Compressive Sensing-Based Sound Source Localization for Microphone Arrays

Qin, Mengmeng; Hu, De; Chen, Zhe; Yin, Fuliang

doi:10.1007/s00034-021-01692-y

Cited by 7 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, when 3.2 s < t < 6 s, the appearance and disappearance of the stationary sources make the number of sources change. In fact, sparsity-based methods, especially SBL, can automatically determine the model order (source number) [11,22]. But in figure 7(c), compared to OW-SBLRVM and MF-CS, MF-SSBL can identify the DOA of the new stationary source more obviously and accurately and the time points of the stationary source appearance and disappearance can also be clearly detected in MF-SSBL as shown in figure 7(c).…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Considering the directions of the sources are always sparse in angular domain, therefore, the DOA estimation of the sound sources can be considered to be sparse signals recovery [7] in compressive sensing (CS) [10] framework. This approach has also been widely used in sound source localization [11]. CS is a technique for finding sparse solutions of underdetermined linear systems, which allows the recovery of sparse signals from a smaller number of measurements.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Guo¹,

卢²

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

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 (CS) 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 variescontinuously. Therefore, the multi-frequency sequential sparse Bayesian learning (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.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo¹,

卢²

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Two of the methods that will be mentioned in this study of sound localization include time difference of arrival (TDOA) [33] and angle of arrival [34,35]. TDOA is the time difference of the sound's arrival; then, the angle measurement is incorporated into the localization algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The generalized cross-correlation (GCC) algorithm has been widely used among many time delay estimation algorithms because of its low computational complexity and easy implementation. The accuracy of localization depends on the number of microphones [35], but that will render costs higher. Another method to improve accuracy is to use more complicated algorithms [37], but that will increase the CPU's load.…”

Section: Introductionmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

Tang,

Zhang,

Zuo

et al. 2023

Circuits Syst Signal Process

View full text Add to dashboard Cite

Compressive Sensing-Based Sound Source Localization for Microphone Arrays

Cited by 7 publications

References 29 publications

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

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

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

Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

Contact Info

Product

Resources

About