The combination of this intricate musical data with the distinct characteristics of Synthwave, a genre rooted in 1980s electronic sounds, creates a one-of-a-kind resource for training models in a variety of applications, including generative AI music, Music Information Retrieval (MIR), and source separation.

The Synthwave dataset contains audio tracks that define the essence of the genre, including rhythmic basslines, arpeggiated synthesizers, and electronic drum patterns that define the nostalgic yet futuristic soundscapes of the 1980s. This dataset serves as a platform for machine learning models to identify and duplicate the complex parts of Synthwave songs, encouraging creativity in generative music creation and improving MIR analysis capabilities. Furthermore, the inclusion of metadata containing chords, key signatures, and timestamps provides a granular understanding of the musical structure, allowing academics and practitioners to conduct source separation tasks with unparalleled precision.