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access icon free Feature selection based on MBFOA for audio signal classification under consideration of Gaussian white noise

  • Author(s): Muthumari Arumugam 1  and  Mala Kaliappan 2
    • View affiliations
  • Source: Volume 12, Issue 6, August 2018, p. 777 – 785
    DOI:  10.1049/iet-spr.2016.0607 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 24/10/2016, Accepted 02/03/2018, Revised 02/03/2018, Published 07/03/2018

Audio classification is a difficult task because of the issue of extracting and choosing the optimum audio features. To reduce the computational complication from existing methods, this study proposes a feature-selection method based on modified bacterial foraging optimisation algorithm (MBFOA) for classification of audio signals. Enhanced mel-frequency cepstral coefficient and enhanced power normalised cepstral coefficients with peak and pitch are estimated the signal feature and optimised using MBFOA with the fitness function. Using the probabilistic neural network, the audio signal is classified into music and speech signal. Then, if the signal is music, the signal is classified as cello, clarinet, flute etc. If the signal is detected as a speech, then it is again classified as male or female voice. This approach shows that it is possible to boost the classification accuracy by using different features and optimisation technique.

Inspec keywords: signal detection; speech processing; audio signal processing; feature selection; probability; optimisation; signal classification; AWGN; neural nets

Other keywords: signal detection; pitch estimation; optimisation technique; signal feature estimation; modified bacterial foraging optimisation algorithm; audio signal classification; Gaussian white noise; computational complication reduction; MBFOA; audio feature extraction; enhanced mel-frequency cepstral coefficient; feature-selection method; peak estimation; enhanced power normalised cepstral coefficient; probabilistic neural network

Subjects: Neural computing techniques; Speech processing techniques; Optimisation techniques; Other topics in statistics; Optimisation techniques; Signal detection; Other topics in statistics; Speech and audio signal processing

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