Classification of audio scenes using Narrow-Band Autocorrelation features

Xavier Valero, Francesc Aliás

Research output: Book chapterConference contributionpeer-review

7 Citations (Scopus)


Multiple single sound events of very different characteristics might coincide in a given space and time, thus composing complex audio scenes. In that context, defining signal features capable of effectively analyzing the holistic audio scenes is a challenging task. This paper introduces a set of features that consider the temporal, spectral and perceptual characteristics of the audio scene signals. Specifically, the features are obtained from the autocorrelation function of band-pass signals computed after applying a Mel filter bank. The so-called Narrow-Band Autocorrelation (NB-ACF) features are compared to state-of-the-art signal features on a corpus of 4 hours composed of 15 audio scenes. Regardless of the learning algorithm employed, the NB-ACF attains the highest averaged recognition rates: 2.3 % higher than Mel Frequency Cepstral Coefficients and 5.6 % higher than Discrete Wavelet Coefficients.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Number of pages5
Publication statusPublished - 2012
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference20th European Signal Processing Conference, EUSIPCO 2012


  • Audio classification
  • autocorrelation function
  • environmental sound recognition
  • feature extraction
  • narrow-band signal analysis


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