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Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK

Author

Listed:
  • Amira Boulmaiz

    (Laboratory of Automatic and Signals of Annaba (LASA), Electronics department, Badji Mokhtar University, Annaba, Algeria)

  • Djemil Messadeg

    (Laboratory of Automatic and Signals of Annaba (LASA), Electronics department, Badji Mokhtar University, Annaba, Algeria)

  • Noureddine Doghmane

    (Laboratory of Automatic and Signals of Annaba (LASA), Electronics department, Badji Mokhtar University, Annaba, Algeria)

  • Abdelmalik Taleb-Ahmed

    (Laboratory of Automatic and Signals of Annaba (LASA), Electronics department, Badji Mokhtar University, Annaba, Algeria)

Abstract

In this paper, a new real-time approach for audio recognition of waterbird species in noisy environments, based on a Texas Instruments DSP, i.e. TMS320C6713 is proposed. For noise estimation in noisy water bird's sound, a tonal region detector (TRD) using a sigmoid function is introduced. This method offers flexibility since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Then, the features Mel Frequency Cepstral Coefficients post processed by Spectral Subtraction (MFCC-SS) were extracted for classification using Support Vector Machine classifier. A development of the Simulink analysis models of classic MFCC and MFCC-SS is described. The audio recognition system is implemented in real time by loading the created models in DSP board, after being converted to target C code using Code Composer Studio. Experimental results demonstrate that the proposed TRD-MFCC-SS feature is highly effective and performs satisfactorily compared to conventional MFCC feature, especially in complex environment.

Suggested Citation

  • Amira Boulmaiz & Djemil Messadeg & Noureddine Doghmane & Abdelmalik Taleb-Ahmed, 2017. "Design and Implementation of a Robust Acoustic Recognition System for Waterbird Species using TMS320C6713 DSK," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(1), pages 98-118, January.
    • Handle: RePEc:igg:jaci00:v:8:y:2017:i:1:p:98-118
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    Cited by:

    1. Sankhadeep Chatterjee & Sarbartha Sarkar & Nilanjan Dey & Soumya Sen, 2018. "Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.

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