In a reverberant environment, interferences such as reflections and background noise can degrade the perception of the sound source signal. Although the DNN-based methods have made a tremendous breakthrough in addressing this issue, the performance of these models is highly dependent on the completeness of the training dataset, which will limit its generalization under unknown environments. In this article, we propose a physical model-based self-supervised learning (PMSSL) method to realize the DNN model optimization under unknown scenarios. This method incorporates a room reverberation physical model into the sound source enhancement model optimization process, realizing the self-learning of the DNN model under physical constraints. In this process, the time-frequency characteristics of the input signal and the spatial feature of the reverberation environment are utilized for parameter optimization, improving the adaptability of the DNN model under unknown scenarios. Experimental results based on simulated and measured data prove that the proposed method can obtain much more accurate source signal enhancement results compared with the pre-trained models, verifying its effectiveness and adaptability in new environments.

The compression of three-dimensional sound field signals has always been a very important issue. Recently, an Independent Component Analysis (ICA) based Higher Order Ambisonics (HOA) compression method introduces blind source separation to solve the shortcomings of discontinuity between frames in the existing Singular Value Decomposition (SVD) based methods. However, ICA is weak to model the reverberant environment, and its target is not to recover original signal. In this work, we replace ICA with autoencoder to further improve the above method’s ability to cope with reverberation conditions and ensure the unanimous optimization both in separation and recovery by reconstruction loss. We constructed a dataset with simulated and recorded signals, and verified the effectiveness of our method through objective and subjective experiments.