Multi-channel audio restoration algorithm based on tensor correlated total variation

As multimedia technology continues to advance, the quality of multichannel audio data has become particularly important for enhancing the user experience. However, audio signals are susceptible to noise interference and data loss during transmission and processing, resulting in degradation of sound quality. This study focuses on the recovery of multichannel audio with deletions using tensor correlated total variation (t-CTV) regularization, which aims to recover the complete audio tensor from partially observed data. By integrating the t-CTV regularization term into the tensor singular value decomposition (t-SVD) framework, a regularization model incorporating low-rankness and local smoothness is constructed and optimized using the alternating direction multiplier method (ADMM). Experimental results on two publicly available datasets, VoiceHome-2 and Alimeeting, show that the method performs well in a variety of data loss scenarios, especially when the audio signal missing rate is up to 75%, it still can significantly recover the audio quality with a PESQ value of 3.666, which is much higher than other algorithms. This study demonstrates the applicability of the t-CTV regularization method for audio restoration tasks, while exploring potential extensions of tensor completion techniques in acoustic applications.