The week's pick
Random Articles
Reseach Article
North Indian Classical Music’s Singer Identification by Timbre Recognition using MIR Toolbox
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 91 - Number 4 |
| Year of Publication: 2014 |
| Authors: Saurabh H. Deshmukh, S.g. Bhirud |
10.5120/15866-4804
|
Saurabh H. Deshmukh, S.g. Bhirud . North Indian Classical Music’s Singer Identification by Timbre Recognition using MIR Toolbox. International Journal of Computer Applications. 91, 4 ( April 2014), 1-5. DOI=10.5120/15866-4804
Abstract
Timbre recognition has always been associated with musical instrument identification. In music information retrieval (MIR) community there has always been increasing interest in identifying singers and identifying the musical instruments. There can be unending debate on, whether to consider human throat as a kind of musical instrument generating sound through air pressure and vibration or not. Any musical sound possesses 'Timbre', an unidentified and undefined entity which is yet non-tangible that uniquely defines the sound. A lot of research has been done on catching this fuzzy term in terms of its synonyms related to light or texture or so. MIR toolbox of MatLab provides some strong techniques to extract variety of audio attributes/characteristics from an audio file. These attributes are called as 'Audio Descriptors'. In this paper, we have studied such audio descriptors that fall under the Timbre category and found out the most prominent audio descriptors that plays substantial role in the identification of a singer from North Indian classical music. In this system we have considered Noisy data, Noise filtered data without background instruments and noise free studio recordings with only Tanpura instrument in the background as input. We have found that roll off, brightness, roughness and irregularity are the four strong audio descriptors designated under the Timbre category of MIR Toolbox, plays vital role in the identification of a singer from North Indian Classical Music giving accuracy of identification of 96. 66% for three singers trained and tested simultaneously on studio recordings containing Tanpura(a Supportive Musical instrument of the singer) each of 5 sec duration and sampling rate of 11,025Hz, 16 bits with Pulse Code Modulation (PCM) uncompressed file format. The results indicate that a singer can also be treated as a wind type of musical instrument and be successfully identified by recognizing its Timbre.
References
- DALIBOR MITROVIC, MATTHIAS ZEPPELZAUER, and CHRISTIAN BREITENEDER, "Features for Content -Based Audio Retrieval," Advances in Computers, vol. 78, pp. 71-150, 2010.
- Geoffroy Peeters, "A large set of audio features for sound description (Similarity and Classification) in the CUIDADO Project," Ircam,Anlysis/Synthesis Team, 1Pl Igor, Stravinsky, 75001, Paris, France, Analysis report V 1. 0, 23rd April,2004.
- ITC Infotech India Ltd. -FAQ. http://www. itcsra. org. [Online]. http://www. itcsra. org/sra_faq_index. html
- S. & Bregman, A. McAdams, "Hearing musical streams," Computer Music Journal republished in C. Roads & J. Strawn (Eds. ) The Foundations of Computer Music, MIT, Cambridge, Mass. , vol. 3, no. 4, pp. in CMJ 26-43 and later in TF of CM 658-698, 1985.
- Hyoung-Gook Kim, Nicolas Moreau, and Thomas Sikora, MPEG-7 AUDIO AND BEYOND. west susscx, England : John Wiley & Sons, Ltd, 2005.
- Tae Hong Park, "Towards Automatic Musical Instrument Timbre Recognition," Princeton University , New Jersey,USA, Dissertation Report of PhD 2004.
- Jean-Julien aucouturier, Francois Pachet, and Mark Sandler, ""the way it sounds":Timbre models for Analysis and retrieval of Music Signals," IEEE Transactions on Mulitimedia, vol. 7, no. 6, December 2005.
- Hermann von Helmholtz, On the Sensations of Tone. New York: Dover, 1954.
- Wayne Slawson, Sound Color. Berkeley: University of California Press, 1985.
- Sigmund Lewarie, A Study in Musical Acoustics. Westport, Conn: Greenwood Press, 1981.
- S. , J. W. Beauchamp, S. Meneguzzi McAdams, "Discrimination of Musical Instruments Sounds Resynthesized with Simplified Spectrotemporal Parameters," JASA , vol. 2, p. 104, 1999.
- MPEG-7 Audio. ((2005, October)) http://mpeg. chiariglione. org/standards/mpeg-7/audio. [Online]. MPEG-7 Audio. [Online].
- Olivier Lartillot, "MIRToolbox 1. 4 User's Manual ," Finnish Centre of Exce!ence in Interdisciplinary Music Research, User Manual 30th May 2012.
- Petri Toiviainen Olivier Lartillot, ""A Matlab Toolbox for Musical Feature Extraction From Audio"," in International Conference on Digital Audio Effects, Bordeaux, 2007.
- Petri Toiviainen, Tuomas Eerola Olivier Lartillot, ""A Matlab Toolbox for Music Information Retrieval"," Springer Data Analysis Machine Learning and Applications,Studies in Classification, Data Analysis, and Knowledge Organization, 2008.
- Wikipedia. (2014, Jan) Mel-Frequency_cepstrum. [Online]. http://en. wikipedia. org/wiki/Mel-frequency_cepstrum
- Saurabh Deshmukh and Sunil Bhirud, ""A Hybrid Selection Method of Audio Descriptors for Singer Identification in North Indian Classical Music"," in Emerging Trends in Engineering and Technology (ICETET) , Himji, Japan, 2012, pp. 224 - 227.
- J. F. Schouten, "Elusive attributes of timbre". , 1968 page no. 42.
- Anil K Jain and Richard C Dubes, Algorithms for Clustering Data. New Jersey, United States of America : Prentice Hall, 1948,1988.
- H. -H. Bock, "Clustering methods:- a history of k-means algorithms," in Selected Contributions in Data Analysis and Classification. Aachen, Germany: Springer Berlin Heidelberg, 2007, pp. 161-172.
Index Terms
Keywords