Deep neural network techniques for monaural speech enhancement: state of the art analysis

Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and image generation. Due to their success, these data driven techniques have been applied in audio domain. More specifically, DNN models have been applied in speech enhancement domain to achieve denosing, dereverberation and multi-speaker separation in monaural speech enhancement. In this paper, we review some dominant DNN techniques being employed to achieve speech separation. The review looks at the whole pipeline of speech enhancement from feature extraction, how DNN based tools are modelling both global and local features of speech and model training (supervised and unsupervised). We also review the use of speech-enhancement pre-trained models to boost speech enhancement process. The review is geared towards covering the dominant trends with regards to DNN application in speech enhancement in speech obtained via a single speaker.Comment: conferenc

Deep learning for speech enhancement : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand

Speech enhancement, aiming at improving the intelligibility and overall perceptual quality of a contaminated speech signal, is an effective way to improve speech communications. In this thesis, we propose three novel deep learning methods to improve speech enhancement performance. Firstly, we propose an adversarial latent representation learning for latent space exploration of generative adversarial network based speech enhancement. Based on adversarial feature learning, this method employs an extra encoder to learn an inverse mapping from the generated data distribution to the latent space. The encoder establishes an inner connection with the generator and contributes to latent information learning. Secondly, we propose an adversarial multi-task learning with inverse mappings method for effective speech representation. This speech enhancement method focuses on enhancing the generator's capability of speech information capture and representation learning. To implement this method, two extra networks are developed to learn the inverse mappings from the generated distribution to the input data domains. Thirdly, we propose a self-supervised learning based phone-fortified method to improve specific speech characteristics learning for speech enhancement. This method explicitly imports phonetic characteristics into a deep complex convolutional network via a contrastive predictive coding model pre-trained with self-supervised learning. The experimental results demonstrate that the proposed methods outperform previous speech enhancement methods and achieve state-of-the-art performance in terms of speech intelligibility and overall perceptual quality

Speech Enhancement with Improved Deep Learning Methods

In real-world environments, speech signals are often corrupted by ambient noises during their acquisition, leading to degradation of quality and intelligibility of the speech for a listener. As one of the central topics in the speech processing area, speech enhancement aims to recover clean speech from such a noisy mixture. Many traditional speech enhancement methods designed based on statistical signal processing have been proposed and widely used in the past. However, the performance of these methods was limited and thus failed in sophisticated acoustic scenarios. Over the last decade, deep learning as a primary tool to develop data-driven information systems has led to revolutionary advances in speech enhancement. In this context, speech enhancement is treated as a supervised learning problem, which does not suffer from issues faced by traditional methods. This supervised learning problem has three main components: input features, learning machine, and training target. In this thesis, various deep learning architectures and methods are developed to deal with the current limitations of these three components. First, we propose a serial hybrid neural network model integrating a new low-complexity fully-convolutional convolutional neural network (CNN) and a long short-term memory (LSTM) network to estimate a phase-sensitive mask for speech enhancement. Instead of using traditional acoustic features as the input of the model, a CNN is employed to automatically extract sophisticated speech features that can maximize the performance of a model. Then, an LSTM network is chosen as the learning machine to model strong temporal dynamics of speech. The model is designed to take full advantage of the temporal dependencies and spectral correlations present in the input speech signal while keeping the model complexity low. Also, an attention technique is embedded to recalibrate the useful CNN-extracted features adaptively. Through extensive comparative experiments, we show that the proposed model significantly outperforms some known neural network-based speech enhancement methods in the presence of highly non-stationary noises, while it exhibits a relatively small number of model parameters compared to some commonly employed DNN-based methods. Most of the available approaches for speech enhancement using deep neural networks face a number of limitations: they do not exploit the information contained in the phase spectrum, while their high computational complexity and memory requirements make them unsuited for real-time applications. Hence, a new phase-aware composite deep neural network is proposed to address these challenges. Specifically, magnitude processing with spectral mask and phase reconstruction using phase derivative are proposed as key subtasks of the new network to simultaneously enhance the magnitude and phase spectra. Besides, the neural network is meticulously designed to take advantage of strong temporal and spectral dependencies of speech, while its components perform independently and in parallel to speed up the computation. The advantages of the proposed PACDNN model over some well-known DNN-based SE methods are demonstrated through extensive comparative experiments. Considering that some acoustic scenarios could be better handled using a number of low-complexity sub-DNNs, each specifically designed to perform a particular task, we propose another very low complexity and fully convolutional framework, performing speech enhancement in short-time modified discrete cosine transform (STMDCT) domain. This framework is made up of two main stages: classification and mapping. In the former stage, a CNN-based network is proposed to classify the input speech based on its utterance-level attributes, i.e., signal-to-noise ratio and gender. In the latter stage, four well-trained CNNs specialized for different specific and simple tasks transform the STMDCT of noisy input speech to the clean one. Since this framework is designed to perform in the STMDCT domain, there is no need to deal with the phase information, i.e., no phase-related computation is required. Moreover, the training target length is only one-half of those in the previous chapters, leading to lower computational complexity and less demand for the mapping CNNs. Although there are multiple branches in the model, only one of the expert CNNs is active for each time, i.e., the computational burden is related only to a single branch at anytime. Also, the mapping CNNs are fully convolutional, and their computations are performed in parallel, thus reducing the computational time. Moreover, this proposed framework reduces the latency by %55 compared to the models in the previous chapters. Through extensive experimental studies, it is shown that the MBSE framework not only gives a superior speech enhancement performance but also has a lower complexity compared to some existing deep learning-based methods

