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Today’s offer of audio content exceeds the human capability of manually searching datasets with hundreds of songs, demanding automated tools capable of handling music recommendation when faced with large-scale collections.
In this work, we address the playlist generation and song discovery tasks with large-scale datasets. It is possible to quickly obtain playlists and explore collections with example- based queries using audio features, lyrics and tags.
We developed a music discovery prototype to demonstrate this content based approach. This demo is based on the Million Song Dataset, a large-scale collection of audio features and associated text data comprising almost 300 GB of in- formation.