A Sound Source Identification Algorithm Based on Bayesian Compressive Sensing and Equivalent Source Method Nearfield acoustic holography (NAH) based on equivalent source method (ESM) is an effective method for identifying sound sources. Conventional ESM focuses on relatively low frequencies and cannot provide a satisfactory solution at high frequencies. So its improved method called wideband acoustic holography (WBH) has been proposed, which has high reconstruction accuracy at mediumtohigh frequencies. However, it is less accurate for coherent sound sources at low frequencies. To improve the reconstruction accuracy of conventional ESM and WBH, a sound source identification algorithm based on Bayesian compressive sensing (BCS) and ESM is proposed. This method uses a hierarchical Laplace sparse prior probability distribution, and adaptively adjusts the regularization parameter, so that the energy is concentrated near the correct equivalent source. Referring to the function beamforming idea, the original algorithm with order v can improve its dynamic range, and then more accurate position information is obtained. Based on the simulation of irregular microphone array, comparisons with conventional ESM and WBH show that the proposed method is more accurate, suitable for a wider range of frequencies, and has better reconstruction performance for coherent sources. By increasing the order v, the coherent sources can be located accurately. Finally, the stability and reliability of the proposed method are verified by experiments.

Robust Bayesian and Maximum a Posteriori Beamforming for Hearing Assistive Devices Multimicrophone speech enhancement systems often apply beamforming to enhance one or multiple desired signals in a noisy environment. Common for many beamforming methods, is that they require the directionofarrival (DOA) of the target sound source to be known in order to achieve optimal noise reduction performance. To improve robustness against DOA uncertainty, we propose maximum a posteriori (MAP) and Bayesian beamformers that are able to take advantage of prior information on the target direction. We compare the proposed MAP and Bayesian beamformers to stateoftheart beamforming methods for noise reduction in hearing assistive devices. We evaluate the proposed beamformers in isotropic babble noise in terms of segmental SNR (SSNR) and extended shorttime objective intelligibility (ESTOI). Results show that the proposed methods outperform current stateoftheart beamformers used for noise reduction in hearing aids in most scenarios.