A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

TL;DR

This paper introduces a novel, compact feature based on auditory summary statistics inspired by human perception, significantly improving acoustic scene classification by capturing environmental sound textures efficiently.

Contribution

It proposes using auditory summary statistics combined with linear discriminant analysis to create a highly discriminative and compact feature for ASC, outperforming traditional handcrafted features.

Findings

Proposed feature achieves superior classification accuracy.

Auditory summary statistics effectively characterize environmental sounds.

Linear discriminant analysis reduces redundancy while maintaining discriminative power.

Abstract

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.