Deep Learning for Distant Speech Recognition

Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.Comment: PhD Thesis Unitn, 201

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Stabilizing and Enhancing Learning for Deep Complex and Real Neural Networks

Dans cette thèse nous proposons un ensemble de contributions originales sous la forme de trois articles relatifs aux réseaux de neurones profonds réels et complexes. Nous abordons à la fois des problèmes théoriques et pratiques liés à leur apprentissage. Les trois articles traitent des méthodes conçues pour apporter des solutions aux problèmes de l’instabilité observée au cours de l’entrainement des réseaux, notamment le problème notoire de dilution et d’explosion des gradients ou «vanishing and exploding gradients » lors de l’entrainement des réseaux de neurones profonds. Nous proposons dans un premier temps la conception de modules d’entrainement appropriés, désignés par «building blocks», pour les réseaux de neurones profonds à valeurs complexes. Notre proposition comporte des méthodes d’initialisation et de normalisation ainsi que des fonctions d’activation des unités neuronales. Les modules conçus sont par la suite utilisés pour la spécification d’architectures profondes à valeurs complexes dédiées à accomplir diverses tâches. Ceci comprend des tâches de vision par ordinateur, de transcription musicale, de prédiction du spectre de la parole, d’extraction des signaux et de séparation des sources audio. Finalement nous procédons à une analyse détaillée de l’utilité de l’hypothèse contraignante d’orthogonalité généralement adoptée pour le paramétrage de la matrice de transition à travers les couches des réseaux de neurones réels récurrents.----------ABSTRACT : This thesis presents a set of original contributions in the form of three chapters on real and complex-valued deep neural networks. We address both theoretical issues and practical challenges related to the training of both real and complex-valued neural networks. First, we investigate the design of appropriate building blocks for deep complex-valued neural networks, such as initialization methods, normalization techniques and elementwise activation functions. We apply our theoretical insights to design building blocks for the construction of deep complex-valued architectures. We use them to perform various tasks in computer vision, music transcription, speech spectrum prediction, signal retrieval and audio source separation. We also perform an analysis of the usefulness of orthogonality for the hidden transition matrix in a real-valued recurrent neural network. Each of the three chapters are dedicated to dealing with methods designed to provide solutions to problems causing training instability, among them, the notorious problem of vanishing and exploding gradients during the training of deep neural networks. Throughout this manuscript we show the usefulness of the methods we propose in the context of well known challenges and clearly identifiable objectives. We provide below a summary of the contributions within each chapter. At present, the vast majority of building blocks, techniques, and architectures for training deep neural networks are based on real-valued computations and representations. However, representations based on complex numbers have started to receive increased attention. Despite their compelling properties complex-valued deep neural networks have been neglected due in part to the absence of the building blocks required to design and train this type of network. The lack of such a framework represents a noticeable gap in deep learning tooling