Automated Organisation and Quality Analysis of User-Generated Audio Content

• Autor(a) principal: Mordido, Gonçalo Filipe Torcato
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/27752
• topic: User-generated content,; Audio fingerprinting; Audio clustering; Audio segmentation; Audio quality; Supervised learning; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

The abundance and ubiquity of user-generated content has opened horizons when it comes to the organization and analysis of vast and heterogeneous data, especially with the increase of quality of the recording devices witnessed nowadays. Most of the activity experienced in social networks today contains audio excerpts, either by belonging to a certain video file or an actual audio clip, therefore the analysis of the audio features present in such content is of extreme importance in order to better understand it. Such understanding would lead to a better handling of ubiquity data and would ultimately provide a better experience to the end-user. The work discussed in this thesis revolves around using audio features to organize and retrieve meaningful insights from user-generated content crawled from social media websites, more particularly data related to concert clips. From its redundancy and abundance (i.e., for the existence of several recordings of a given event), recordings from musical shows represent a very good use case to derive useful and practical conclusions around the scope of this thesis. Mechanisms that provide a better understanding of such content are presented and already partly implemented, such as audio clustering based on the existence of overlapping audio segments between different audio clips, audio segmentation that synchronizes and relates the different cluster’s clips in time, and techniques to infer audio quality of such clips. All the proposed methods use information retrieved from an audio fingerprinting algorithm, used for the synchronization of the different audio files, with methods for filtering possible false positives of the algorithm being also presented. For the evaluation and validation of the proposed methods, we used one dataset made of several audio recordings regarding different concert clips manually crawled from YouTube.