Dual-Channel Acoustic Event Detection in Multisource Environments Using Nonnegative Tensor Factorization and Hidden Markov Model

Abstract

In this paper, we propose a dual-channel acoustic event detection (AED) method using nonnegative tensor factorization (NTF) and hidden Markov model (HMM) in order to improve detection accuracy of AED in multisource environments. The proposed method first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique applied to dual-channel input signals. After that, an HMM-based likelihood ratio test is carried out to verify the detected events by using channel gains. The detection accuracy of the proposed method is measured by F-measures under 9 different multisource conditions. Then, it is also compared with those of conventional AED methods such as Gaussian mixture model and nonnegative matrix factorization. It is shown from the experiments that the proposed method outperforms the convectional methods under all the multisource conditions.

Instantaneous Incident Detection System Based on Analysis of Acoustic Signal from Crash and Skid in Tunnel

“…(2), exploits nonnegative tensor factorization and hidden Markov model techniques to identify incident-related acoustic signals. The algorithm initially suggested by Jeon et al [9] first detects multiple acoustic events by utilizing channel gains obtained from the NTF technique. Subsequently, an HMM-based likelihood ratio test is performed to verify the detected events.…”