Gammatone cepstral coefficients: Biologically inspired features for non-speech audio classification

Xavier Valero, Francesc Alias

Producción científica: Artículo en revista indizadaArtículorevisión exhaustiva

204 Citas (Scopus)


In the context of non-speech audio recognition and classification for multimedia applications, it becomes essential to have a set of features able to accurately represent and discriminate among audio signals. Mel frequency cepstral coefficients (MFCC) have become a de facto standard for audio parameterization. Taking as a basis the MFCC computation scheme, the Gammatone cepstral coefficients (GTCCs) are a biologically inspired modification employing Gammatone filters with equivalent rectangular bandwidth bands. In this letter, the GTCCs, which have been previously employed in the field of speech research, are adapted for non-speech audio classification purposes. Their performance is evaluated on two audio corpora of 4 h each (general sounds and audio scenes), following two cross-validation schemes and four machine learning methods. According to the results, classification accuracies are significantly higher when employing GTCC rather than other state-of-the-art audio features. As a detailed analysis shows, with a similar computational cost, the GTCC are more effective than MFCC in representing the spectral characteristics of non-speech audio signals, especially at low frequencies.

Idioma originalInglés
Número de artículo6202347
Páginas (desde-hasta)1684-1689
Número de páginas6
PublicaciónIEEE Transactions on Multimedia
EstadoPublicada - 2012


Profundice en los temas de investigación de 'Gammatone cepstral coefficients: Biologically inspired features for non-speech audio classification'. En conjunto forman una huella única.

Citar esto