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

Taehyeon Kim, Man-Je Kim () and Chang Wook Ahn ()
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Taehyeon Kim: AI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
Man-Je Kim: Convergence of AI, Chonnam National University, Gwangju 61186, Republic of Korea
Chang Wook Ahn: AI Graduate School, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea

Mathematics, 2025, vol. 13, issue 11, 1-10

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.

Keywords: multi-instrument music transcription; joint feature extraction; combined frequency and periodicity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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