Working with emotions : Recommending subjective labels to music tracks using machine learning
Abstract: Curated music collection is a growing field as a result of the freedom and supply that streaming music services like Spotify provide us with. To be able to categorize music tracks based on subjective core values in a scalable manner, this thesis has explored if recommending such labels are possible through machine learning. When analysing 2464 tracks with one or more of the 22 different core values a profile was built up for each track by features from three different categories: editorial, cultural and acoustic. When classifying the tracks into core values different methods of multi-label classification were explored. By combining five different transformation approaches with three base classifiers and using two algorithm adaptations a total of 17 different configurations were constructed. The different configu- rations were evaluated with multiple measurements including (but not limited to) Hamming Loss, Ranking Loss, One error, F1 score, exact match and both training and testing time. The results showed that the problem transformation algorithm Label Powerset together with Sequential minimal optimization outper- formed the other configurations. We also found promising results for neural networks, something that should be investigated further in the future.
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