The week's pick
Reseach Article
Separation of Singing Voice from Music Background
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 129 - Number 4 |
| Year of Publication: 2015 |
| Authors: Harshada Burute, P.B. Mane |
10.5120/ijca2015906806
|
Harshada Burute, P.B. Mane . Separation of Singing Voice from Music Background. International Journal of Computer Applications. 129, 4 ( November 2015), 22-26. DOI=10.5120/ijca2015906806
Abstract
Songs are representation of audio signal and musical instruments. An audio signal separation system should be able to identify different audio signals such as speech, background noise and music. In a song the singing voice provides useful information regarding pitch range, music content, music tempo and rhythm. An automatic singing voice separation system is used for attenuating or removing the music accompaniment. The paper presents survey of the various algorithm and method for separating singing voice from musical background. From the survey it is observed that most of researchers used Robust Principal Component Analysis method for separation of singing voice from music background, by taking into account the rank of music accompaniment and the sparsity of singing voices.
References
- Yipeng Li and DeLiang, “Separation of Singing Voice From Music Accompaniment for Monaural Recordings”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 4, pp. 1475 – 1487, May 2007.
- A.S.Bregman, Auditory scene analysis, Cambridge, MA: MIT press, 1990, pp.1-45,455-528.
- Alexey Ozerov, Pierrick Philippe, Frederic Bimbot and RemiGribonval, “Adaptation of Bayesian models for single channel source separation and its application to voice/music separation in popular songs”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no.5, pp. 1564-1578, July 2007.
- Chao-Ling Hsu, DeLiang Wang, Jyh-Shing Roger Jang and Ke Hu, “A Tandem Algorithm for Singing Pitch Extraction and voice Separation from Music accompaniment”, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, no.5, pp.1482-1491, July 2012.
- Hideyuki Tachibana, Nobutaka Ono and Shigeki Sagayama, “Singing voice enhancement in monaural music signals based on two-stage harmonic/percussive sound separation on multiple resolution spectrograms” , IEEE Transactions on Audio, Speech, and Language Processing, vol.22, no.1, pp.228-237, January 2014.
- Bilei Zhu, Wei Li, Ruijiang Li and XiangyangXue, “Multi-stage non negative matrix factorization for monaural singing voice separation”, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, no.10, pp.2096-2107, October 2013.
- S. Umesh and Rohit Sinha, “A study of filter bank smoothing in MFCC features for recognition of children’s speech”, IEEE Transactions on Audio, Speech, and Language Processing, vol.15, no.8, pp.2418-2430, November 2007.
- Zafar Rafii, Francois G. Germain, Dennis L. Sun and Gautham J. Mysore, “Combining modelling of singing voice and background music for automatic separation of musical mixtures”, ISMIR, 2013.
- Ifeachor and Jervis, “Digital signal processing: a practical approach”, second edition, Pearson educations, pp.645-680.
- Po-.Sen. Huang, Scott Deeann Chen, Paris Smaragdis and Mark Hasegawa-Johnson, “Singing voice separation from monaural recordings using robust principal component analysis”, ICASSP, 2012.
- Zhouchen Lin, Minming Chen, Leqin Wu and Yi Ma, “The augmented Langrange multiplier method exact recovery of corrupted low-rank matrices”, Tech. Rep.UILU-ENG-09- 2215, UIUC, Nov.2009.
- Emmanuel J. Candes, Xiaodong Li, Yi Ma and John Wright, “Robust principal component analysis ?”, Journal of the ACM, vol.58, no. 3, article 11, pp.11:1-11:37, May 2011.
- John Wright, Yigang Peng, Yi Ma, Arvind Ganesh and Shankar Rao, “Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices by Convex Optimization”, pp. 1-9.
- Justin J. Salamon, “Melody Extraction from Polyphonic Music Signals” , Ph.D. thesis, Department of Information and Communication Technologies University Pompeu Fabra, Barcelona, Spain, 2013.
- Kerui Min, Zhengdong Zhang, John Wright and Yi Ma, “Decomposing background topics from keywords by principal component pursuit”, CIKM, 2010.
- Zafar Rafii and Bryan Pardo, “A simple music/voice separation method based on the extraction of the repeating musical structure”, ICASSP, May 2011, pp.221-224.
- Chao-Ling Hsu and Jyh-Shing Roger Jang, “On the improvement of singing voice separation for monaural recordings using the MIR-1K datasets”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, Issue 2, pp. 310-319, February 2010.
- Justin Salamon, Emilia Gómez, Daniel P. W. Ellis and Gael Richard, “Melody extraction from polyphonic music signals: Approaches, applications and challenges,” IEEE Signal Processing Magazine, pp. 118-134, March 2014.
- Zafar Rafii and Bryan Pardo, “REpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation”, IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 1, pp. 71 – 82, January 2013.
- Shankar Vembu and Stephan Baumann, “Separation of Vocal from Polyphonic Audio Recordings”, Proceedings of 6th international conference of Music Information Retrieval, 2005.
- Ying Hu and Guizhong Liu, “Separation of Singing Voice Using Nonnegative Matrix Partial Co-Factorization for Singer Identification”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 4, pp. 643 – 653, April 2015.
Index Terms
Keywords