Low-Rank Sparse Representation with Pre-Learned Dictionaries and Side Information for Singing Voice Separation ()
ABSTRACT
At present, although the human speech separation has
achieved fruitful results, it is not ideal for the separation of singing and
accompaniment. Based on low-rank and sparse optimization theory, in this paper,
we propose a new singing voice separation algorithm called Low-rank, Sparse
Representation with pre-learned dictionaries and side Information (LSRi). The
algorithm incorporates both the vocal and instrumental spectrograms as sparse
matrix and low-rank matrix, meanwhile combines pre-learning dictionary and the
reconstructed voice spectrogram form the annotation. Evaluations on the iKala
dataset show that the proposed methods are effective and efficient for singing
voice separation.
Share and Cite:
Yang, C. and Zhang, H. (2018) Low-Rank Sparse Representation with Pre-Learned Dictionaries and Side Information for Singing Voice Separation.
Advances in Pure Mathematics,
8, 419-427. doi:
10.4236/apm.2018.84024.
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