LooPy: A Research-Friendly Mix Framework for Music Information Retrieval on Electronic Dance Music
This provides a tool to address data scarcity for researchers in music information retrieval focusing on EDM, though it is incremental as it builds on existing symbolic music generation pipelines.
The authors tackled the lack of labeled data for electronic dance music (EDM) in music information retrieval by developing LooPy, a Python package for automated EDM audio generation, which produces mixes matching the quality of reference songs from world-famous artists based on subjective and objective criteria.
Music information retrieval (MIR) has gone through an explosive development with the advancement of deep learning in recent years. However, music genres like electronic dance music (EDM) has always been relatively less investigated compared to others. Considering its wide range of applications, we present a Python package for automated EDM audio generation as an infrastructure for MIR for EDM songs, to mitigate the difficulty of acquiring labelled data. It is a convenient tool that could be easily concatenated to the end of many symbolic music generation pipelines. Inside this package, we provide a framework to build professional-level templates that could render a well-produced track from specified melody and chords, or produce massive tracks given only a specific key by our probabilistic symbolic melody generator. Experiments show that our mixes could achieve the same quality of the original reference songs produced by world-famous artists, with respect to both subjective and objective criteria. Our code is accessible in this repository: https://github.com/Gariscat/loopy and the official site of the project is also online https://loopy4edm.com .