On-the-Fly Audio Source Separation-A Novel User-Friendly Framework

This paper addresses the challenging problem of single-channel audio source separation. We introduce a novel userguided framework where source models that govern the separation process are learned on-the-fly from audio examples retrieved online. The user only provides the search keywords that describe the sources in the mixture. In this framework, the generic spectral characteristics of each source are modeled by a universal sound class model learned from the retrieved examples via nonnegative matrix factorization. We propose several group sparsity-inducing constraints in order to efficiently exploit a relevant subset of the universal model adapted to the mixture to be separated. We then derive the corresponding multiplicative update rules for parameter estimation. Separation results obtained from automated and user tests on mixtures containing various types of sounds confirm the effectiveness of the proposed framework

Algoritmos de procesado de señal basados en Non-negative Matrix Factorization aplicados a la separación, detección y clasificación de sibilancias en señales de audio respiratorias monocanal

La auscultación es el primer examen clínico que un médico lleva a cabo para evaluar el estado del sistema respiratorio, debido a que es un método no invasivo, de bajo coste, fácil de realizar y seguro para el paciente. Sin embargo, el diagnóstico que se deriva de la auscultación sigue siendo un diagnóstico subjetivo que se encuentra condicionado a la habilidad, experiencia y entrenamiento de cada médico en la escucha e interpretación de las señales de audio respiratorias. En consecuencia, se producen un alto porcentaje de diagnósticos erróneos que ponen en riesgo la salud de los pacientes e incrementan el coste asociado a los centros de salud. Esta Tesis propone nuevos métodos basados en Non-negative Matrix Factorization aplicados a la separación, detección y clasificación de sonidos sibilantes para proporcionar una vía de información complementaria al médico que ayude a mejorar la fiabilidad del diagnóstico emitido por el especialista. Auscultation is the first clinical examination that a physician performs to evaluate the condition of the respiratory system, because it is a non-invasive, low-cost, easy-to-perform and safe method for the patient. However, the diagnosis derived from auscultation remains a subjective diagnosis that is conditioned by the ability, experience and training of each physician in the listening and interpretation of respiratory audio signals. As a result, a high percentage of misdiagnoses are produced that endanger the health of patients and increase the cost associated with health centres. This Thesis proposes new methods based on Non-negative Matrix Factorization applied to separation, detection and classification of wheezing sounds in order to provide a complementary information pathway to the physician that helps to improve the reliability of the diagnosis made by the doctor.Tesis Univ. Jaén. Departamento INGENIERÍA DE TELECOMUNICACIÓ