The traditional multiple audio objects codec extracts the parameters of each object in the frequency domain and produces serious confusion because of high coincidence degree in subband among objects. This paper uses sparse domain instead of frequency domain and reconstruct audio object using the binary mask from the down-mixed signal based on the sparsity of each audio object. In order to overcome high coincidence degree of subband among different audio objects, the sparse autoencoder neural network is established. On this basis, a multiple audio objects codec system is built up. To evaluate this proposed system, the objective and subjective evaluation are carried on and the results show that the proposed system has the better performance than SAOC.

Authors: Zhang, Shuang; Wu, Xihong; Qu, Tianshu
Affiliation: Peking University, Beijing, China
AES Convention: 146 (March 2019) Paper Number: 10172
Publication Date: March 10, 2019 Import into BibTeX
Subject: Machine Learning: Part 2
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=20305

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