Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

• Published in: PloS one 2021-12, Vol.16 (12), p.e0259140-e0259140
• Main Authors: Gong, Cihun-Siyong Alex, Su, Chih-Hui Simon, Chao, Kuo-Wei, Chao, Yi-Chu, Su, Chin-Kai, Chiu, Wei-Hang
• Format: Article
• Language: English
• Subjects: Acoustics; Algorithms; Amphibia; Amphibians; Analysis; Animal behavior; Animal communication; Animals; Anura; Artificial intelligence; Artificial neural networks; Big Data; Bioacoustics; Biology and Life Sciences; Classification; Computer and Information Sciences; Data collection; Datasets; Deep learning; Electrical engineering; Engineering and Technology; Frogs; Identification and classification; Long short-term memory; Machine learning; Memory, Long-Term - physiology; Memory, Short-Term - physiology; Methods; Microprocessors; Neural networks; Neural Networks, Computer; Observations; Physical Sciences; Principal components analysis; Research and Analysis Methods; Signal processing; Sound; Speech; Taiwan; Time series; Toads; Voice recognition
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0259140

The research describes the recognition and classification of the acoustic characteristics of amphibians using deep learning of deep neural network (DNN) and long short-term memory (LSTM) for biological applications. First, original data is collected from 32 species of frogs and 3 species of toads commonly found in Taiwan. Secondly, two digital filtering algorithms, linear predictive coding (LPC) and Mel-frequency cepstral coefficient (MFCC), are respectively used to collect amphibian bioacoustic features and construct the datasets. In addition, principal component analysis (PCA) algorithm is applied to achieve dimensional reduction of the training model datasets. Next, the classification of amphibian bioacoustic features is accomplished through the use of DNN and LSTM. The Pytorch platform with a GPU processor (NVIDIA GeForce GTX 1050 Ti) realizes the calculation and recognition of the acoustic feature classification results. Based on above-mentioned two algorithms, the sound feature datasets are classified and effectively summarized in several classification result tables and graphs for presentation. The results of the classification experiment of the different features of bioacoustics are verified and discussed in detail. This research seeks to extract the optimal combination of the best recognition and classification algorithms in all experimental processes.