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

Considerations on the Psychological Status of the Patients Undergoing Radical Cystectomy

The psychological impact on patients suffering radical cystectomy is twofold - (both that of the underlying neoplastic disease and that measured by the quality of life subsequent to surgery) and increases as the urinary derivation technique is less physiological and affects more the local anatomy. Although there are numerous questionnaires that assess the quality of life of patients with cancer (HRQoL - health related QoL), not many probe bladder cancer morbidity or correlate the different types of urinary diversions’ impact on QoL (quality of life). We analyzed 39 cases in our clinic who underwent radical cystectomy between August 2013 and August 2014. Different diversions were performed, as follows: for 24 patients a cutaneous ureterostomy was performed, in 10 cases a Mainz II pouch, in 3 cases a Bricker derivation and in 2 patients a Studer neobladder was performed. In these patients, QoL - Cancer Version and FACT-BL questionnaires were administered and were followed for an initial period of 2 years. According to our survey, the Bricker derivation is best tolerated, followed by neobladder and the Mainz II pouch

Measurement of the cosmic ray spectrum above 4×10184{\times}10^{18} eV using inclined events detected with the Pierre Auger Observatory

A measurement of the cosmic-ray spectrum for energies exceeding $4{\times}10^{18}$ eV is presented, which is based on the analysis of showers with zenith angles greater than $60^{\circ}$ detected with the Pierre Auger Observatory between 1 January 2004 and 31 December 2013. The measured spectrum confirms a flux suppression at the highest energies. Above $5.3{\times}10^{18}$ eV, the "ankle", the flux can be described by a power law $E^{-\gamma}$ with index $\gamma=2.70 \pm 0.02 \,\text{(stat)} \pm 0.1\,\text{(sys)}$ followed by a smooth suppression region. For the energy ($E_\text{s}$) at which the spectral flux has fallen to one-half of its extrapolated value in the absence of suppression, we find $E_\text{s}=(5.12\pm0.25\,\text{(stat)}^{+1.0}_{-1.2}\,\text{(sys)}){\times}10^{19}$ eV.Comment: Replaced with published version. Added journal reference and DO

Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory

The Auger Engineering Radio Array (AERA) is part of the Pierre Auger Observatory and is used to detect the radio emission of cosmic-ray air showers. These observations are compared to the data of the surface detector stations of the Observatory, which provide well-calibrated information on the cosmic-ray energies and arrival directions. The response of the radio stations in the 30 to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of the incoming electric field. For the latter, the energy deposit per area is determined from the radio pulses at each observer position and is interpolated using a two-dimensional function that takes into account signal asymmetries due to interference between the geomagnetic and charge-excess emission components. The spatial integral over the signal distribution gives a direct measurement of the energy transferred from the primary cosmic ray into radio emission in the AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air shower arriving perpendicularly to the geomagnetic field. This radiation energy -- corrected for geometrical effects -- is used as a cosmic-ray energy estimator. Performing an absolute energy calibration against the surface-detector information, we observe that this radio-energy estimator scales quadratically with the cosmic-ray energy as expected for coherent emission. We find an energy resolution of the radio reconstruction of 22% for the data set and 17% for a high-quality subset containing only events with at least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO

Measurement of the Radiation Energy in the Radio Signal of Extensive Air Showers as a Universal Estimator of Cosmic-Ray Energy

We measure the energy emitted by extensive air showers in the form of radio emission in the frequency range from 30 to 80 MHz. Exploiting the accurate energy scale of the Pierre Auger Observatory, we obtain a radiation energy of 15.8 \pm 0.7 (stat) \pm 6.7 (sys) MeV for cosmic rays with an energy of 1 EeV arriving perpendicularly to a geomagnetic field of 0.24 G, scaling quadratically with the cosmic-ray energy. A comparison with predictions from state-of-the-art first-principle calculations shows agreement with our measurement. The radiation energy provides direct access to the calorimetric energy in the electromagnetic cascade of extensive air showers. Comparison with our result thus allows the direct calibration of any cosmic-ray radio detector against the well-established energy scale of the Pierre Auger Observatory.Comment: Replaced with published version. Added journal reference and DOI. Supplemental material in the ancillary file

Identification of Lynch syndrome risk variants in the Romanian population.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadTwo familial forms of colorectal cancer (CRC), Lynch syndrome (LS) and familial adenomatous polyposis (FAP), are caused by rare mutations in DNA mismatch repair genes (MLH1, MSH2, MSH6, PMS2) and the genes APC and MUTYH, respectively. No information is available on the presence of high-risk CRC mutations in the Romanian population. We performed whole-genome sequencing of 61 Romanian CRC cases with a family history of cancer and/or early onset of disease, focusing the analysis on candidate variants in the LS and FAP genes. The frequencies of all candidate variants were assessed in a cohort of 688 CRC cases and 4567 controls. Immunohistochemical (IHC) staining for MLH1, MSH2, MSH6, and PMS2 was performed on tumour tissue. We identified 11 candidate variants in 11 cases; six variants in MLH1, one in MSH6, one in PMS2, and three in APC. Combining information on the predicted impact of the variants on the proteins, IHC results and previous reports, we found three novel pathogenic variants (MLH1:p.Lys84ThrfsTer4, MLH1:p.Ala586CysfsTer7, PMS2:p.Arg211ThrfsTer38), and two novel variants that are unlikely to be pathogenic. Also, we confirmed three previously published pathogenic LS variants and suggest to reclassify a previously reported variant of uncertain significance to pathogenic (MLH1:c.1559-1G>C).European Union EE