Automatic Music Transcription – An Overview

Abstract

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.

Related

September 2022 | Interspeech

Unsupervised Speaker Diarization that is Agnostic to Language Overlap Aware and Free of Tuning

M Iftekhar Tanveer, Diego Casabuena, Jussi Karlgren, Rosie Jones

September 2022 | Interspeech

Exploring audio-based stylistic variation in podcasts

Katariina Martikainen, Jussi Karlgren, Khiet Truong

July 2022 | SIGIR

What Makes a Good Podcast Summary?

Rezvaneh Rezapour, Sravana Reddy, Ann Clifton, Rosie Jones