Name
Abstract | ||
|---|---|---|
The need to classify audio into categories such as speech or music is an important aspect of many multimedia document retrieval systems. In this paper, we investigate audio features that have not been previously used in music-speech classification, such as the mean and variance of the discrete wavelet transform, the variance of Mel-frequency cepstral coefficients, the root mean square of a lowpass signal, and the difference of the maximum and minimum zero-crossings. We, then, employ fuzzy C-means clustering to the problem of selecting a viable set of features that enables better classification accuracy. Three different classification frameworks have been studied:Multi-Layer Perceptron (MLP) Neural Networks, radial basis functions (RBF) Neural Networks, and Hidden Markov Model (HMM), and results of each framework have been reported and compared. Our extensive experimentation have identified a subset of features that contributes most to accurate classification, and have shown that MLP networks are the most suitable classification framework for the problem at hand. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/s00530-006-0034-0 | Multimedia Systems |
Keywords | Field | DocType |
radial basis function,discrete wavelet transform,document retrieval,machine learning,root mean square,multi layer perceptron,mel frequency cepstral coefficient,hidden markov model | Mel-frequency cepstrum,Computer science,Discrete wavelet transform,Artificial intelligence,Audio signal processing,Cluster analysis,Artificial neural network,Pattern recognition,Fuzzy logic,Speech recognition,Hidden Markov model,Perceptron,Machine learning | Journal |
Volume | Issue | ISSN |
12 | 1 | 1432-1882 |
Citations | PageRank | References |
18 | 0.80 | 25 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Kashif Saeed Khan | 1 | 26 | 1.35 |
| Wasfi G. Al-Khatib | 2 | 139 | 11.03 |