Fretting-Transformer: Encoder-Decoder Model for MIDI to Tablature Transcription
원문 초록
Music transcription plays a pivotal role in Music Information Retrieval (MIR), particularly for stringed instruments like the guitar, where symbolic music notations such as MIDI lack crucial playability information. This contribution introduces the Fretting-Transformer, an encoderdecoder model that utilizes a T5 transformer architecture to automate the transcription of MIDI sequences into guitar tablature. By framing the task as a symbolic translation problem, the model addresses key challenges, including string-fret ambiguity and physical playability. The proposed system leverages diverse datasets, including DadaGP, GuitarToday, and Leduc, with novel data pre-processing and tokenization strategies. We have developed metrics for tablature accuracy and playability to quantitatively evaluate the performance. The experimental results demonstrate that the Fretting-Transformer surpasses baseline methods like A* and commercial applications like Guitar Pro. The integration of context-sensitive processing and tuning/capo conditioning further enhances the model's performance, laying a robust foundation for future developments in automated guitar transcription.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Polyphonic Piano Music Transcription System Exploiting Mutual Correlations of Different Musical Note States
A Hierarchical Deep Learning Approach for Minority Instrument Detection
Universal Music Representations? Evaluating Foundation Models on World Music Corpora
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
Layer-wise Investigation of Large-Scale Self-Supervised Music Representation Models
Learning Normal Patterns in Musical Loops
토론
이 논문과 연결된 포럼 글이 아직 없습니다.