PF2N: Periodicity–Frequency Fusion Network for Multi-Instrument Music Transcription

Abstract

Automatic music transcription in multi-instrument settings remains a highly challenging task due to overlapping harmonics and diverse timbres. To address this, we propose the Periodicity–Frequency Fusion Network (PF2N), a lightweight and modular component that enhances transcription performance by integrating both spectral and periodicity-domain representations. Inspired by traditional combined frequency and periodicity (CFP) methods, the PF2N reformulates CFP as a neural module that jointly learns harmonically correlated features across the frequency and cepstral domains. Unlike handcrafted alignments in classical approaches, the PF2N performs data-driven fusion using a learnable joint feature extractor. Extensive experiments on three benchmark datasets (Slakh2100, MusicNet, and MAESTRO) demonstrate that the PF2N consistently improves transcription accuracy when incorporated into state-of-the-art models. The results confirm the effectiveness and adaptability of the PF2N, highlighting its potential as a general-purpose enhancement for multi-instrument AMT systems. © 2025 by the authors.