FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription

TL;DR

This paper introduces FretNet, a novel method for guitar tablature transcription that estimates continuous pitch contours, capturing pitch modulation and improving multi-pitch estimation resolution compared to traditional quantized approaches.

Contribution

FretNet is the first approach to estimate continuous-valued pitch contours in guitar tablature transcription, addressing pitch modulation and enhancing MPE resolution.

Findings

Significantly improves pitch resolution in MPE.

Achieves competitive tablature estimation results.

Effectively captures pitch modulation techniques.

Abstract

In recent years, the task of Automatic Music Transcription (AMT), whereby various attributes of music notes are estimated from audio, has received increasing attention. At the same time, the related task of Multi-Pitch Estimation (MPE) remains a challenging but necessary component of almost all AMT approaches, even if only implicitly. In the context of AMT, pitch information is typically quantized to the nominal pitches of the Western music scale. Even in more general contexts, MPE systems typically produce pitch predictions with some degree of quantization. In certain applications of AMT, such as Guitar Tablature Transcription (GTT), it is more meaningful to estimate continuous-valued pitch contours. Guitar tablature has the capacity to represent various playing techniques, some of which involve pitch modulation. Contemporary approaches to AMT do not adequately address pitch modulation, and offer only less quantization at the expense of more model complexity. In this paper, we present a GTT formulation that estimates continuous-valued pitch contours, grouping them according to their string and fret of origin. We demonstrate that for this task, the proposed method significantly improves the resolution of MPE and simultaneously yields tablature estimation results competitive with baseline models.