Ten lectures on wavelets
Hybrid representations for audiophonic signal encoding
Signal Processing - Image and Video Coding beyond Standards
A Greedy EM Algorithm for Gaussian Mixture Learning
Neural Processing Letters
Speech decomposition and enhancement
Speech decomposition and enhancement
Speech enhancement using transient speech components
Speech enhancement using transient speech components
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Minimum classification error learning for sequential data in the wavelet domain
Pattern Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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The auditory system, like the visual system, may be sensitive to abrupt stimulus changes, and the transient component in speech may be particularly critical to speech perception. If this component can be identified and selectively amplified, improved speech perception in background noise may be possible. This paper describes an algorithm to decompose speech into tonal, transient, and residual components. The modified discrete cosine transform (MDCT) was used to capture the tonal component and the wavelet transform was used to capture transient features. A hidden Markov chain (HMC) model and a hidden Markov tree (HMT) model were applied to capture statistical dependencies between the MDCT coefficients and between the wavelet coefficients, respectively. The transient component identified by the wavelet transform was selectively amplified and recombined with the original speech to generate modified speech, with energy adjusted to equal the energy of the original speech. The intelligibility of the original and modified speech was evaluated in eleven human subjects using the modified rhyme protocol. Word recognition rate results show that the modified speech can improve speech intelligibility at low SNR levels (8% at -15dB, 14% at -20dB, and 18% at -25dB) and has minimal effect on intelligibility at higher SNR levels.