According to the Parkinson’s Foundation, more than 10 million people world- wide suffer from Parkinson’s disease (PD). The common symptoms are tremor, muscle rigidity and slowness of movement. There is no cure available cur- rently, but clinical intervention can help alleviate the symptoms significantly. Recently, it has been found that PD can be detected and telemonitored by voice signals, such as sustained phonation /a/. However, the voiced-based PD detector suffers from severe performance degradation in adverse envi- ronments, such as noise, reverberation and nonlinear distortion, which are common in uncontrolled settings. In this thesis, we focus on deriving speech modeling and robust estima- tion algorithms capable of improving the PD detection accuracy in adverse environments. Robust estimation algorithms using parametric modeling of voice signals are proposed. We present both segment-wise and sample-wise robust pitch tracking algorithms using the harmonic model. ...

From artificial voices in GPS to automatic systems of dictation, from voice-based identity verification to voice pathology detection, speech processing applications are nowadays omnipresent in our daily life. By offering solutions to companies seeking for efficiency enhancement with simultaneous cost saving, the market of speech technology is forecast to be especially promising in the next years. The present thesis deals with advances in glottal analysis in order to incorporate new techniques within speech processing applications. While current systems are usually based on information related to the vocal tract configuration, the airflow passing through the vocal folds, and called glottal flow, is expected to exhibit a relevant complementarity. Unfortunately, glottal analysis from speech recordings requires specific complex processing operations, which explains why it has been generally avoided. The main goal of this thesis is to provide new advances in glottal analysis ...

Audio systems receive the speech signals of interest usually in the presence of noise. The noise has profound impacts on the quality and intelligibility of the speech signals, and it is therefore clear that the noisy signals must be cleaned up before being played back, stored, or analyzed. We can estimate the speech signal of interest from the noisy signals using a priori knowledge about it. A human speech signal is broadband and consists of both voiced and unvoiced parts. The voiced part is quasi-periodic with a time-varying fundamental frequency (or pitch as it is commonly referred to). We consider the periodic signals basically as the sum of harmonics. Therefore, we can pass the noisy signals through bandpass filters centered at the frequencies of the harmonics to enhance the signal. In addition, although the frequencies of the harmonics are the ...

Speech synthesis technology plays an important role in many aspects of man–machine interaction, particularly in telephony applications. In order to be widely accepted, the synthesised speech quality should be as human–like as possible. This thesis investigates novel techniques for the speech signal generation stage in a speech synthesiser, based on concepts from nonlinear dynamical theory. It focuses on natural–sounding synthesis for voiced speech, coupled with the ability to generate the sound at the required pitch. The one–dimensional voiced speech time–domain signals are embedded into an appropriate higher dimensional space, using Takens’ method of delays. These reconstructed state space representations have approximately the same dynamical properties as the original speech generating system and are thus effective models. A new technique for marking epoch points in voiced speech that operates in the state space domain is proposed. Using the fact that one ...

The topic of this thesis is speech enhancement with a focus on models of voiced speech. Speech is divided into two subcategories dependent on the characteristics of the signal. One part is the voiced speech, the other is the unvoiced. In this thesis, we primarily focus on the voiced speech parts and utilise the structure of the signal in relation to speech enhancement. The basis for the models is the harmonic model which is a very often used model for voiced speech because it describes periodic signals perfectly. First, we consider the problem of non-stationarity in the speech signal. The speech signal changes its characteristics continuously over time whereas most speech analysis and enhancement methods assume stationarity within 20-30 ms. We propose to change the model to allow the fundamental frequency to vary linearly over time by introducing a chirp ...

This thesis examines sparse statistical modeling on a range of applications in audio modeling, audio localizations, DNA sequencing, and spectroscopy. In the examined cases, the resulting estimation problems are computationally cumbersome, both as one often suffers from a lack of model order knowledge for this form of problems, but also due to the high dimensionality of the parameter spaces, which typically also yield optimization problems with numerous local minima. In this thesis, these problems are treated using sparse modeling heuristics, with the resulting criteria being solved using convex relaxations, inspired from disciplined convex programming ideas, to maintain tractability. The contributions to audio modeling and estimation focus on the estimation of the fundamental frequency of harmonically related sinusoidal signals, which is commonly used model for, e.g., voiced speech or tonal audio. We examine both the problems of estimating multiple audio sources ...

Cochlear implants (CI) are surgically implanted hearing aids that provide auditory sensations to deaf people through direct electrical stimulation of the auditory nerve. Although relatively good speech understanding can be achieved by implanted subjects, pitch perception by CI subjects is about 50 times worse than observed for normal-hearing (NH) persons. Pitch is, however, important for intonation, music, speech understanding in tonal languages, and for separating multiple simultaneous sound sources. The major goal of this work is to improve pitch perception by CI subjects. In CI subjects two fundamental mechanisms are used for pitch perception: place pitch and temporal pitch. Our results show that place pitch is correlated to the sound¢s brightness because place pitch sensation is related to the centroid of the excitation pattern along the cochlea. The slopes of the excitation pattern determine place pitch sensitivity. Our results also ...

