Speech processing using group delay functions

Murthy, Hema A.; Yegnanarayana, B.

doi:10.1016/0165-1684(91)90014-a

Cited by 44 publications

(30 citation statements)

References 4 publications

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“…Additionally, this desynchronization is centered on the first harmonic phase, like for the RPS. The finite difference makes also the PD similar to the group delay, whose perceived characteristics have been already studied and demonstrated [56] and whose applications are numerous [48,[57][58][59][60][61].…”

Section: From Relative Phase Shift To Phase Distortionmentioning

confidence: 99%

A uniform phase representation for the harmonic model in speech synthesis applications

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

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Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

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Section: From Relative Phase Shift To Phase Distortionmentioning

confidence: 99%

A uniform phase representation for the harmonic model in speech synthesis applications

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

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“…The main problem is the phase unwrapping operation or window synchronization [17]. It has been shown in various studies ([17]- [19]) that a large number of very high jumps in the phase slope (i.e., the-negative-group delay function) of audio signals exist due to zeros of the z-transform closely located around the unit circle. Steiglitz and Dickinson [20] have shown that the roots of the z-transform of a short-time signal tend to be evenly distributed in angle and tightly clustered near the unit circle as the degree of the polynomial (length of the time domain signal) increases.…”

mentioning

confidence: 99%

Three Dimensions of Pitched Instrument Onset Detection

Holzapfel

Stylianou

Gedik

et al. 2010

IEEE Trans. Audio Speech Lang. Process.

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Abstract-In this paper, we suggest a novel group delay based method for the onset detection of pitched instruments. It is proposed to approach the problem of onset detection by examining three dimensions separately: phase (i.e., group delay), magnitude and pitch. The evaluation of the suggested onset detectors for phase, pitch and magnitude is performed using a new publicly available and fully onset annotated database of monophonic recordings which is balanced in terms of included instruments and onset samples per instrument, while it contains different performance styles. Results show that the accuracy of onset detection depends on the type of instruments as well as on the style of performance. Combining the information contained in the three dimensions by means of a fusion at decision level leads to an improvement of onset detection by about 8% in terms of F-measure, compared to the best single dimension.

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“…The MGD processed spectrum for the subband can be derived from the MGD using Eqs. (4) and (3a). The MGD processed subband spectra so obtained can be concatenated directly to get the overall fullband spectrum using the DFT symmetry as shown in Fig.…”

Section: Power Spectrum Estimation Based On Dcthwt and Mgdmentioning

confidence: 97%

“…Since the MGD removes the zeros close to the unit circle, even due to the associated white noise, its performance is valid even when the SNR is low. The MGD has been successfully applied for formant and pitch extraction of speech signals [4], spectral estimation of real and complex signals [8,18]. Further, the MGD has been applied to WVD and instantaneous power spectrum to remove the Gibbs ripple [5,10] and both the Gibbs ripple and ringing effect, respectively; preserving the frequency resolution of the rectangular window.…”

Section: Introductionmentioning

confidence: 99%

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Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

Narasimhan

Harish

Haripriya

et al. 2008

SIViP

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This paper proposes a new harmonic wavelet transform (HWT) based on discrete cosine transform (DCTHWT) and its application for signal or image compression and subband spectral estimation using modified group delay (MGD). Further, the existing DFTHWT has also been explored for image compression. The DCTHWT provides better quality decomposed decimated signals, which enable improved compression and MGD processing. For signal/ image compression, compared to the HWT based on DFT (DFTHWT), the DCTHWT reduces the reconstruction error. Compared to DFTHWT for the speech signal considered for a compression factor of 0.62, the DCTWHT provides a 30% reduction in reconstruction error. For an image, the DCTHWT algorithm due to its real nature, is computationally simple and more accurate than the DFTHWT. Further compared to Cohen-Daubechies-Feauveau 9/7 biorthogonal symmetric wavelet, the DCTHWT, with its computational advantage, gives a better or comparable performance. For an image with 6.25% coefficients, the reconstructed image by DFTHWT is significantly inferior in appearance to that by DCTHWT which is reflected in the error index as its values are 3.0 and 2.65%, respectively. For spectral estimation, DCTHWT reduces the bias both in frequency (frequency resolution) and spectral magnitude. The reduction in magnitude bias in turn improves the signal detectability. In DCTHWT, the improvement in frequency resolution and the signal detectability is not only due to good quality DCT subband signals but also due to their stretching (decimation) in the wavelet transform. The MGD reduces the variance while preserving the frequency resolution achieved by DCT and decimation. In view of these, the new spectral estimator facilitates a significant improvement both in magnitude and frequency bias, variance and signal detection ability; compared to those of MGD processing of both DFT and DCT fullband and DFT subband signals.

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Speech processing using group delay functions

Cited by 44 publications

References 4 publications

A uniform phase representation for the harmonic model in speech synthesis applications

A uniform phase representation for the harmonic model in speech synthesis applications

Three Dimensions of Pitched Instrument Onset Detection

Discrete cosine harmonic wavelet transform and its application to signal compression and subband spectral estimation using modified group delay

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