MIRFLEX 提供一站式音乐特征提取工具，助力音乐推荐与生成研究。
MIRFLEX: Music Information Retrieval Feature Library for Extraction
- 模块化设计整合多种音乐特征模型，支持扩展
- 可提取调性、节拍、人声性别等20+类特征
- 适合音乐推荐、生成与数据集构建的研究者使用
本文提出一个可扩展的模块化系统 MIRFLEX，集成多种音乐信息检索特征提取模型，涵盖调性、节拍、流派等音乐元素，以及乐器识别、人声/伴奏分类、人声性别检测等音频特征。所用模型均为当前最先进或最新开源版本。特征可输出为潜在表示或后处理标签，便于集成至生成音乐、推荐系统和歌单生成等应用。模块化架构支持新模型快速接入，具备良好基准测试与对比能力。该工具包为研究者提供具体可操作的音乐特征，推动创新解决方案的发展。
原文摘要 · Abstract (English)
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.
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