Semantic Scholar Open Access 2019 281 sitasi

Automatic Music Transcription: An Overview

Emmanouil Benetos S. Dixon Z. Duan Sebastian Ewert

Lihat Sumber DOI

Abstrak

The capability of transcribing music audio into music notation is a fascinating example of human intelligence. It involves perception (analyzing complex auditory scenes), cognition (recognizing musical objects), knowledge representation (forming musical structures), and inference (testing alternative hypotheses). Automatic music transcription (AMT), i.e., the design of computational algorithms to convert acoustic music signals into some form of music notation, is a challenging task in signal processing and artificial intelligence. It comprises several subtasks, including multipitch estimation (MPE), onset and offset detection, instrument recognition, beat and rhythm tracking, interpretation of expressive timing and dynamics, and score typesetting.

Topik & Kata Kunci

Computer Science

Penulis (4)

Emmanouil Benetos

S. Dixon

Z. Duan

Sebastian Ewert

Format Sitasi

APA MLA BibTeX

Benetos, E., Dixon, S., Duan, Z., Ewert, S. (2019). Automatic Music Transcription: An Overview. https://doi.org/10.1109/MSP.2018.2869928

Akses Cepat

Lihat di Sumber doi.org/10.1109/MSP.2018.2869928

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 281×
Sumber Database: Semantic Scholar
DOI: 10.1109/MSP.2018.2869928
Akses: Open Access ✓