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

TL;DR

This paper presents a method for automatic music transcription of polyphonic piano recordings by transforming audio signals with constant-Q and applying CNNs to produce music scores.

Contribution

It introduces a processing pipeline combining constant-Q transform features with CNNs specifically for polyphonic piano transcription.

Findings

Effective note detection in polyphonic recordings

High accuracy in transcribing classical piano audio

Pipeline adaptable to other polyphonic instruments

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.