Multi-channel audio recovery based on tensor decomposition

In order to recover the missing data of multi-channel audio during collection, an efficient tensor decomposition method was proposed based on weight optimization. Firstly, multi-channel audio was represented as an audio tensor. A weight tensor of the identical size as audio tensor was defined, and which identified the location of missing channel data. Afterward, the CANDECOMP/PARAFAC decomposition (CPD) was formulated as a weighted least squares problem which was solved by using a first-order optimization approach. At last, the audio were recovered via achieved factor matrices. In experiments about varying number of missing channel entries, the results of multiple stimuli with hidden reference and anchor show that it is validated by CPD of multi-channel audio in the presence of missing data, and tensor decomposition is a successful approach for recover audio.