New article published in the Journal of Acoustical Society of America

A  new scientific paper, resulted from the collaboration between the Acoustics group and the Human Computer Interaction group of La Salle R&D,  has been published in the Journal of Acoustical Society of America this month.

The article, coauthored by Xavier Valero and Francesc Alías, proposes a novel signal feature extraction technique inspired by human perception for the automatic recognition of acoustic environments.

Title: “Narrow-band Autocorrelation function features for the automatic recognition of acoustic environments”

Published in: Journal of the Acoustical Society of America, vol. 134, issue 1, pp. 880-890.

Abstract: Acoustic environments are typically composed of multiple sound sources of different typologies, making them especially complex to model and parameterize. To develop an automatic acoustic environment recognition system, this work proposes a spectro-temporal signal parameterization technique inspired by human perception. The proposed parameters are derived from the analysis of the autocorrelation function of narrow-band signals (NB-ACF) obtained from an auditory gammatone filter bank. Five features related to acoustic phenomena are extracted from the NB-ACF to parameterize sound signals. Experiments are conducted on a 4 h sound database (composed of 15 different acoustic environments) employing four different machine learning techniques: K-nearest neighbor, Gaussian mixture models, neural networks and support vector machines. The averaged recognition rates increase by 4.5% when using the proposed NB-ACF features instead of the popular Mel frequency cepstral coefficients. The improvement is even greater (5.6%) when the features are tested in acoustic environments from unknown locations. These results are attributed to the better modeling of the acoustic environments thanks to the complementarity of the spectro-temporal features derived from NB-ACF analysis.

© 2013 Acoustical Society of America