Abstract:
:This letter proposes a multichannel source separation technique, the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class index. By treating the latent space variables and the class index as the unknown parameters of this generative model, we can develop a convergence-guaranteed algorithm for supervised determined source separation that consists of iteratively estimating the power spectrograms of the underlying sources, as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.
journal_name
Neural Computjournal_title
Neural computationauthors
Kameoka H,Li L,Inoue S,Makino Sdoi
10.1162/neco_a_01217subject
Has Abstractpub_date
2019-09-01 00:00:00pages
1891-1914issue
9eissn
0899-7667issn
1530-888Xjournal_volume
31pub_type
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