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

PUBLIC INTERNAL AUDIT LAW - A NEW BRANCH OF LAW IN ROMANIA

In Romania, the introduction of internal audit is relatively recent fall in the overall effort to modernize financial management in both private and public sectors. Also, note that the introduction of public sector internal audit is not transposed in the Romanian economy features of the acquis communitarian, for the simple reason that there is no internal audit. At the same time, there is no legal framework and procedures to adapt to one of our member countries, since it does not correspond to current concepts of internal auditing and internal controls.Internal audit, public law, management control systems

Health Status Improved by Aronia Melanocarpa Polyphenolic Extract

This chapter focuses on certain natural polyphenolic extracts from Aronia melanocarpa (Michx.) Elliott and also on their effects in insulin-dependent diabetes mellitus. The phenolic profile of berries ethanolic extract was characterized by HPLC/DAD/ESI-MS. HPLC/DAD/ESI-MS allowed identification of five phenolic compounds: chlorogenic acid, kuromanin, rutin, hyperoside, and quercetin. The results reveal that the glycosylated hemoglobin values are much higher in the diabetic group (DM) and they are significantly lower in the group protected by polyphenols (DM+P). It is found that due to the polyphenolic protection of the rats from the DM+P, the atherogen risk is preserved at normal limits. The serous activity of glutathione-peroxidase (GSH-Px) and superoxide-dismutase (SOD) has significantly lower values in the diabetic group as compared to the group protected by polyphenols. Renal function indicators like creatinine and blood-urea nitrogen (BUN) were also elevated in the streptozotocin diabetic rats when compared with control rats. When compared with the diabetic group the elevated levels of BUN was significantly (p < 0.001) reduced in animals treated with natural polyphenols. Through the hypoglycemiant, hypolipemiant, and antioxidant effects, A. melanocarpa represents a possible dietary adjunct for the treatment of diabetes and a potential source of active agents for the prevention of microvascular diabetes complications

Measuring Investment Efficiency in Public Education - Some Cross-Country Comparative Results

This paper presents some cross-country comparative measures of efficiency in public education, using existing data for European countries. It also presents an overview of different approaches to the question of efficiency of investment in public education and the measurement thereof. Although non-parametric methods have been used in some studies on investment efficiency in the education sector, the approach adopted here extends previous research by conducting the analysis combining the data from different sources. The variables used in the empirical analysis are constructed from different databases (the joint Unesco-OECD-Eurostat data collection, IEA or OECD’s databases) which are all designed so that data is comparable across countries for the same reference year.JRC.G.9-Econometrics and statistical support to antifrau