Comparing MFCC and MPEG-7 audio features for feature extraction, maximum likelihood HMM and entropic prior HMM for sports audio classification

Authors:
Ziyou Xiong;R. Radhakrishnan;A. Divakaran;T. S. Huang
Affiliations:
Illinois Univ., Urbana, IL, USA;Sch. of Electr., Comput. & Telecommun. Eng., Wollongong Univ., NSW, Australia;Sch. of Electr., Comput. & Telecommun. Eng., Wollongong Univ., NSW, Australia;Perceptual Interfaces & Reality Lab., Maryland Univ., College Park, MD, USA
Venue:
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Year:
2003

Citing 0
Cited 5

Detection of speech and music based on spectral tracking

Speech Communication
Repeating pattern discovery from audio stream

ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Feature analysis and classification of classical musical instruments: an empirical study

ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Identifying the classical music composition of an unknown performance with wavelet dispersion vector and neural nets

Information Sciences: an International Journal
OS-Guard: on-site signature based framework for multimedia surveillance data management

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale frequency cepstrum coefficients (MFCC). For the classification we also have two choices: maximum likelihood hidden Markov models (ML-HMM) and entropic prior HMM (EP-HMM). EP-HMM, in turn, has two variations: with and without trimming of the model parameters. We thus have 6 possible methods, each of which corresponds to a combination. Our results show that all the combinations achieve classification accuracy of around 90% with the best and the second best being MPEG-7 features with EP-HMM and MFCC with ML-HMM.