Serra, Xavier
Oramas, Sergio
2017-11-14
<p>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. </p>
Full list of associated resources http://sergiooramas.com/phd-thesis/
Slides https://www.slideshare.net/soramas/phd-thesis-knowledge-extraction-and-representation-learning-for-music-recommendation-and-classification
Presentation video: https://youtu.be/NpZhtKNBhZk
https://doi.org/10.5281/zenodo.1100973
oai:zenodo.org:1100973
eng
Zenodo
https://zenodo.org/communities/mdm-dtic-upf
https://doi.org/10.5281/zenodo.1048496
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
music information retrieval
recommender systems
natural language processing
deep learning
musicology
information extraction
Knowledge Extraction and Representation Learning for Music Recommendation and Classification
info:eu-repo/semantics/doctoralThesis