexible
and physically interpretable set of features.
The Log-Gabor filters-based feature is adopted to
supplement the MFCC features to yield higher classification
accuracy for environmental sounds.
Extensive experiments are performed to prove the
effectiveness of these joint features for environmental sound
recognition. Besides, we provide empirical results showing
that our method is robust for audio surveillance
Applications.