Clasificación de escenas acústicas a través de descriptores de audio y máquinas de aprendizaje. Aplicación en escenas de Medellín

In recent years, automatic learning methods have been paired to obtain models for the analysis and classification of audio signals, such as the support vector machines, Ensemble Classifier, among others. These methods present a problem because they are not very understandable in their internal functioning, since they do not show the user an explanatory structure of how predictions are made and that they are understandable. It is worth mentioning that the models are accurate, but they are not presented properly. There is not a sound bank of the acoustic scenes of the city, it was necessary to record these outside scenes in the field. Audio descriptors such as MFCC and Chroma Vector were used to identify the acoustic scenes together with two SVM algorithms and one Ensemble Classifier. The result was an efficiency rate of 72.22% for the cases of SVM machines (Medium Gaussian and Quadratic), which are satisfactory. On the other hand, the learning machine based on Ensemble Classifier (Boosted Tree) had an Accuracy rate of 55.55%, this being a low performance machine.

• Autores: Chica Osorio, Carlos Andrés; Yurgaky Valoyes, Dudley
• Fecha de publicación: 2019
• Institución: Universidad de San Buenaventura
• Repositorio: Repositorio USB
• Idioma: spa
• OAI Identifier: oai:bibliotecadigital.usb.edu.co:10819/6827
• Acceso en línea: http://hdl.handle.net/10819/6827
• Palabra clave: Grabaciones en campo; Aprendizaje automático; Descriptores de audio; Eficiencia; Field recording; Machine Learning; Audio predictors; Accuracy; Transmisión del sonido; Fuentes de sonido; Sonido digital; Ingeniería de sonido; Fuentes acústicas; Acústica; Efectos y procesamiento de audio; Sistemas de procesamiento de audio; Producción de audio; Equipos de audio portátil; Frecuencia de audio; Audio
• License: Atribución-NoComercial-SinDerivadas 2.5 Colombia