Speech enhancement using deep learning

This thesis explores the possibility to achieve enhancement on noisy speech signals using Deep Neural Networks. Signal enhancement is a classic problem in speech processing. In the last years, researches using deep learning has been used in many speech processing tasks since they have provided very satisfactory results. As a first step, a Signal Analysis Module has been implemented in order to calculate the magnitude and phase of each audio file in the database. The signal is represented into its magnitude and its phase, where the magnitude is modified by the neural network, and then it is reconstructed with the original phase. The implementation of the Neural Networks is divided into two stages.The first stage was the implementation of a Speech Activity Detection Deep Neural Network (SAD-DNN). The magnitude previously calculated, applied to the noisy data, will train the SAD-DNN in order to classify each frame in speech or non-speech. This classification is useful for the network that does the final cleaning. The Speech Activity Detection Deep Neural Network is followed by a Denoising Auto-Encoder (DAE). The magnitude and the label speech or non-speech will be the input of this second Deep Neural Network in charge of denoising the speech signal. The first stage is also optimized to be adequate for the final task in this second stage. In order to do the training, Neural Networks require datasets. In this project the Timit corpus [9] has been used as dataset for the clean voice (target) and the QUT-NOISE TIMIT corpus[4] as noisy dataset (source). Finally, Signal Synthesis Module reconstructs the clean speech signal from the enhanced magnitudes and the phase. In the end, the results provided by the system have been analysed using both objective and subjective measures.Esta tesis explora la posibilidad de conseguir mejorar señales de voz con ruido utilizando Redes Neuronales Profundas. La mejora de señales es un problema clásico del procesado de señal, pero recientemente se esta investigando con deep learning, ya que son técnicas que han dado resultados muy satisfactorios en muchas tareas del procesado de señal. Como primer paso, se ha implementado un Módulo de Análisis de Señal con el objetivo de extraer el módulo y fase de cada archivo de voz de la base de datos. La señal se representa en módulo y fase, donde el módulo se modifica con la red neuronal y posteriormente se reconstruye con la fase original. La implementación de la Red Neuronal consta de dos etapas. En la primera etapa se implementó una Red Neuronal de Detección de Actividad de Voz. El módulo previamente calculado, aplicado a los datos con ruido, se utiliza como entrada para entrenar esta red, de manera que se consigue clasificar cada trama en voz o no voz. Esta clasificación es útil para la red que se encarga de hacer la limpieza. A continuación de la Red Neuronal de Detección de Actividad de Voz se implementa otra, con el objetivo de eliminar el ruido. El módulo junto con la etiqueta obtenida en la red anterior serán la entrada de esta nueva red. En esta segunda etapa también se optimiza la primera para adaptarse a la tarea final. Las Redes Neuronales requieren bases de datos para el entrenamiento. En este proyecto se ha utilizado el Timit corpus [9] como base de datos de voz limpia (objetivo) y el QUT-NOISE TIMIT [4] como base de datos con ruido (fuente). A continuación, el Módulo de Síntesis de Señal reconstruye la señal de voz limpia a partir del módulo sin ruido y la fase original.Aquesta tesis explora la possibilitat d'aconseguir millorar senyals de veu amb soroll, utilitzant Xarxes Neuronals Profundes. La millora de senyals és un problema clàssic del processat de senyal, però recentment s'està investigant amb deep learning, ja que són tècniques que han donat resultats molt satisfactoris en moltes tasques de processament de veu. Com a primer pas, s'ha implementat un Mòdul d'Anàlisi de Senyal amb l'objectiu d'extreure el mòdul i la fase de cada arxiu d'àudio de la base de dades. El senyal es representa en mòdul i fase, on el mòdul es modifica amb la xarxa neuronal i posteriorment es reconstrueix amb la fase original. La implementació de les Xarxes Neuronals consta de dues etapes. En la primera etapa es va implementar una Xarxa Neuronal de Detecció d'Activitat de Veu. El mòdul prèviament calculat, aplicat a les dades amb soroll, s'utilitza com entrada per entrenar aquesta xarxa, de manera que s'aconsegueix classificar cada trama en veu o no veu. Aquesta classificació és útil per la xarxa que fa la neteja final. A continuació de la Xarxa Neuronal de Detecció d'Activitat de Veu s'implementa una altra amb l'objectiu d'eliminar el soroll. El mòdul, juntament amb la etiqueta obtinguda en la xarxa anterior, seran l'entrada d'aquesta nova xarxa. En aquesta segona etapa també s'optimitza la primera per adaptar-se a la tasca final. Les Xarxes Neuronals requereixen bases de dades per fer l'entrenament. En aquest projecte s'ha utilitzat el Timit corpus [9] com a base de dades de veu neta (objectiu) i el QUT-NOISE TIMIT[4] com a base de dades amb soroll (font). A continuació, el Mòdul de Síntesi de Senyal reconstrueix el senyal de veu net a partir del mòdul netejat i la fase original. Finalment, els resultats obtinguts del sistema van ser analitzats utilitzant mesures objectives i subjectives

Perception Driven Texture Generation

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.Comment: 7 pages, 4 figures, icme201

Speech vocoding for laboratory phonology

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications