ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912461558382592 |
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| author | Koo, Junghyun Martínez-Ramírez, Marco A. Liao, Wei-Hsiang Fabbro, Giorgio Mancusi, Michele Mitsufuji, Yuki |
| author_facet | Koo, Junghyun Martínez-Ramírez, Marco A. Liao, Wei-Hsiang Fabbro, Giorgio Mancusi, Michele Mitsufuji, Yuki |
| contents | Music mastering style transfer aims to model and apply the mastering characteristics of a reference track to a target track, simulating the professional mastering process. However, existing methods apply fixed processing based on a reference track, limiting users' ability to fine-tune the results to match their artistic intent. In this paper, we introduce the ITO-Master framework, a reference-based mastering style transfer system that integrates Inference-Time Optimization (ITO) to enable finer user control over the mastering process. By optimizing the reference embedding during inference, our approach allows users to refine the output dynamically, making micro-level adjustments to achieve more precise mastering results. We explore both black-box and white-box methods for modeling mastering processors and demonstrate that ITO improves mastering performance across different styles. Through objective evaluation, subjective listening tests, and qualitative analysis using text-based conditioning with CLAP embeddings, we validate that ITO enhances mastering style similarity while offering increased adaptability. Our framework provides an effective and user-controllable solution for mastering style transfer, allowing users to refine their results beyond the initial style transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16889 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors Koo, Junghyun Martínez-Ramírez, Marco A. Liao, Wei-Hsiang Fabbro, Giorgio Mancusi, Michele Mitsufuji, Yuki Sound Audio and Speech Processing Music mastering style transfer aims to model and apply the mastering characteristics of a reference track to a target track, simulating the professional mastering process. However, existing methods apply fixed processing based on a reference track, limiting users' ability to fine-tune the results to match their artistic intent. In this paper, we introduce the ITO-Master framework, a reference-based mastering style transfer system that integrates Inference-Time Optimization (ITO) to enable finer user control over the mastering process. By optimizing the reference embedding during inference, our approach allows users to refine the output dynamically, making micro-level adjustments to achieve more precise mastering results. We explore both black-box and white-box methods for modeling mastering processors and demonstrate that ITO improves mastering performance across different styles. Through objective evaluation, subjective listening tests, and qualitative analysis using text-based conditioning with CLAP embeddings, we validate that ITO enhances mastering style similarity while offering increased adaptability. Our framework provides an effective and user-controllable solution for mastering style transfer, allowing users to refine their results beyond the initial style transfer. |
| title | ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.16889 |