A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation

TL;DR

This paper introduces a lightweight, instrument-agnostic neural network for polyphonic music transcription that jointly predicts onsets, pitches, and note activations, achieving competitive accuracy with less complexity.

Contribution

The authors propose a simple, multi-output neural model capable of generalizing across instruments, including vocals, with improved accuracy over baseline methods.

Findings

Outperforms comparable baseline in note estimation accuracy.

Achieves frame-level accuracy close to specialized state-of-the-art systems.

Supports polyphonic transcription for a wide variety of instruments.

Abstract

Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results over instrument-agnostic methods. Similarly, higher accuracy can be obtained when only estimating frame-wise $f_0$ values and neglecting the harder note event detection. Despite their high accuracy, such specialized systems often cannot be deployed in the real-world. Storage and network constraints prohibit the use of multiple specialized models, while memory and run-time constraints limit their complexity. In this paper, we propose a lightweight neural network for musical instrument transcription, which supports polyphonic outputs and generalizes to a wide variety of instruments (including vocals). Our model is trained to jointly predict frame-wise onsets, multipitch and note activations, and we experimentally show that this multi-output structure improves the resulting frame-level note accuracy. Despite its simplicity, benchmark results show our system's note estimation to be substantially better than a comparable baseline, and its frame-level accuracy to be only marginally below those of specialized state-of-the-art AMT systems. With this work we hope to encourage the community to further investigate low-resource, instrument-agnostic AMT systems.