Ms. Monali R. Pimpale, Prof. Shanthi Therese , Prof. Vinayak Shinde,, ,
Affiliations Department of Computer Engineering, Mumbai University, Shree L.R. Tiwari College of Engineering and Technology,Mira Road, India.
now a day’s multimedia databases are growing rapidly on large scale. For the effective management and exploration of large amount of music data the technology of singer identification is developed. With the help of this technology songs performed by particular singer can be clustered automatically. To improve the Performance of singer identification the technologies are emerged that can separate the singing voice from music accompaniment. One of the methods used for separating the singing voice from music accompaniment is non-negative matrix partial co factorization. This paper studies the different techniques for separation of singing voice from music accompaniment.
Monali R. Pimpale," A Survey on: Sound Source Separation Methods”, International Journal of Computer Engineering In Research Trends, 3(11):580-584,November-2016
Keywords : singer identification, non-negative matrix partial co factorization
 Tuomas Virtanen ,”Unsupervised Learning Methods for Source Separation in Monaural Music Signals” Tuomas Virtanen
 T. Virtanen, “Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 3, pp. 1066–1074,Mar. 2007.
 J. Yoo et al., “Nonnegative matrix partial co-factorization for drum source separation,” in Proc. IEEE Int. Conf. Acoust. Speech, Signal Process., 2010, pp. 1942 1945.
 M. Kim et al., “Nonnegative matrix partial co-factorization for spectral and temporal drum source separation,” IEEE J. Sel. Topics Signal Process., vol. 5, no. 6, pp. 1192–1204, Dec. 2011.
 Y. Hu and G. Z. Liu, “Singer identification based on computational auditory scene analysis and missing feature methods,” J. Intell. Inf. Syst., pp. 1–20, 2013.
 McAulay, Robert J., and Thomas F. Quatieri. "Pitch estimation and voicing detection based on a sinusoidal speech model." Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on. IEEE, 1990.
 T. Virtanen, A. Mesaros, and M. Ryynanen, “Combining pitch-based inferenceandnon-negative spectrogram factorization in separating vocals from polyphonic music,” in Proc. ISCA Tutorial Res. Workshop Statist. Percept. Audit. (SAPA), 2008
 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.
 Ying Hu and Guizhong Liu, “Separation of Singing Voice Using Nonnegative Matrix Partial CoFactorization for Singer Identification”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 4, pp. 643 – 653, April 2015.
 Yipeng Li, DeLiang Wang, Separation of Singing Voice from Music Accompaniment for Monaural Recordings, IEEE Transactions on Audio, Speech, and Language Processing,v.15 n.4, p.1475-1487, May 2007.
 Virtanen, Tuomas. "Sound source separation using sparse coding with temporal continuity objective." Proc. ICMC. Vol. 3. 2003.
 ICASSP 2007 Tutorial - Audio Source Separation based on Independent Component Analysis Shoji Makino and Hiroshi Sawada (NTT Communication Science Laboratories, NTT Corporation)
 Makino, Shoji, et al. "Audio source separation based on independent component analysis." Circuits and Systems, 2004. ISCAS'04. Proceedings of the 2004 International Symposium on. Vol. 5. IEEE, 2004.
 Virtanen, Tuomas. "Separation of sound sources by convolutive sparse coding." ISCA Tutorial and Researc Workshop (ITRW) on Statistical and Perceptual Audio Processing. 2004.
 Non-negative matrix factorization based compensation of music for automatic speech recognition, Bhiksha Raj, T. Virtanen, Sourish Chaudhure, Rita Singh, 2010.
 Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. "Speaker verification using adapted Gaussian mixture models." Digital signal processing 10.1 (2000): 19-41.
 Hochreiter, Sepp, and Michael C. Mozer. "Monaural separation and classification of mixed signals: A support-vector regression perspective." 3rd International Conference on Independent Component Analysis and Blind Signal Separation, San Diego, CA. 2001.
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