Timbre-Adaptive Transcription: A Lightweight Architecture with Associative Memory for Dynamic Instrument Separation

• URL: http://arxiv.org/abs/2509.12712v1
• Date: Tue, 16 Sep 2025 06:05:36 GMT
• Title: Timbre-Adaptive Transcription: A Lightweight Architecture with Associative Memory for Dynamic Instrument Separation
• Authors: Ruigang Li, Yongxu Zhu,
• Abstract summary: A timbre-agnostic backbone achieves state-of-the-art performance with only half the parameters of comparable models.<n>A novel associative memory mechanism mimics human auditory cognition to dynamically encode unseen timbres.
• Score: 8.166820420083175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing multi-timbre transcription models struggle with generalization beyond pre-trained instruments and rigid source-count constraints. We address these limitations with a lightweight deep clustering solution featuring: 1) a timbre-agnostic backbone achieving state-of-the-art performance with only half the parameters of comparable models, and 2) a novel associative memory mechanism that mimics human auditory cognition to dynamically encode unseen timbres via attention-based clustering. Our biologically-inspired framework enables adaptive polyphonic separation with minimal training data (12.5 minutes), supported by a new synthetic dataset method offering cost-effective, high-precision multi-timbre generation. Experiments show the timbre-agnostic transcription model outperforms existing models on public benchmarks, while the separation module demonstrates promising timbre discrimination. This work provides an efficient framework for timbre-related music transcription and explores new directions for timbre-aware separation through cognitive-inspired architectures.

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