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GRAFX: An Open-Source Library for Audio Processing Graphs in PyTorch

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arxiv 2408.03204 v1 pith:Y2XPAPNI submitted 2024-08-06 cs.SD eess.AS

Sungho Lee , Marco Martínez-Ramírez , Wei-Hsiang Liao , Stefan Uhlich , Giorgio Fabbro , Kyogu Lee , Yuki Mitsufuji This is my paper · ORCID
classification cs.SDeess.AS
keywords grafxgraphslibraryaudioopen-sourceparametersprocessingprocessor
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We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MixAssist: An Audio-Language Dataset for Co-Creative AI Assistance in Music Mixing

    cs.SD 2025-07 conditional novelty 7.0 of 10

    MixAssist is the first audio-grounded, multi-turn conversational dataset for co-creative music mixing instruction, and fine-tuning Qwen-Audio on it yields human-comparable mixing advice.

  2. WildFX: A DAW-Powered Pipeline for In-the-Wild Audio FX Graph Modeling

    cs.SD 2025-07 conditional novelty 6.0 of 10

    WildFX generates multi-track audio datasets by rendering real DAW effect graphs with commercial plugins inside Docker, and demonstrates the pipeline on blind mixing-graph estimation.

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