Automatic Bird Species Recognition Based on Spiking Neural Network Ricky Mohanty, Bandi Kumar Mallik and Sandeep Singh Solanki Abstract The main focus of this paper is to analyze sound waves produced by birds of many different kind using spectral features, which is present in an audio recording. This data helps in to give program examination and remote checking of the entire populace and species of birds. This can help for natural preservation of these species. Also survival plans and actions can be studied. The sounds produced by these birds have special acoustic features which could help us in detecting the type of birds. In this paper, we study about a technique which will help in finding the type of birds using the Mel Frequency Cepstral Coefficient as well as wavelet features and classification performance is compared using two different models. The classification of these features is done using Spiking Neural Network (SNN) and Gaussian Mixture Model (GMM). Spiking neural network has edge over GMM in some aspects such as less computation time and more accuracy. Keywords Mel frequency cepstral coefficient · Discrete wavelet transform · Spiking neural network (SNN) and gaussian mixture model (GMM) · Bird species recognition 1 Introduction Sound perceived in our day-to-day life is from various organisms as found in speech utter by human, avian sound, amphibian calls, mammalian calls, etc. These make sounds to do meet their daily chore or any sign of danger. These sound differ from R. Mohanty (B ) · S. S. Solanki Department of Electronics & Communication, Birla Institute of Technology (BIT), Mesra, Ranchi, India e-mail: mohantyricky@gmail.com S. S. Solanki e-mail: sssolanki@bitmesra.ac.in B. K. Mallik Central Poultry Development Organisation, Eastern Region, Bhubaneswar, India e-mail: bandim64@gmail.com © Springer Nature Singapore Pte Ltd. 2020 V. Nath and J. K. Mandal (eds.), Nanoelectronics, Circuits and Communication Systems, Lecture Notes in Electrical Engineering 642, https://doi.org/10.1007/978-981-15-2854-5_30 343