Improving the evaluation of soundscape variability via blind source separation

Presented in 174th Meeting of the Acoustical Society of America @ New Orleans, USA

Improving the evaluation of soundscape variability via blind source separation

Tzu-Hao Lin, Yu Tsao
Research Center for Information Technology Innovation, Academia Sinica

Tomonari Akamatsu
National Research Institute of Fisheries Science, Japan Fisheries Research and Education Agency

Mao-Ning Tuanmu, Joe Chun-Chia Huang
Biodiversity Research Center, Academia Sinica

Chiou-Ju Yao
National Museum of Natural Science

Shih-Hua Fang
Department of Electrical Engineering, Yuan Ze University

Abstract

Evaluation of soundscape variability is essential for acoustic-based biodiversity monitoring. To study biodiversity change, many researchers tried to quantify the complexity of biological sound. However, the analysis of biological sound remains difficult because the soundscape is made up of multiple sound sources. To facilitate the acoustic analysis, we have applied non-negative matrix factorization (NMF) to separate different sound sources in an unsupervised manner. NMF is a self-learning algorithm which factorizes a non-negative matrix as a basis matrix and an encoding matrix. Based on the periodicity information learned from the encoding matrix, biological chorus and the other noise sources can be efficiently separated. Besides, vocalizations of different species can also be separated by using the encoding information learned from multiple layers of NMF and convolutive NMF. In this presentation, we will demonstrate the application of NMF-based blind source separation in the analysis of long-duration field recordings. Our preliminary results suggest that NMF-based blind source separation can effectively recognize biological and non-biological sounds without any learning database. It can also accurately differentiate different vocalizing animals and improve acoustic-based biodiversity monitoring in a noisy environment.

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