# Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform

arXiv: https://arxiv.org/abs/2505.04451 · HTML: https://mlsanity.com/paper/2505.04451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 3 of 3)

- [Neural Autoregressive Distribution Estimation](https://mlsanity.com/paper/1605.02226/) (2016)
- [Onsets and Frames: Dual-Objective Piano Transcription](https://mlsanity.com/paper/1710.11153/) (2017)
- [On the Potential of Simple Framewise Approaches to Piano Transcription](https://mlsanity.com/paper/1612.05153/) (2016)

## Abstract (arXiv)

Automatic music transcription (AMT) is the problem of analyzing an audio recording of a musical piece and detecting notes that are being played. AMT is a challenging problem, particularly when it comes to polyphonic music. The goal of AMT is to produce a score representation of a music piece, by analyzing a sound signal containing multiple notes played simultaneously. In this work, we design a processing pipeline that can transform classical piano audio files in .wav format into a music score representation. The features from the audio signals are extracted using the constant-Q transform, and the resulting coefficients are used as an input to the convolutional neural network (CNN) model.