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

Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.Comment: submitted to IWAENC 201

SNR-Based Teachers-Student Technique for Speech Enhancement

It is very challenging for speech enhancement methods to achieves robust performance under both high signal-to-noise ratio (SNR) and low SNR simultaneously. In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. Specifically, this method consists of multiple teacher models and a student model. We first train the teacher models under multiple small-range SNRs that do not coincide with each other so that they can perform speech enhancement well within the specific SNR range. Then, we choose different teacher models to supervise the training of the student model according to the SNR of the training data. Eventually, the student model can perform speech enhancement under both high SNR and low SNR. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset. We experimentally analyzed the effectiveness of the SNR-based teachers-student technique and compared the proposed method with several state-of-the-art methods.Comment: Published in 2020 IEEE International Conference on Multimedia and Expo (ICME 2020

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Raw Multi-Channel Audio Source Separation using Multi-Resolution Convolutional Auto-Encoders

Supervised multi-channel audio source separation requires extracting useful spectral, temporal, and spatial features from the mixed signals. The success of many existing systems is therefore largely dependent on the choice of features used for training. In this work, we introduce a novel multi-channel, multi-resolution convolutional auto-encoder neural network that works on raw time-domain signals to determine appropriate multi-resolution features for separating the singing-voice from stereo music. Our experimental results show that the proposed method can achieve multi-channel audio source separation without the need for hand-crafted features or any pre- or post-processing

A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation

The objective of deep learning methods based on encoder-decoder architectures for music source separation is to approximate either ideal time-frequency masks or spectral representations of the target music source(s). The spectral representations are then used to derive time-frequency masks. In this work we introduce a method to directly learn time-frequency masks from an observed mixture magnitude spectrum. We employ recurrent neural networks and train them using prior knowledge only for the magnitude spectrum of the target source. To assess the performance of the proposed method, we focus on the task of singing voice separation. The results from an objective evaluation show that our proposed method provides comparable results to deep learning based methods which operate over complicated signal representations. Compared to previous methods that approximate time-frequency masks, our method has increased performance of signal to distortion ratio by an average of 3.8 dB