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.
View on arXiv@article{telila2025_2505.04451,
title={ Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform },
author={ Yohannis Telila and Tommaso Cucinotta and Davide Bacciu },
journal={arXiv preprint arXiv:2505.04451},
year={ 2025 }
}