Publication details

Y. Telila, T. Cucinotta, D. Bacciu. "Automatic Music Transcription using Convolutional Neural Networks and Constant-Q Transform," in Proceedings of the 3rd National CINI Conference on Artificial Intelligence (Ital-IA 2023), AI for Media & Games Workshop, May 29-31, 2023, Pisa, Italy.

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.

Open Access under a Creative Commons License (CC BY 4.0).

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Last updated on
07 November 2024