Ikemiya, Y., Yoshii, K. and Itoyama, K. (2015) Singing Voice Analysis and Editing Based on Mutually Dependent F0 Estimation and Source Separation. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, 19-24 April 2015, 574-578.
https://doi.org/10.1109/ICASSP.2015.7178034

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.