Time-Frequency Coherence for Periodic-Aperiodic Decomposition of Speech Signals

Vijayan, Karthika; Dhiman, Jitendra Kumar; Seelamantula, Chandra Sekhar

doi:10.21437/interspeech.2017-726

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Barszcz [5] analyzed the theory of adaptive filters and investigated the possibility of decomposing vibration signals into deterministic and non-deterministic components based on the Cramer-Wold theorem [9]. As a result, non-deterministic components of the vibration signal were detected, indicating bearing support failure in the wind turbine.…”

Section: Introductionmentioning

confidence: 99%

Signal Decomposition for Monitoring Systems of Processes

Pavlenko,

Trojanowska,

Ivanov

et al. 2024

Processes

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This article is devoted to the problem of signal decomposition into periodic and aperiodic components. According to the proposed approach, there is no need to evaluate the aperiodic component as a difference between the total signal of its periodic components. This research aims to create a general analytical approach that combines the Fourier and Maclaurin series methodologies into a single comprehensive series. As a result, analytical expressions for determining deposition coefficients were established for an aperiodic signal with a monoharmonic overlay. Recurrence relations were established to determine the coefficients of this series. These relations allow direct integrations of the obtained values of integrals to be avoided. The evaluated numerical values of the coefficients are also presented graphically and tabulated. It was proven that the values of these coefficients are universal numbers since they do not depend on the period/frequency of oscillations. The reliability of the proposed approach was confirmed by the fact that the evaluated coefficients are equal to the Fourier series coefficients in the case of a periodic signal. Also, for an aperiodic signal, these coefficients were reduced to the coefficients of the Maclaurin series. The usability of the proposed generalized analytical approach for signal decomposition is for control and monitoring systems of processes.

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Section: Introductionmentioning

confidence: 99%

Signal Decomposition for Monitoring Systems of Processes

Pavlenko,

Trojanowska,

Ivanov

et al. 2024

Processes

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“…Our technique utilizes the complex Riesz transform [14] for accurate estimation of 2-D amplitude and frequency modulations. In our previous work, we have shown the importance of the frequency modulation (FM) for some of the fundamental speech processing tasks such as pitch estimation [15] and periodic/aperiodic speech decomposition of a speech signal [16]. While the FM component characterizes the source excitation attributes in 2-D, the amplitude modulation (AM) can effectively model the vocal-tract-filter magnitude response.…”

Section: Introductionmentioning

confidence: 99%

On the Suitability of the Riesz Spectro-Temporal Envelope for WaveNet Based Speech Synthesis

2019

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We address the problem of estimating the time-varying spectral envelope of a speech signal using a spectro-temporal demodulation technique. Unlike the conventional spectrogram, we consider a pitch-adaptive spectrogram and model a spectrotemporal patch using an amplitude-and frequency-modulated two-dimensional (2-D) cosine signal. We employ a demodulation technique based on the Riesz transform that we proposed recently to estimate the amplitude and frequency modulations. The amplitude modulation (AM) corresponds to the vocal-tract filter magnitude response (or envelope) and the frequency modulation (FM) corresponds to the excitation. We consider the AM and demonstrate its effectiveness by incorporating it as an acoustic feature for local conditioning in the statistical WaveNet vocoder for the task of speech synthesis. The quality of the synthesized speech obtained with the Riesz envelope is compared with that obtained using the envelope estimated by the WORLD vocoder. Objective measures and subjective listening tests on the CMU-Arctic database show that the quality of synthesis is superior to that obtained using the WORLD envelope. This study thus establishes the Riesz envelope as an efficient alternative to the WORLD envelope.

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“…This requires the design of an accurate 2-D AM and FM estimation technique, and the complex Riesz transform [13] based demodulation approach was used for achieving this goal. The generalized model in [8] gives a 2 to 4 dB benefit in reconstruction SNR in comparison with [6], and has also been used for pitch estimation [14] and periodic/aperiodic separation of speech [15]. In this paper, we further analyze the 2-D AM-FM model in [8] and make two contributions.…”

Section: Introductionmentioning

confidence: 99%

Multicomponent 2-D AM-FM Modeling of Speech Spectrograms

2018

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In contrast to 1-D short-time analysis of speech, 2-D modeling of spectrograms provides a characterization of speech attributes directly in the joint time-frequency plane. Building on existing 2-D models to analyze a spectrogram patch, we propose a multicomponent 2-D AM-FM representation for spectrogram decomposition. The components of the proposed representation comprise a DC, a fundamental frequency carrier and its harmonics, and a spectrotemporal envelope, all in 2-D. The number of harmonics required is patch-dependent. The estimation of the AM and FM is done using the Riesz transform, and the component weights are estimated using a least-squares approach. The proposed representation provides an improvement over existing state-of-the-art approaches, for both male and female speakers. This is quantified using reconstruction SNR and perceptual evaluation of speech quality (PESQ) metric. Further, we perform an overlap-add on the DC component, pooling all the patches and obtain a time-frequency (t-f) aperiodicity map for the speech signal. We verify its effectiveness in improving speech synthesis quality by using it in an existing state-of-theart vocoder.

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Time-Frequency Coherence for Periodic-Aperiodic Decomposition of Speech Signals

Cited by 5 publications

References 26 publications

Signal Decomposition for Monitoring Systems of Processes

Signal Decomposition for Monitoring Systems of Processes

On the Suitability of the Riesz Spectro-Temporal Envelope for WaveNet Based Speech Synthesis

Multicomponent 2-D AM-FM Modeling of Speech Spectrograms

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