MIRFLEX: Music Information Retrieval Feature Library for Extraction
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Hierarchical Deep Learning Approach for Minority Instrument Detection
Universal Music Representations? Evaluating Foundation Models on World Music Corpora
Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
ScholarSearch: Benchmarking Scholar Searching Ability of LLMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.