A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Fundamentals of speech recognition
Fundamentals of speech recognition
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical methods for speech recognition
Statistical methods for speech recognition
Approximations with evolutionary pursuit
Signal Processing
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Expert Systems with Applications: An International Journal
Wavelet denoising with evolutionary algorithms
Digital Signal Processing
Hybrid feature selection by combining filters and wrappers
Expert Systems with Applications: An International Journal
Evolutionary cepstral coefficients
Applied Soft Computing
Engineering Applications of Artificial Intelligence
Evolutionary splines for cepstral filterbank optimization in phoneme classification
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Evolutionary approach to improve wavelet transforms for image compression in embedded systems
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
Evolutionary feature selection via structure retention
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The most widely used speech representation is based on the mel-frequency cepstral coefficients, which incorporates biologically inspired characteristics into artificial recognizers. However, the recognition performance with these features can still be enhanced, specially in adverse conditions. Recent advances have been made with the introduction of wavelet based representations for different kinds of signals, which have shown to improve the classification performance. However, the problem of finding an adequate wavelet based representation for a particular problem is still an important challenge. In this work we propose a genetic algorithm to evolve a speech representation, based on a non-orthogonal wavelet decomposition, for phoneme classification. The results, obtained for a set of spanish phonemes, show that the proposed genetic algorithm is able to find a representation that improves speech recognition results. Moreover, the optimized representation was evaluated in noise conditions.