Blind Acoustic Source Separation Via System Identification for Leak Detection in Pipelines

Abstract

The main motivation for this paper is to improve an acoustic leak detection system for pipelines by using blind source separation. In this setup hundreds of microphones are used to continuously monitor a pipeline. We propose to use a source separation scheme to eliminate overlapping sounds in the measured signals making is easier to detect and locate acoustic events in the measured data. To separate the sources, a large scale system identification problem results. In this paper we present one way that the identification problem can be made more computationally efficient. First, the blind source separation problem is parameterized as a channel estimation problem. Due to the presence of echoes, the channel impulse responses are very long, but are sparse in the sense that they are zero for a significant portion of the response. Then this sparsity is exploited for reducing the computational complexity of the identification problem. Our method is tested on a small scale test pipeline.