Ozerov, A., Philippe, P., Gribonval, R. and Bimbot, F. (2005) One Microphone Singing Voice Separation Using Source-Adapted Models. 2005 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, 16 October 2005, 90-93.
https://doi.org/10.1109/ASPAA.2005.1540176

has been cited by the following article:

• TITLE: Low-Rank Sparse Representation with Pre-Learned Dictionaries and Side Information for Singing Voice Separation

  AUTHORS: Chenghong Yang, Hongjuan Zhang

  KEYWORDS: Singing Voice Separation, Low-Rank and Sparse, Dictionary Learning

  JOURNAL NAME: Advances in Pure Mathematics, Vol.8 No.4, April 24, 2018

  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.