ANALYSIS OF MEL BASED FEATURES FOR AUDIO RETRIEVAL

Nowadays the electronic gadgets have been updated to store large amount of music information. It is necessary to have an efficient retrieval system to choose the required data. The important task in audio retrieval system is feature extraction. In the feature extraction stage, the feature which gives relevant information about music has to be extracted. In this paper, various Mel based feature which includes Mel Frequency Cepstral Coefficient (MFCC), Delta MFCC (DMFCC), Double Delta MFCC (DDMFCC), hybrid feature (MFCC+DMFCC+DDMFCC) has been analyzed for audio retrieval system. It has been found out that the audio retrieval system which makes use of hybrid feature will provide better result compared to the other features.

[1]  P. Dhanalakshmi,et al.  Classification of audio signals using SVM and RBFNN , 2009, Expert Syst. Appl..

[2]  François Pachet,et al.  "The way it Sounds": timbre models for analysis and retrieval of music signals , 2005, IEEE Transactions on Multimedia.

[3]  Gonçalo Marques,et al.  A Music Classification Method based on Timbral Features , 2009, ISMIR.

[4]  Tao Li,et al.  A comparative study on content-based music genre classification , 2003, SIGIR.

[5]  Thomas Sikora,et al.  Audio classification based on MPEG-7 spectral basis representations , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Haizhou Li,et al.  Low-Variance Multitaper MFCC Features: A Case Study in Robust Speaker Verification , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Atanas Ouzounov.  Cepstral Features and Text-Dependent Speaker Identification – A Comparative Study , 2010 .

[8]  Keikichi Hirose,et al.  MFCC enhancement using joint corrupted and noise feature space for highly non-stationary noise environments , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[9]  Ishwar K. Sethi,et al.  Classification of general audio data for content-based retrieval , 2001, Pattern Recognit. Lett..

[10]  Christian Breiteneder,et al.  Features for Content-Based Audio Retrieval , 2010, Adv. Comput..

[11]  Santosh Chapaneri,et al.  Spoken Digits Recognition using Weighted MFCC and Improved Features for Dynamic Time Warping , 2012 .

[12]  Guodong Guo,et al.  Content-based audio classification and retrieval by support vector machines , 2003, IEEE Trans. Neural Networks.

[13]  Riccardo Miotto,et al.  A Generative Context Model for Semantic Music Annotation and Retrieval , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Hsin-Min Wang,et al.  Homogeneous segmentation and classifier ensemble for audio tag annotation and retrieval , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[15]  E Tsunoo,et al.  Beyond Timbral Statistics: Improving Music Classification Using Percussive Patterns and Bass Lines , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[17]  V. Tiwari.  MFCC and its applications in speaker recognition , 2010 .