Work

Classifying soundtracks with audio texture features

Year: 2011

Type: conference-paper

Abstract: Sound textures may be defined as sounds whose character depends on statistical properties as much as the specific details of each individually-perceived event. Recent work has devised a set of statistics that, when synthetically imposed, allow listeners to identify a wide range of environmental sound textures. In this work, we investigate using these statistics for automatic classification of a set of environmental sound classes defined over a set of web videos depicting "multimedia events". We show that the texture statistics perform as well as our best conventional statistics (based on MFCC covariance). We further examine the relative contributions of the different statistics, showing the importance of modulation spectra and cross-band envelope correlations.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp.2011.5947699

Authors: Daniel P. W. Ellis, Xiaohong Zeng, Josh H. McDermott

Institutions: Columbia University, New York University

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Geospatial-Intelligence Agency

OpenAlex ID: W2128031217

FWCI: 4.857

Cites: 10

Cited by: 49

Related to: 10