This thesis addresses the development of a system for pitch-informed solo and accompaniment separation capable of separating main instruments from music accompaniment regardless of the musical genre of the track, or type of music accompaniment. For the solo instrument, only pitched monophonic instruments were considered in a single-channel scenario where no panning or spatial location information is available. In the proposed method, pitch information is used as an initial stage of a sinusoidal modeling approach that attempts to estimate the spectral information of the solo instrument from a given audio mixture. Instead of estimating the solo instrument on a frame by frame basis, the proposed method gathers information of tone objects to perform separation. Tone-based processing allowed the inclusion of novel processing stages for attack re nement, transient interference reduction, common amplitude modulation (CAM) of tone objects, and for better ...

We present new results on single-channel speech separation and suggest a new separation approach to improve the speech quality of separated signals from an observed mix- ture. The key idea is to derive a mixture estimator based on sinusoidal parameters. The proposed estimator is aimed at finding sinusoidal parameters in the form of codevectors from vector quantization (VQ) codebooks pre-trained for speakers that, when combined, best fit the observed mixed signal. The selected codevectors are then used to reconstruct the recovered signals for the speakers in the mixture. Compared to the log- max mixture estimator used in binary masks and the Wiener filtering approach, it is observed that the proposed method achieves an acceptable perceptual speech quality with less cross- talk at different signal-to-signal ratios. Moreover, the method is independent of pitch estimates and reduces the computational complexity of the ...

Music is built from sound, ultimately resulting from an elaborate interaction between the sound-generating properties of physical objects (i.e. music instruments) and the sound perception abilities of the human auditory system. Humans, even without any kind of formal music training, are typically able to ex- tract, almost unconsciously, a great amount of relevant information from a musical signal. Features such as the beat of a musical piece, the main melody of a complex musical ar- rangement, the sound sources and events occurring in a complex musical mixture, the song structure (e.g. verse, chorus, bridge) and the musical genre of a piece, are just some examples of the level of knowledge that a naive listener is commonly able to extract just from listening to a musical piece. In order to do so, the human auditory system uses a variety of cues ...

Sound source separation refers to the task of estimating the signals produced by individual sound sources from a complex acoustic mixture. It has several applications, since monophonic signals can be processed more efficiently and flexibly than polyphonic mixtures. This thesis deals with the separation of monaural, or, one-channel music recordings. We concentrate on separation methods, where the sources to be separated are not known beforehand. Instead, the separation is enabled by utilizing the common properties of real-world sound sources, which are their continuity, sparseness, and repetition in time and frequency, and their harmonic spectral structures. One of the separation approaches taken here use unsupervised learning and the other uses model-based inference based on sinusoidal modeling. Most of the existing unsupervised separation algorithms are based on a linear instantaneous signal model, where each frame of the input mixture signal is modeled ...

In this thesis we examine the autonomous oscillator model for synthesis of speech signals. The contributions comprise an analysis of realizations and training methods for the nonlinear function used in the oscillator model, the combination of the oscillator model with inverse filtering, both significantly increasing the number of `successfully' re-synthesized speech signals, and the introduction of a new technique suitable for the re-generation of the noise-like signal component in speech signals. Nonlinear function models are compared in a one-dimensional modeling task regarding their presupposition for adequate re-synthesis of speech signals, in particular considering stability. The considerations also comprise the structure of the nonlinear functions, with the aspect of the possible interpolation between models for different speech sounds. Both regarding stability of the oscillator and the premiss of a nonlinear function structure that may be pre-defined, RBF networks are found a ...

This thesis presents methods to enhance the speech of patients with voice disorders or with substitution voices. The first method enhances speech of patients with laryngeal neoplasm. The enhancement enables a reduction of pitch and a strengthening of the harmonics of voiced segments as well as decreasing the perceived speaking effort. The need for reliable pitch mark determination on disordered and substitution voices led to the implementation of a state-space based algorithm. Its performance is comparable to a state-of-the art pitch detection algorithm but does not require post processing. A subsequent part of the thesis deals with alaryngeal speech, with a focus on Electro-Larynx (EL) speech. After investigating an EL speech production model, which takes into account the common source of the speech signal and the directly radiated EL (DREL) sound, a solution to suppress the direct sound is based ...

This thesis deals with developing improved modeling methods for speech and audio processing based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the well-known linear prediction (LP) based all-pole models currently applied in many modern speech and audio processing systems. In the first part of this thesis, we introduce \emph{Sparse Linear Prediction}, a set of speech processing tools created by introducing sparsity constraints into the LP framework. This approach defines predictors that look for a sparse residual rather than a minimum variance one, with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter is model as an impulse train. Introducing sparsity in the LP framework, will also bring to develop the ...

Nowadays, speech synthesis is part of various daily life applications. The ultimate goal of such technologies consists in extending the possibilities of interaction with the machine, in order to get closer to human-like communications. However, current state-of-the-art systems often lack of realism: although high-quality speech synthesis can be produced by many researchers and companies around the world, synthetic voices are generally perceived as hyperarticulated. In any case, their degree of articulation is fixed once and for all. The present thesis falls within the more general quest for enriching expressivity in speech synthesis. The main idea consists in improving statistical parametric speech synthesis, whose most famous example is Hidden Markov Model (HMM) based speech synthesis, by introducing a control of the articulation degree, so as to enable synthesizers to automatically adapt their way of speaking to the contextual situation, like humans ...