FX-ENCODER++: EXTRACTING INSTRUMENT-WISE AUDIO EFFECTS REPRESENTATIONS FROM MIXTURES
Journal
Proceedings of the International Society for Music Information Retrieval Conference
Journal Volume
2025
Start Page
626
End Page
634
ISSN
30063094
Date Issued
2025-09-21
Author(s)
Abstract
General-purpose audio representations have proven effective across diverse music information retrieval applications, yet their utility in intelligent music production remains limited by insufficient understanding of audio effects (Fx). Although previous approaches have emphasized audio effects analysis at the mixture level, this focus falls short for tasks demanding instrument-wise audio effects understanding, such as automatic mixing. In this work, we present Fx-Encoder++, a novel model designed to extract instrument-wise audio effects representations from music mixtures. Our approach leverages a contrastive learning framework and introduces an “extractor” mechanism that, when provided with instrument queries (audio or text), transforms mixture-level audio effects embeddings into instrument-wise audio effects embeddings. We evaluated our model across retrieval and audio effects parameter matching tasks, testing its performance across a diverse range of instruments. The results demonstrate that Fx-Encoder++ outperforms previous approaches at mixture level and show a novel ability to extract effects representation instrument-wise, addressing a critical capability gap in intelligent music production systems.
SDGs
Publisher
International Society for Music Information Retrieval
Type
book part