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arXiv · 2505.04451

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

Yohannis Telila·Tommaso Cucinotta·Davide Bacciu
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Abstract

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.

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Yohannis Telila, Tommaso Cucinotta, Davide Bacciu. 2025-05-07. Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform. https://arxiv.org/abs/2505.04451

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