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.

To reduce the burden of storing and transmitting audio signals, they are often compressed with a lossy single-channel code. Because the high-frequency components are effectively truncated when using a low bitrate encoder, listeners may experience the sound as being uncomfortable, muffled, or dull. To compensate for the perceived degradation, bandwidth extension technology can be used to regenerate the missing high frequencies from the low-frequency components during the decoding process. In this paper the authors propose a bandwidth extension method based on Generative Adversarial Networks (GAN), which is used to estimate the relationship between the MDCT spectrum in the high-frequency part and the low-frequency part. It is evaluated by a discriminant network in the GAN to get a more natural result. A complete audio coding system was built by using AAC Low Complex as the single-channel core encoder with the proposed bandwidth extension method. To evaluate the audio quality decoded by the new system, a subjective evaluation experiment was carried out using the HE-AAC as the baseline system with the MUSHRA experimental method.