CFP last date
20 December 2024
Reseach Article

Audio Retrieval based on Cepstral Feature

by R. Christopher Praveen Kumar, S. Suguna, J.becky Elfreda
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 8
Year of Publication: 2014
Authors: R. Christopher Praveen Kumar, S. Suguna, J.becky Elfreda
10.5120/18774-0079

R. Christopher Praveen Kumar, S. Suguna, J.becky Elfreda . Audio Retrieval based on Cepstral Feature. International Journal of Computer Applications. 107, 8 ( December 2014), 28-33. DOI=10.5120/18774-0079

@article{ 10.5120/18774-0079,
author = { R. Christopher Praveen Kumar, S. Suguna, J.becky Elfreda },
title = { Audio Retrieval based on Cepstral Feature },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 8 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number8/18774-0079/ },
doi = { 10.5120/18774-0079 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:33.505574+05:30
%A R. Christopher Praveen Kumar
%A S. Suguna
%A J.becky Elfreda
%T Audio Retrieval based on Cepstral Feature
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 8
%P 28-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The interest towards music is rapidly growing in our day to day life. It is necessary to have efficient system to retrieve relevant music for the user. The audio retrieval system mainly depends on the feature extraction process because only the meaningful feature will provide better retrieval task. In this work, audio information retrieval has been performed on GTZAN datasets using weighted Mel-Frequency Cepstral Coefficients (WMFCC) feature which is a kind of cepstral feature. The results obtained for the various stages of feature extraction WMFCC and retrieval performance plot has been presented. The mean precision values obtained for the audio files from the GTZAN database are 96. 40% respectively.

References
  1. Tomi Kinnunen, Rahim Saeidi, "Low-Variance Multitaper MFCC Features: A Case Study In Robust Speaker Verification", IEEE Transactions On Speech, Audio And Language Processing,2012
  2. Masayuki Suzuki, Takuya Yoshioka, "MFCC Enhancement Using Joint Corrupted And Noise Feature Space For Highly Non-Stationary Noise Environments", ICASSP 2012
  3. Dalibor Mitrovi´C, Matthias Zeppelzauer, and Christian Breiteneder, "Features For Content-Based Audio Retrieval", Advances in Computers Vol. 78, pp. 71-150,2010
  4. Guodong Guo and Stan Z. Li, "Content-Based Audio Classification and Retrieval by Support Vector Machines", IEEE Transactions on Neural Networks, Vol. 14, No. 1, January 2003.
  5. Riccardo Miotto and Gert Lanckriet, "A Generative Context Model for Semantic Music Annotation and Retrieval", IEEE Transactions On Audio, Speech, And Language Processing, Vol. 20, No. 4, May 2012.
  6. Hung-Yi Lo, Ju-Chiang Wang, Hsin-Min Wang, "Homogeneous Segmentation and Classifier Ensemble for Audio Tag Annotation and Retrieval", National Science Council of Taiwan, 2008.
  7. Jean-Julien Aucouturier, François Pachet, And Mark Sandler ,"The Way It Sounds": Timbre Models For Analysis And Retrieval Of Music Signals, IEEE Transactions On Multimedia, Vol. 7, No. 6, December 2005.
  8. P. Dhanalakshmi, S. Palanivel, V. Ramalingam, "Classification of audio signals using SVM and RBFNN", Expert Systems with Applications Elsevier, 2008.
  9. Thibault Langlois, Gonc¸Alo Marques, "A Music Classification Method Based On Timbral Features", International Society for Music Information Retrieval Conference (ISMIR) 2009.
  10. Dongge Li, Ishwar K. Sethi, "Classification of General Audio Data for Content Based Retrieval", Pattern Recognition Letter, Elsevier 2001.
  11. Elif Bozkurt, Engin Erzin, "Formant position based weighted spectral features for emotion recognition", Elsevier 6 May 2011.
  12. G. Salton. The SMART Retrieval System. Prentice Hall,Englewood Cliffs, NJ, 1971.
  13. D. Sturim, D. Reynolds, E. Singer, J. Campbell. "Speakerindexing in large audio databases using anchor models. " Proc. Of ICASSP, vol. I, pp. 429–433, 2001
  14. George Tzanetakis, "Musical Genre Classification of Audio Signals", IEEE Transactions On Speech And Audio Processing, Vol. 10, No. 5, July 2002.
  15. Douglas Turnbull, Luke Barrington, David Torres, and Gert Lanckriet, "Semantic Annotation and Retrieval of Music and Sound Effects", IEEE Transactions on Audio, Speech, and Language Processing, Vol. 16, No. 2, February 2008.
  16. Tao Li, Mitsunori Ogihara, Qi Li, "A Comparative Study on Content-Based Music Genre Classification", SIGIR'03, 2003.
  17. Chandika Mohan Babu, Manish Puri and Anamika Das, "Effective principle analysis of speech recognition systems using MFCC and time domain approach for isolated word for training phase spectrum", World Journal of Science and Technology, April 2012.
  18. Atanas Ouzounov, "Cepstral Features and Text-Dependent Speaker Identification –A Comparative Study", Cybernetics and Information Technologies Volume 10, No 1, 2010.
  19. Hyoung-Gook Kim, Nicolas Moreau, and Thomas Sikora, "Audio Classification Based on MPEG-7 Spectral Basis Representations", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 5, May 2004.
  20. Vibha Tiwari, "MFCC and its applications in speaker recognition", International Journal on Emerging Technologies, November 2009.
  21. A. Ghias, J. Logan, D. Chamberlin: Query By Humming - Musical Information Retrieval in An Audio Database", Proc. ACM Multimedia Conference, pp. 231-235, Anaheim, CA, 1995.
  22. J. Foote: Content-Based Retrieval of Music and Audio", Proc. SPIE'97, Dallas, 1997.
  23. Elias Pampalk, Arthur Flexer, and Gerhard Widmer, "Improvements of audio-based music similarity and genre classi?cation," ISMIR, 2005.
  24. M. S. Lewicki, "Ef?cient coding of natural sounds", in Nature Neuroscience, Vol. 5,No. 4, pp 356-363, 2002.
  25. S. Sundaram and S. Narayanan ,"Analysis of Audio Clustering using Words". Presented at ICASSP, Hawaii, USA. 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Audio Retrieval Cepstral Feature WMFCC Feature Extraction Mel Filter Bank.