A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification
This work addresses the problem of feature representation for acoustic scene classification, which is incremental as it builds on neuroscience insights to improve existing methods.
The paper tackled the challenge of representing environmental sounds for acoustic scene classification by proposing a compact feature based on auditory summary statistics, which achieved outstanding performance over conventional handcrafted features.
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.