Multiple pitch tracking of speech is an important task for the segregation of multiple speakers in a single-channel recording. In this thesis, a probabilistic model-based approach for estimation and tracking of multiple pitch trajectories is proposed. A probabilistic model that captures pitch-dependent characteristics of the single-speaker short-time spectrum is obtained a priori from clean speech data. The resulting speaker model, which is based on Gaussian mixture models, can be trained either in a speaker independent (SI) or a speaker dependent (SD) fashion. Speaker models are then combined using an interaction model to obtain a probabilistic description of the observed speech mixture. A factorial hidden Markov model is applied for tracking the pitch trajectories of multiple speakers over time. The probabilistic model-based approach is capable to explicitly incorporate timbral information and all associated uncertainties of spectral structure into the model. While ...

Single-channel speech enhancement refers to the reduction of noise signal components in a single-channel signal composed of both speech and noise. Spectral speech enhancement methods are among the most popular approaches to solving this problem. Since the short-time spectral amplitude has been identified as a highly perceptually relevant quantity, most conventional approaches rely on processing the amplitude spectrum only, ignoring any information that may be contained in the spectral phase. As a consequence, the noisy short-time spectral phase is neither enhanced for the purpose of signal reconstruction nor is it used for refining short-time spectral amplitude estimates. This thesis investigates the use of the spectral phase and its structure in algorithms for single-channel speech enhancement. This includes the analysis of the spectral phase in the context of theoretically optimal speech estimators. The resulting knowledge is exploited in formulating single-channel speech ...

Reducing interference noise in a noisy speech recording has been a challenging task for many years yet has a variety of applications, for example, in handsfree mobile communications, in speech recognition, and in hearing aids. Traditional single-channel noise reduction schemes, such as Wiener filtering, do not work satisfactorily in the presence of non-stationary background noise. Alternatively, supervised approaches, where the noise type is known in advance, lead to higher-quality enhanced speech signals. This dissertation proposes supervised and unsupervised single-channel noise reduction algorithms. We consider two classes of methods for this purpose: approaches based on nonnegative matrix factorization (NMF) and methods based on hidden Markov models (HMM). The contributions of this dissertation can be divided into three main (overlapping) parts. First, we propose NMF-based enhancement approaches that use temporal dependencies of the speech signals. In a standard NMF, the important temporal ...

Speech signals carry important information about a speaker such as age, gender, language, accent and emotional/psychological state. Automatic recognition of speaker characteristics has a wide range of commercial, medical and forensic applications such as interactive voice response systems, service customization, natural human-machine interaction, recognizing the type of pathology of speakers, and directing the forensic investigation process. This research aims to develop accurate methods and tools to identify different physical characteristics of the speakers. Due to the lack of required databases, among all characteristics of speakers, our experiments cover gender recognition, age estimation, language recognition and accent/dialect identification. However, similar approaches and techniques can be applied to identify other characteristics such as emotional/psychological state. For speaker characterization, we first convert variable-duration speech signals into fixed-dimensional vectors suitable for classification/regression algorithms. This is performed by fitting a probability density function to acoustic ...

In many speech applications such as hands-free telephony or voice-controlled home assistants, the distance between the user and the recording microphones can be relatively large. In such a far-field scenario, the recorded microphone signals are typically corrupted by noise and reverberation, which may severely degrade the performance of speech recognition systems and reduce intelligibility and quality of speech in communication applications. In order to limit these effects, speech enhancement algorithms are typically applied. The main objective of this thesis is to develop novel speech enhancement algorithms for noisy and reverberant environments and signal-based measures to evaluate these algorithms, focusing on solutions that are applicable in realistic scenarios. First, we propose a single-channel speech enhancement algorithm for joint noise and reverberation reduction. The proposed algorithm uses a spectral gain to enhance the input signal, where the gain is computed using a ...

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 ...

The topic of this thesis is the enhancement of noisy, periodic signals with application to speech signals. Generally speaking, enhancement methods can be divided into signal- and noise-driven methods. In this thesis, we focus on the signal-driven approach by employing relevant signal parameters for the enhancement of periodic signals. The enhancement problem consists of two major subproblems: the estimation of relevant parameters or statistics, and the actual noise reduction of the observed signal. We consider both of these subproblems. First, we consider the problem of estimating signal parameters relevant to the enhancement of periodic signals. The fundamental frequency is one example of such a parameter. Furthermore, in multichannel scenarios, the direction-of-arrival of the periodic sources onto an array of sensors is another parameter of relevance. We propose methods for the estimation of the fundamental frequency that have benefits compared to ...

