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dc.rights.license http://creativecommons.org/licenses/by-nc-nd/4.0 es_ES
dc.contributor Juan Manuel Ramos Arreguín es_ES
dc.creator Rodrigo González Huerta es_ES
dc.date 2023-06-01
dc.date.accessioned 2023-06-02T19:14:35Z
dc.date.available 2023-06-02T19:14:35Z
dc.date.issued 2023-06-01
dc.identifier.uri https://ri-ng.uaq.mx/handle/123456789/8442
dc.description Environmental sound classification is a computational task that belongs to the branch of artificial intelligence called sound recognition. Several techniques and different approaches exist to tackle this task; one that yields excellent results is through the utilization of deep learning techniques, i.e., neural networks. Despite their good results, neural networks in some cases fail to generalize well to new data when the amount of training data is scarce. This can lead to a phenomena called overfitting. A solution to this inconvenience is based on the use of deep generative models to generate synthetic data through the approximation of high-dimensional probability distributions. This allow to generate new samples, similar to the ones used to train the generative model. Generative Adversarial Networks (GANs) are a kind of generative model which trains two neural networks simultaneously in an adversarial way, i.e., pitting one against the other. In this work it is shown the effect of using GANs as data augmentation technique that could be used to improve the performance of different sound classification models. es_ES
dc.format Adobe PDF es_ES
dc.language.iso spa es_ES
dc.publisher Ingeniería es_ES
dc.relation.requires Si es_ES
dc.rights Acceso Abierto es_ES
dc.subject Ingeniería y Tecnología es_ES
dc.subject Ciencias Tecnológicas es_ES
dc.subject Otras especialidades tecnológicas es_ES
dc.title Classification of multiple sound events in a single frame using generative adversarial networks es_ES
dc.type Tesis de maestría es_ES
dc.creator.tid CURP es_ES
dc.contributor.tid CURP es_ES
dc.creator.identificador GOHR910829HQTNRD06 es_ES
dc.contributor.identificador RAAJ710606HGTMRN01 es_ES
dc.contributor.role Director es_ES
dc.degree.name Maestría en Ciencias en Inteligencia Artificial es_ES
dc.degree.department Facultad de Ingeniería es_ES
dc.degree.level Maestría es_ES


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