FabianGroeger96/deep-embedded-music
Creation of an embedding space using unsupervised triplet loss and Tile2Vec that can be used for a variety of downstream tasks
This project helps you organize and understand large collections of audio by grouping similar-sounding tracks together. It takes raw audio files and generates a 'map' where the distance between points represents how similar two sounds are. Audio engineers, music curators, or sound archivists can use this to quickly identify patterns or create playlists based on acoustic similarity.
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Use this if you need to automatically categorize or find similarities between audio files without manually tagging them, especially for tasks like music genre classification or environmental sound analysis.
Not ideal if you need a pre-trained, off-the-shelf solution for audio classification without any model training or if your primary need is for human-understandable labels rather than similarity scores.
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Jupyter Notebook
License
MIT
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Last pushed
Aug 09, 2021
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