A Survey on Automatic Music Transcription

• Author(s): Pranav Bhagwat ; Vishwajit Shelke ; Akhilesh Murugkar ; Krishiv Dakwala ; Shweta C. Dharmadhikari
• Paper ID: 1704431
• Page: 268-274
• Published Date: 12-05-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 6 Issue 11 May-2023

Abstract

Automatic Music Transcription (AMT) is a critical but less investigated problem in the field of music information retrieval. In this paper, we study different approaches for achieving Automatic Music Transcription using various methods based on pitch, timbre and note detection. Use of Convolutional Neural Network (CNN) and/or Long Short Term Memory Network (LSTM) is made to transcribe notes from the audio input. We also discuss source separation as a precursor to AMT and different approaches for the same.

Keywords

Automatic Music Transcription (AMT), Pitch Detection, Note Detection, Deep Learning, Convolutional Neural Network (CNN), Long Short Term Memory Network (LSTM), Source Separation

Citations

IRE Journals:
Pranav Bhagwat , Vishwajit Shelke , Akhilesh Murugkar , Krishiv Dakwala , Shweta C. Dharmadhikari "A Survey on Automatic Music Transcription" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 268-274

IEEE:
Pranav Bhagwat , Vishwajit Shelke , Akhilesh Murugkar , Krishiv Dakwala , Shweta C. Dharmadhikari "A Survey on Automatic Music Transcription" Iconic Research And Engineering Journals, 6(11)