MIRFLEX: Music Information Retrieval Feature Library for Extraction
It provides a versatile toolkit for the music information retrieval research community to support benchmarking and development of innovative solutions, though it is incremental as it integrates existing models.
The paper tackles the problem of extracting diverse musical features for music information retrieval by introducing MIRFLEX, an extendable modular system that compiles state-of-the-art models for features like key, genre, and instrument recognition, enabling integration into applications such as generative music and recommendation.
This paper introduces an extendable modular system that compiles a range of music feature extraction models to aid music information retrieval research. The features include musical elements like key, downbeats, and genre, as well as audio characteristics like instrument recognition, vocals/instrumental classification, and vocals gender detection. The integrated models are state-of-the-art or latest open-source. The features can be extracted as latent or post-processed labels, enabling integration into music applications such as generative music, recommendation, and playlist generation. The modular design allows easy integration of newly developed systems, making it a good benchmarking and comparison tool. This versatile toolkit supports the research community in developing innovative solutions by providing concrete musical features.