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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Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

Article for IEEE MLSP 2017 DATA CHALLENGE

Deblending of simultaneous-source seismic data via periodicity-coded nonnegative matrix factorization

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

Abstract

To increase the efficiency of seismic acquisition, one needs to break down the simultaneous-source seismic data into single source responses by a procedure called deblending. In this study, we employed the periodicity-coded non-negative matrix factorization (PC-NMF) to separate the primary and the secondary sources recorded in Petroleum Geo-Services dataset. Due to the application of random delay times between consecutive shots, the two sources displayed different patterns among various shots. By combining the response matrices of successive receivers, the PC-NMF can learn the basic components and group the basic components into two clusters according to the periodicity among shot index. Therefore, the deblending of simultaneous-source seismic data can be effectively achieved in an unsupervised manner.

The full paper can be accessed in IEEE DATA PORT.