cosine.club

cosine.club utilizes a deep learning model, trained via contrastive learning on a large corpus of audio, to generate high-dimensional vector embeddings of songs. These embeddings encapsulate a rich representation of audio characteristics, effectively mapping complex acoustic features into a dense numerical space.

When a query track is input, its embedding is computed and compared against a database of pre-computed track embeddings using cosine similarity as the distance metric. This process identifies tracks with the most similar acoustic properties in the embedding space. cosine.club can identify musically similar tracks across genres and styles based on intrinsic audio features. This method captures tonal, rhythmic, and structural affinities between tracks, regardless of conventional categorizations or traditional metadata based approaches.