Sound signals have been widely applied in various fields. One of the popular applications is sound localization, where the location and direction of a sound source are determined by analyzing the sound signal. In this study, two microphone linear arrays were used to locate the sound source in an indoor environment. The TDOA is also designed to deal with the problem of delay in the reception of sound signals from two microphone arrays by using the generalized cross-correlation algorithm to calculate the TDOA. The proposed microphone array system with the algorithm can successfully estimate the sound source’s location. The test was performed in a standardized chamber. This experiment used two microphone arrays, each with two microphones. The experimental results prove that the proposed method can detect the sound source and obtain good performance with a position error of about 2.0~2.3 cm and angle error of about 0.74 degrees. Therefore, the experimental results demonstrate the feasibility of the system.

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Sound Localization Based on Acoustic Source Using Multiple Microphone Array in an Indoor Environment

To obtain the direction of arrival (DOA) of the moving sound source from the sequential measurements collected by the microphone array is the main task in acoustic tracking and detection. Thanks to the development of compressive sensing and sparse Bayesian learning (SBL), treating time-varying DOA estimation as time-varying sparse signal recovery is considered to be a promising idea. However, most methods have assumed that the source is narrowband and the DOA is on the predefined sparse grid at each estimation step. In fact, most sound sources in the air are wideband and the DOA varies continuously. Therefore, the multi-frequency sequential SBL is proposed for the DOA estimation of the moving wideband sound source in this paper. In this method, gamma hyperprior is used as sparsity-promoting prior for multi-frequency bins so that the multi-frequency measurements can be utilized simultaneously, and with an inexact dynamic model, the sparsity-dependent information from the multi-frequency sequential measurements can be propagated to the next estimation step to improve the performance. Besides, the off-grid refinement is incorporated into the framework to adapt to the continuously varying DOA. Simulation results demonstrate that the proposed method has better performances under low signal-to-noise conditions with higher estimation accuracy and less computation time compared to other state-of-the-art methods. The field experiments show that our proposed method can has a stronger ability to suppress grating lobes and spatial aliasing than conventional methods in the estimation for wideband DOA and adapt to the scenarios where the number of sources also changes.

Multi-frequency sequential sparse Bayesian learning for DOA estimation of the moving wideband sound source

Low-frequency sound source localization in enclosed space based on time reversal method

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Sound source localization with less data is a challenging task. To address this problem, a novel sound source localization method based on compressive sensing theory is proposed in this paper. Specifically, a sparsity basis is first constructed for each microphone by shifting the audio signal recorded from one reference microphone. In this manner, the microphones except the reference one are allowed to capture audio signals under the sampling rate far below the Nyquist criterion. Next, the source positions are estimated by solving an $$l_1$$
 minimization based on each frame of audio signals. Finally, a fine localization scheme is presented by fusing the estimated source positions from multiple frames. The proposed method can directly determine the number of sound sources in one step and successfully estimate the source positions in noisy and reverberant environments. Experimental results demonstrate the validity of the proposed method.

Mengmeng Qin

Papers

Compressive Sensing-Based Sound Source Localization for Microphone Arrays