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Bird species recognition using spiking neural network along with distance based fuzzy co-clustering

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Abstract

This paper has emphasized several sounds for Bird Species Recognition based on their vocalization. Although various techniques have been designed with good equipment for identifying different birds’ sounds, still it has limitations to classify or recognize different birds’ sounds at dissimilar moments of traveling. Thus, this paper presents the model for recognizing the bird’s sound in several situations using Spiking Neural Network (SNN). In this model, Permutation Pair Frequency Matrix Coefficients are considered for the features representing the bird sound frames, after the application of pre-processing method on the moment of recordings. These features are applied as input to the three-layered SNN and subsequently to the hidden layer, which performs the work of spiking if there is any change in the feature. The spikes activate any one of the neuron’s outputs, that produces a higher probability class. Further, the SNN along with the Distance Fuzzy Co-clustering algorithm technique used on these bird sounds to achieve optimum results. The performance of this model achieved more than 94% accuracy and a computation time of 4.16 ms better than other existing approaches.

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Correspondence to Vinayakumar Ravi.

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Mohanty, R., Bhuyan, H.K., Pani, S.K. et al. Bird species recognition using spiking neural network along with distance based fuzzy co-clustering. Int J Speech Technol 26, 681–694 (2023). https://doi.org/10.1007/s10772-023-10040-1

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