SonicMaster: Towards Controllable All-in-One Music Restoration and
  Mastering
Music recordings often suffer from audio quality issues such as excessive
reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo
image, especially when created in non-professional settings without specialized
equipment or expertise. These problems are typically corrected using separate
specialized tools and manual adjustments. In this paper, we introduce
SonicMaster, the first unified generative model for music restoration and
mastering that addresses a broad spectrum of audio artifacts with text-based
control. SonicMaster is conditioned on natural language instructions to apply
targeted enhancements, or can operate in an automatic mode for general
restoration. To train this model, we construct the SonicMaster dataset, a large
dataset of paired degraded and high-quality tracks by simulating common
degradation types with nineteen degradation functions belonging to five
enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our
approach leverages a flow-matching generative training paradigm to learn an
audio transformation that maps degraded inputs to their cleaned, mastered
versions guided by text prompts. Objective audio quality metrics demonstrate
that SonicMaster significantly improves sound quality across all artifact
categories. Furthermore, subjective listening tests confirm that listeners
prefer SonicMaster's enhanced outputs over the original degraded audio,
highlighting the effectiveness of our unified approach.
68943937741a16f544fbcf91
Jan Melechovsky
68943937741a16f544fbcf92
Ambuj Mehrish
68943937741a16f544fbcf93
Dorien Herremans
claimed_verified
2025-08-07T10:37:57.123Z
655431b2997379e9b0999d23
https://cdn-avatars.huggingface.co/v1/production/uploads/655431b2997379e9b0999d23/OhHCPCXfOS61Z7CSewUCU.png
Dorien Herremans
user
dorienh
SonicMaster: Towards Controllable All-in-One Music Restoration and
  Mastering
Music recordings often suffer from audio quality issues such as excessive
reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo
image, especially when created in non-professional settings without specialized
equipment or expertise. These problems are typically corrected using separate
specialized tools and manual adjustments. In this paper, we introduce
SonicMaster, the first unified generative model for music restoration and
mastering that addresses a broad spectrum of audio artifacts with text-based
control. SonicMaster is conditioned on natural language instructions to apply
targeted enhancements, or can operate in an automatic mode for general
restoration. To train this model, we construct the SonicMaster dataset, a large
dataset of paired degraded and high-quality tracks by simulating common
degradation types with nineteen degradation functions belonging to five
enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our
approach leverages a flow-matching generative training paradigm to learn an
audio transformation that maps degraded inputs to their cleaned, mastered
versions guided by text prompts. Objective audio quality metrics demonstrate
that SonicMaster significantly improves sound quality across all artifact
categories. Furthermore, subjective listening tests confirm that listeners
prefer SonicMaster's enhanced outputs over the original degraded audio,
highlighting the effectiveness of our unified approach.
SonicMaster, a unified generative model, improves music audio quality by addressing various artifacts using text-based control and a flow-matching generative training paradigm.
generative model music restoration mastering text-based control flow-matching audio transformation equalization dynamics reverb amplitude stereo