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Knowledge Extraction and Representation Learning for Music Recommendation and Classification

Title Knowledge Extraction and Representation Learning for Music Recommendation and Classification
Publication Type PhD Thesis
Year of Publication 2017
University Universitat Pompeu Fabra
Authors Oramas, S.
Advisor Serra, X.
Academic Department Department of Information and Communication Technologies
Number of Pages 203
Date Published 11/2017
City Barcelona
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.
Final publication https://doi.org/10.5281/zenodo.1100973
Additional material:
List of related resources http://sergiooramas.com/phd-thesis/
Video of the defense https://youtu.be/NpZhtKNBhZk
Slides https://www.slideshare.net/soramas/phd-thesis-knowledge-extraction-and-r...