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 ...

A large part of human speech communication takes place in noisy environments and is supported by technical devices. For example, a hearing-impaired person might use a hearing aid to take part in a conversation in a busy restaurant. These devices, but also telecommunication in noisy environments or voiced-controlled assistants, make use of speech enhancement and separation algorithms that improve the quality and intelligibility of speech by separating speakers and suppressing background noise as well as other unwanted effects such as reverberation. If the devices are equipped with more than one microphone, which is very common nowadays, then multi-channel speech enhancement approaches can leverage spatial information in addition to single-channel tempo-spectral information to perform the task. Traditionally, linear spatial filters, so-called beamformers, have been employed to suppress the signal components from other than the target direction and thereby enhance the desired ...

The improvement of speech intelligibility is a traditional problem which still remains open and unsolved. The recent boom of applications such as hands-free communi- cations or automatic speech recognition systems and the ever-increasing demands of the hearing-impaired community have given a definitive impulse to the research in this area. This PhD thesis is focused on speech enhancement for audiological applications. Most of the research conducted in this thesis has been focused on the improvement of speech intelligibility in hearing aids, considering the variety of restrictions and limitations imposed by this type of devices. The combination of source separation techniques and spatial filtering with machine learning and evolutionary computation has originated novel and interesting algorithms which are included in this thesis. The thesis is divided in two main parts. The first one contains a preliminary study of the problem and a ...

In a natural acoustic environment, multiple sources are usually active at the same time. The task of source separation is the estimation of individual source signals from this complex mixture. The challenge of single channel source separation (SCSS) is to recover more than one source from a single observation. Basically, SCSS can be divided in methods that try to mimic the human auditory system and model-based methods, which find a probabilistic representation of the individual sources and employ this prior knowledge for inference. This thesis presents several strategies for the separation of two speech utterances mixed into a single channel and is structured in four parts: The first part reviews factorial models in model-based SCSS and introduces the soft-binary mask for signal reconstruction. This mask shows improved performance compared to the soft and the binary masks in automatic speech recognition ...

Single channel source separation is a quite recent problem of constantly growing interest in the scientific world. However, this problem is still very far to be solved, and even more, it cannot be solved in all its generality. Indeed, since this problem is highly underdetermined, the main difficulty is that a very strong knowledge about the sources is required to be able to separate them. For a grand class of existing separation methods, this knowledge is expressed by statistical source models, notably Gaussian Mixture Models (GMM), which are learned from some training examples. The subject of this work is to study the separation methods based on statistical models in general, and then to apply them to the particular problem of separating singing voice from background music in mono recordings of songs. It can be very useful to propose some satisfactory ...

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 ...

Despite a lot of progress in speech separation, enhancement, and automatic speech recognition realistic meeting recognition is still fairly unsolved. Most research on speech separation either focuses on spectral cues to address single-channel recordings or spatial cues to separate multi-channel recordings and exclusively either rely on neural networks or probabilistic graphical models. Integrating a spatial clustering approach and a deep learning approach using spectral cues in a single framework can significantly improve automatic speech recognition performance and improve generalizability given that a neural network profits from a vast amount of training data while the probabilistic counterpart adapts to the current scene. This thesis at hand, therefore, concentrates on the integration of two fairly disjoint research streams, namely single-channel deep learning-based source separation and multi-channel probabilistic model-based source separation. It provides a general framework to integrate spatial and spectral cues in ...

The research in this thesis addresses the problem of automatic speech recognition in noisy environments. Automatic speech recognition systems obtain acceptable performances in noise free conditions but these performances degrade dramatically in presence of additive noise. This is mainly due to the mismatch between the training and the noisy operating conditions. In the time-frequency representation of the noisy speech signal, some of the clean speech features are masked by noise. In this case the clean speech features cannot be correctly estimated from the noisy speech and therefore they are considered as missing or unreliable. In order to improve the performance of speech recognition systems in additive noise conditions, special attention should be paid to the problems of detection and compensation of these unreliable features. This thesis is concerned with the problem of missing features applied to automatic speaker-independent speech recognition. ...