PSD Estimation and Source Separation in a Noisy Reverberant Environment using a Spherical Microphone Array
This work addresses sound source separation and noise reduction in acoustic environments, which is important for audio processing applications like hearing aids or speech recognition, but it appears incremental as it builds on existing spherical harmonics methods with specific engineering improvements.
The paper tackles the problem of estimating power spectral density (PSD) components for individual sound sources, noise, and reverberation in a noisy, multi-source reverberant environment using a spherical microphone array, achieving performance evaluated in a practical setting with a commercial array and demonstrating application in source separation with comparisons to contemporary methods.
In this paper, we propose an efficient technique for estimating individual power spectral density (PSD) components, i.e., PSD of each desired sound source as well as of noise and reverberation, in a multi-source reverberant sound scene with coherent background noise. We formulate the problem in the spherical harmonics domain to take the advantage of the inherent orthogonality of the spherical harmonics basis functions and extract the PSD components from the cross-correlation between the different sound field modes. We also investigate an implementation issue that occurs at the nulls of the Bessel functions and offer an engineering solution. The performance evaluation takes place in a practical environment with a commercial microphone array in order to measure the robustness of the proposed algorithm against all the deviations incurred in practice. We also exhibit an application of the proposed PSD estimator through a source septation algorithm and compare the performance with a contemporary method in terms of different objective measures.