Classification of multiple sound events in a single frame using generative adversarial networks

Rodrigo González Huerta

Buscar

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