[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82070-en":3,"doc-seo-82070-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82070,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Clean2FX: Label-conditioned Modeling for Clean-to-effect Guitar Audio Transformations","Clean2FX explores label-conditioned clean-to-effect transformation for electric guitar audio, taking a clean input and a target effect label to synthesize an effected signal while preserving musical content. Training and evaluation pairs are assembled from EGFxSet real single-tone recordings by matching clean/effected chords, melodies, and mixed timelines for controlled comparisons. Four neural spectrogram models are tested: two variational autoencoders and two conditional U-Nets (linear vs log magnitude). U-Nets outperform VAEs, with strongest gains for distortion and weaker FAD improvements for delay and reverb.","Proceedings of the 29th International Conference on Digital Audio Effects (DAFx26), Cambridge, MA, USA, 1 –4 September 2026 (Demo)  \nCLEAN2FX: LABEL-CONDITIONED MODELING FOR CLEAN-TO-EFFECT GUITAR  \nAUDIO TRANSFORMATIONS  \nOliverio Bombicci Pontelli  \nSchool of Electronic Engineering & Computer Science Queen Mary University of London London, United Kingdom [oliveriobp@gmail.com](oliveriobp@gmail.com)  \nIran R. Roman  \nSchool of Electronic Engineering & Computer Science Queen Mary University of London London, United Kingdom [i.roman@qmul.ac.uk](i.roman@qmul.ac.uk)  \narXiv :2607 .08863v 1 [ cs . SD] 9 Jul 2026  \nABSTRACT  \nWe present Clean2FX, a study and demo of label-conditioned clean-to-effect transformation for electric guitar audio. Given a clean guitar input and a target effect label, the task is to synthesize the corresponding effected signal while preserving the musical content. Training and evaluation pairs are constructed from EGFxSet real, single-tone recordings by assembling matched clean/effected chords, melodies, and mixed timelines. This allows for controlled comparison across effects. We evaluate four neural approaches under a common spectrogram-based transformation setting: two variational autoencoders and two U-Net models that differ in whether they operate on linear or log-magnitude representations. Performance is measured using linear-magnitude spectrogram MSE and Fréchet Audio Distance. The U-Net models outperform the variational autoencoder variants. Per-effect results show that distortion effects are most readily improved, whereas delay and reverb effects exhibit weaker FAD gains despite substantial spectral-error reductions. A conditioning-sensitivity diagnostic provides evidence that the best model responds to target labels rather than collapsing to a single transformation. Our demo website compares two models applied on real-world guitar performances outside training and validation data, providing audio and spectrogram examples of the practical clean-to-effect behavior.  \n1. INTRODUCTION  \nEffects are central to the sound of the electric guitar: they reshape timbre, dynamics, sustain, and apparent space, and have become part of both the instrument’s musical vocabulary and the broader history of audio effects processing [1, 2, 3, 4] . From a signal-processing perspective, effects can be understood as transformations of a clean instrumental source [5] . Recent neural approaches to audio effects have shown that input–output behavior can be learned directly from paired recordings, including blackbox modeling of audio effects [6], real-time guitar-amplifier emulation [7], and controllable audio-effect transformation [8] . Much of this work, however, is organized around a single device, a single effect class, or a parametric processor. Clean-to-effect guitar performance transformation across several effect families poses a problem: preservation of pitch, rhythm, and playing content while applying a requested transformation whose acoustic footprint may range from broadband distortion to modulation, delay, or reverb.  \nCopyright: © 2026 Oliverio Bombicci Pontelli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, adaptation, and reproduction in any medium, provided the original author and source are credited.  \nThis paper presents Clean2FX as both a study of labelconditioned clean-to-effect guitar audio transformation and a web demo. We construct paired clean/effected examples from EGFxSet, a dataset of electric-guitar tones processed through real hardware effects [9, 10], by assembling matched chords, melodies, and mixed timelines. This gives a controlled setting in which the musical content is held fixed and the target effect label defines the transformation. We compare four neural approaches: two variational autoencoders and two conditional encoder–decoder U-Nets.  \nThe demo website com","cbCaisPgRaKTM4zC","https://ap.wps.com/l/cbCaisPgRaKTM4zC","pdf",204812,1,4,"English","en",105,"# Abstract\n# Introduction\n# Methods","[{\"question\":\"Which model family performs best, and how is it evaluated?\",\"answer\":\"Conditional U-Net models outperform variational autoencoder variants. 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