PhD Thesis Sergio Oramas - Knowledge Extraction and Representation Learning for Music Recommendation
Channel: MusicTechnologyGroup
Date: 2018-02-14

PhD Thesis defense of Sergio Oramas 
29th November 2017
Title: Knowledge Extraction and Representation Learning for Music Recommendation and Classification
Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain

Slides: https://www.slideshare.net/soramas/phd-thesis-knowledge-extraction-and-representation-learning-for-music-recommendation-and-classification

Document: http://dx.doi.org/10.5281/zenodo.1100973

Abstract
In this thesis, we address the problems of classifying and recommending music present in large collections. We focus on the semantic enrichment of descriptions associated to musical items (e.g., artists biographies, album reviews, metadata), and the exploitation of multimodal data (e.g., text, audio, images). To this end, we first focus on the problem of linking music-related texts with online knowledge repositories and on the automated construction of music knowledge bases. Then, we show how modeling semantic information may impact musicological studies and helps to outperform purely text-based approaches in music similarity, classification, and recommendation. Next, we focus on learning new data representations from multimodal content using deep learning architectures, addressing the problems of cold-start music recommendation and multi-label music genre classification, combining audio, text, and images. We show how the semantic enrichment of texts and the combination of learned data representations improve the performance on both tasks.