CREPE Notes: A new method for segmenting pitch contours into discrete notes

TL;DR

This paper introduces a simple post-processing method built on CREPE for segmenting monophonic pitch contours into discrete notes, achieving state-of-the-art accuracy with fewer parameters, aiding music analysis and generation.

Contribution

It presents a novel, effective post-processing approach for note segmentation that improves accuracy and reduces model complexity compared to existing deep learning methods.

Findings

Achieves state-of-the-art note segmentation results on challenging datasets.

Reduces total model parameters by 97% compared to other deep learning approaches.

Demonstrates effectiveness of simple neural network-based post-processing for music transcription.

Abstract

Tracking the fundamental frequency (f0) of a monophonic instrumental performance is effectively a solved problem with several solutions achieving 99% accuracy. However, the related task of automatic music transcription requires a further processing step to segment an f0 contour into discrete notes. This sub-task of note segmentation is necessary to enable a range of applications including musicological analysis and symbolic music generation. Building on CREPE, a state-of-the-art monophonic pitch tracking solution based on a simple neural network, we propose a simple and effective method for post-processing CREPE's output to achieve monophonic note segmentation. The proposed method demonstrates state-of-the-art results on two challenging datasets of monophonic instrumental music. Our approach also gives a 97% reduction in the total number of parameters used when compared with other deep learning based methods.