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

• URL: http://arxiv.org/abs/2505.04451v1
• Date: Wed, 07 May 2025 14:20:43 GMT
• Title: Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform
• Authors: Yohannis Telila, Tommaso Cucinotta, Davide Bacciu,
• Abstract summary: We design a processing pipeline that can transform classical piano audio files in.wav format into a music score representation.<n>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.
• Score: 14.72084645157747
• License: http://creativecommons.org/licenses/by/4.0/
• 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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