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Discrete Frequency Warped Wavelets: Theory and Applications
 IEEE Trans. Signal Processing
, 1998
"... In this paper, we extend the definition of dyadic wavelets to include frequency warped wavelets. The new wavelets are generated and the transform computed in discretetime by alternating the Laguerre transform with perfect reconstruction filterbanks. This scheme provides the unique implementation of ..."
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Cited by 14 (7 self)
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In this paper, we extend the definition of dyadic wavelets to include frequency warped wavelets. The new wavelets are generated and the transform computed in discretetime by alternating the Laguerre transform with perfect reconstruction filterbanks. This scheme provides the unique implementation of orthogonal or biorthogonal warped wavelets by means of rational transfer functions. We show that the discretetime warped wavelets lead to welldefined continuoustime wavelet bases, satisfying a warped form of the twoscale equation. The shape of the wavelets is not invariant by translation. Rather, the "wavelet translates" are obtained from one another by allpass filtering. We show that the phase of the delay element is asymptotically a fractal. A feature of the warped wavelet transform is that the cutoff frequencies of the wavelets may be arbitrarily assigned while preserving a dyadic structure. The new transform provides an arbitrary tiling of the timefrequency plane, which can be designed by selecting as little as a single parameter. This feature is particularly desirable in cochlear and perceptual models of speech and music, where accurate bandwidth selection is an issue. As our examples show, by defining pitchsynchronous wavelets based on warped wavelets, the analysis of transients and denoising of inharmonic pseudoperiodic signals is greatly enhanced.
Wavelet Based Feature Extraction for Phoneme Recognition
 Proc. of 4th Int. Conf. of Spoken Language Processing
"... In an effort to provide a more efficient representation of the acoustical speech signal in the preclassification stage of a speech recognition system, we consider the application of the BestBasis Algorithm of Coifman and Wickerhauser. This combines the advantages of using a smooth, compactlysuppo ..."
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Cited by 10 (0 self)
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In an effort to provide a more efficient representation of the acoustical speech signal in the preclassification stage of a speech recognition system, we consider the application of the BestBasis Algorithm of Coifman and Wickerhauser. This combines the advantages of using a smooth, compactlysupported wavelet basis with an adaptive timescale analysis dependent on the problem at hand.
Analysis and synthesis of pseudoperiodic 1/flike noise by means of wavelets with applications to digital audio
 EURASIP JASP
"... Voiced musical sounds have nonzero energy in sidebands of the frequency partials. Our work is based on the assumption, often experimentally verified, that the energy distribution of the sidebands is shaped as powers of the inverse of the distance from the closest partial. The power spectrum of these ..."
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Cited by 5 (3 self)
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Voiced musical sounds have nonzero energy in sidebands of the frequency partials. Our work is based on the assumption, often experimentally verified, that the energy distribution of the sidebands is shaped as powers of the inverse of the distance from the closest partial. The power spectrum of these pseudoperiodic processes is modeled by means of a superposition of modulated 1/f components, that is, by a pseudoperiodic 1/flike process. Due to the fundamental selfsimilar character of the wavelet transform, 1/f processes can be fruitfully analyzed and synthesized by means of wavelets. We obtain a set of very loosely correlated coefficients at each scale level that can be well approximated by white noise in the synthesis process. Our computational scheme is based on an orthogonal Pband filter bank and a dyadic wavelet transform per channel. The P channels are tuned to the left and right sidebands of the harmonics so that sidebands are mutually independent. The structure computes the expansion coefficients of a new orthogonal and complete set of harmonicband wavelets. The main point of our scheme is that we need only two parameters per harmonic in order to model the stochastic fluctuations of sounds from a pure periodic behavior.
Dynamic Models of PseudoPeriodicity
 PROCEEDINGS OF THE 99 DIGITAL AUDIO EFFECTS WORKSHOP
, 1999
"... Voiced musical sounds have nonzero energy in sidebands of the frequency partials. Our work is based on the assumption, often experimentally verified, that the energy distribution of the sidebands is shaped as powers of the inverse of the distance from the closest partial. The power spectrum of thes ..."
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Cited by 4 (3 self)
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Voiced musical sounds have nonzero energy in sidebands of the frequency partials. Our work is based on the assumption, often experimentally verified, that the energy distribution of the sidebands is shaped as powers of the inverse of the distance from the closest partial. The power spectrum of these pseudoperiodic processes is modeled by means of a superposition of modulated 1/f components, i.e., by a pseudoperiodic 1/flike process. Due to the fundamental selfsimilar character of the wavelet transform, 1/f processes can be fruitfully analyzed and synthesized by means of wavelets, obtaining a set of very loosely correlated coefficients at each scale level that can be well approximated by white noise in the synthesis process. Our computational scheme is based on an orthogonal Pband filter bank and a dyadic wavelet transform per channel. The P channels are tuned to the left and right sidebands of the harmonics so that sidebands are mutually independent. The structure computes the e...
Dispersive and PitchSynchronous Processing of Sounds
"... The aim of this paper is to present results on digital processing of sounds by means of both dispersive delay lines and pitchsynchronous transforms in a unified framework. The background on frequency warping is detailed and applications of this technique are pointed out with reference to the exi ..."
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Cited by 3 (2 self)
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The aim of this paper is to present results on digital processing of sounds by means of both dispersive delay lines and pitchsynchronous transforms in a unified framework. The background on frequency warping is detailed and applications of this technique are pointed out with reference to the existing literature. These include transient extraction, pitch shifting, harmonic detuning and auditory modeling.
TimeVarying Frequency Warping: Results and Experiments
"... Dispersive tapped delay lines are attractive structures for altering the frequency content of a signal. In previous papers we showed that in the case of a homogeneous line with first order allpass sections the signal formed by the output samples of the chain of delays at a given time is equivalent ..."
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Cited by 2 (1 self)
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Dispersive tapped delay lines are attractive structures for altering the frequency content of a signal. In previous papers we showed that in the case of a homogeneous line with first order allpass sections the signal formed by the output samples of the chain of delays at a given time is equivalent to compute the Laguerre transform of the input signal. However, most musical signals require a timevarying frequency modification in order to be properly processed. Vibrato in musical instruments or voice intonation in the case of vocal sounds may be modeled as small and slow pitch variations. Simulations of these effects require techniques for timevarying pitch and/or brightness modification that are very useful for sound processing. In our experiments the basis for timevarying frequency warping is a timevarying version of the Laguerre transformation. The corresponding implementation structure is obtained as a dispersive tapped delay line, where each of the frequency dependent delay elem...
AN AUTOMATIC SYSTEM FOR TURKISH WORD RECOGNITION USING DISCRETE WAVELET NEURAL NETWORK BASED ON ADAPTIVE ENTROPY
"... In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of tw ..."
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Cited by 1 (0 self)
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In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multilayer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the timefrequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multilayer perceptron used for classification is a feedforward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about % 92.58 for the sample speech signals. Key words: word recognition, Turkish word signal, feature extraction, DWT, entropy, wavelet neural networks, automatic system. * Address for correspondence: