Wrapped Gaussian Mixture Models for Modeling and High-Rate Quantization of Phase Data of Speech

Agiomyrgiannakis, Yannis; Stylianou, Yannis

doi:10.1109/tasl.2008.2008229

Cited by 46 publications

(32 citation statements)

References 29 publications

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“…In order to represent the instantaneous phase parameter φ i,h , models have been already suggested for phase synchronisation between frames [45,46] and speech coding [14,47]. In this work, we suggest to represent the measured φ i,h using a model similar to that in [47]:…”

Section: Theoretical Model Of the Instantaneous Phasementioning

confidence: 99%

“…In order to estimate the mean and standard deviation, we assume that the distribution of PD i ( f ) obeys a normal distribution. Moreover, since PD i ( f ) is a circular data defined in (−π, π], we make use of the wrapped normal distribution [47,64].…”

Section: Statistical Features Of the Phase Distortionmentioning

confidence: 99%

“…Since the values PD i ( f ) are circular data, the wrapped normal distribution [47,64] has been used in this work to model PD i ( f ) over a few periods. The mean is estimated with [64]:…”

Section: The Short-term Mean Of Pdmentioning

confidence: 99%

“…For this purpose, we suggest to follow the phase model Equation 6 while using the short-term mean and standard deviation of the phase distortion in order to resynthesis a source shape. First, at each instantt i , we synthesize a phase distortionPD i,h using the wrapped normal distribution [47,64]:…”

Section: X[n] ⇔ X(t))mentioning

confidence: 99%

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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: Theoretical Model Of the Instantaneous Phasementioning

confidence: 99%

Section: Statistical Features Of the Phase Distortionmentioning

confidence: 99%

“…Since the values PD i ( f ) are circular data, the wrapped normal distribution [47,64] has been used in this work to model PD i ( f ) over a few periods. The mean is estimated with [64]:…”

Section: The Short-term Mean Of Pdmentioning

confidence: 99%

Section: X[n] ⇔ X(t))mentioning

confidence: 99%

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

Degottex

Erro

2014

J AUDIO SPEECH MUSIC PROC.

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“…This creates complications in the Gaussian approximations inherent in both linearization-based and Rao-Blackwellized filters, because distributions such as the Gaussian are not suitable for circular estimation; other approaches exist (Agiomyrgiannakis and Stylianou, 2009;Lo, 1977;Tidefelt and Schön, 2009), but they are difficult to apply with Rao-Blackwellization.…”

Section: Instant Phase Model With Multiple Phase Statesmentioning

confidence: 99%

Frequency Tracking for Speed Estimation

Lindfors¹

2018

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Estimating the frequency of a periodic signal, or tracking the time-varying frequency of an almost periodic signal, is an important problem that is well studied in literature. This thesis focuses on two subproblems where contributions can be made to the existing theory: frequency tracking methods and measurements containing outliers.Maximum-likelihood-based frequency estimation methods are studied, focusing on methods which can handle outliers in the measurements. Katkovnik's frequency estimation method is generalized to real and harmonic signals, and a new method based on expectation-maximization is proposed. The methods are compared in a simulation study in which the measurements contain outliers. The proposed methods are compared with the standard periodogram method.Recursive Bayesian methods for frequency tracking are studied, focusing on the Rao-Blackwellized point mass filter (rbpmf). Two reformulations of the rbpmf aiming to reduce computational costs are proposed. Furthermore, the technique of variational approximate Rao-Blackwellization is proposed, which allows usage of a Student's t distributed measurement noise model. This enables recursive frequency tracking methods to handle outliers using heavy-tailed noise models in Rao-Blackwellized filters such as the rbpmf. A simulation study illustrates the performance of the methods when outliers occur in the measurement noise.The framework above is applied to and studied in detail in two applications. The first application is on frequency tracking of engine sound. Microphone measurements are used to track the frequency of Doppler-shifted variants of the engine sound of a vehicle moving through an area. These estimates can be used to compute the speed of the vehicle. Periodogram-based methods and the rbpmf are evaluated on simulated and experimental data. The results indicate that the rbpmf has lower rmse than periodogram-based methods when tracking fast changes in the frequency.The second application relates to frequency tracking of wheel vibrations, where a car has been equipped with an accelerometer. The accelerometer measurements are used to track the frequency of the wheel axle vibrations, which relates to the wheel rotational speed. The velocity of the vehicle can then be estimated without any other sensors and without requiring integration of the accelerometer measurements. In situations with high signal-to-noise ratio (snr), the methods perform well. To remedy situations when the methods perform poorly, an accelerometer input is introduced to the formulation. This input is used to predict changes in the frequency for short time intervals.v Populärvetenskaplig sammanfattningPeriodiska signaler förekommer ofta i praktiken. I många tillämpningar är det intressant att försöka skatta frekvensen av dessa periodiska signaler, eller vibrationer, genom mätningar av dem. Detta kallas för frekvensskattning eller frekvensföljning beroende på om frekvensen är konstant eller varierar över tid. Två tillämpningar studeras i denna licentiatavhandling. Målet i...

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Introduction: Phase Processing, History

Mowlaee

2016

Single Channel Phase‐Aware Signal Processing in Speech Communication: Theory and Practice

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