Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform
This addresses the challenging problem of transcribing polyphonic music for applications in music analysis and production, but it appears incremental as it applies existing methods to a specific domain.
The paper tackles automatic music transcription for polyphonic piano audio by designing a pipeline that uses constant-Q transform features and a convolutional neural network to convert .wav files into music scores.
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.