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

Amira Boulmaiz, Djemil Messadeg, Noureddine Doghmane and Abdelmalik Taleb-Ahmed
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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

International Journal of Ambient Computing and Intelligence (IJACI), 2017, vol. 8, issue 1, 98-118

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.

Date: 2017
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