6533b855fe1ef96bd12b063c

RESEARCH PRODUCT

A case study on feature sensitivity for audio event classification using support vector machines

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Automatic recognition of multiple acoustic events is an interesting problem in machine listening that generalizes the classical speech/non-speech or speech/music classification problem. Typical audio streams contain a diversity of sound events that carry important and useful information on the acoustic environment and context. Classification is usually performed by means of hidden Markov models (HMMs) or support vector machines (SVMs) considering traditional sets of features based on Mel-frequency cepstral coefficients (MFCCs) and their temporal derivatives, as well as the energy from auditory-inspired filterbanks. However, while these features are routinely used by many systems, it is not yet understood which is their relative importance in the classification task. This paper presents a preliminary study to assess the sensitivity of these features under a common SVM framework, aiming at providing deeper insight into appropriate low-level audio event representation for classification tasks.