SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering

## Abstract

Abstract 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 other baselines. The model and demo samples are available through https://github.com/AMAAI-Lab/SonicMaster.

## 1 Introduction

Music recordings produced in amateur settings often suffer from a variety of quality issues that distinguish them from professionally mastered recordings (Wilson & Fazenda, 2016; Mourgela et al., 2024; Deruty & Tardieu, 2014). For instance, an enthusiast recording vocals in a garage may introduce excessive reverberation, making the voice sound distant and “echoey.” Similarly, using inexpensive microphones or misconfigured interfaces can lead to distortion and clipping when loud peaks exceed the recording range, resulting in harsh crackles or flattened dynamics (Zang et al., 2025). Tonal imbalances are also common: a home recording might sound overly “muddy” or “tinny” if certain frequency bands dominate or vanish due to poor room acoustics or improper microphone placement. Even the stereo image can be narrowed or skewed, reducing the sense of space in the mix. In practice, engineers address these problems with specialized tools: e.g., dereverberation plugins to remove room echo, declipping algorithms to reconstruct saturated peaks, equalizers to rebalance frequencies, and stereo enhancers to widen the image. Mastering a flawed track has become a labor-intensive process requiring expert skill and multiple stages of manual adjustment.

The need for an automated all-in-one solution is evident. Creators with limited resources often lack the expertise to apply the right combination of restoration tools, and a piecemeal approach may fail to fully recover a track’s fidelity. This motivates SonicMaster, a unified approach to music restoration and mastering that can correct a broad spectrum of audio degradations within a single model.
We introduce a single flow-based generative framework (Liu et al., 2022; Esser et al., 2024) that simultaneously performs dereverberation, equalization, declipping, dynamic-range expansion, and stereo enhancement. The backbone is trained on a curated corpus of polyphonic music rendered through a combinatorial grid of simulated degradations, enabling the network to learn the joint statistics and cross-couplings of common artifacts rather than treating them in isolation. This joint training eliminates the need for error-prone cascades of task-specific modules and reduces inference to a single forward pass.

Crucially, SonicMaster incorporates multimodal conditioning through natural language instructions that capture production objectives. A prompt such as reduce the hollow room sound attenuates late reflections without suppressing desirable early reverberation,
whereas increase the brightness selectively enhances the treble frequencies while preserving spectral balance elsewhere.In the absence of a prompt, SonicMaster switches to an automatic mode that applies perceptually balanced mastering. Existing speech restoration models (e.g. VoiceFixer (Liu et al., 2021)) also address artifacts sequentially, ignoring their mutual influence. By unifying restoration and mastering tasks under a single, prompt-driven generative model, SonicMaster delivers professional-grade improvements while affording fine-grained creative control. Recent advances such as Mustango (Text-guided music generation) (Melechovsky et al., 2024), FlowSep (Yuan et al., 2025) (text-guided source separation), TangoFlux (Hung et al., 2024) (reward-optimized text-to-audio diffusion), and instruction-guided models like AUDIT (Wang et al., 2023) or AudioLDM/AudioLDM2 (Liu et al., 2023; 2024) illustrate powerful generative methods, but they target orthogonal tasks—generation, separation, or localized editing—rather than unified restoration. In contrast, SonicMaster uniquely addresses comprehensive multi-artifact music restoration and mastering through a single controllable rectified-flow architecture, bridging dereverberation, declipping, tonal rebalancing, dynamics, and stereo enhancement under prompt guidance.

In the absence of text-conditioned music-restoration data, we build a new large-scale corpus for controllable restoration. From $\approx 580$ $k$ Jamendo recordings, we retain $\approx 25$ $k$ high-quality 30-s segments, balanced across 10 genre groups by production quality score. Each clean clip is corrupted with one to three of 19 common effects drawn from five categories—EQ, dynamics, reverb, amplitude, and stereo—producing paired degraded versions. Every degraded sample is accompanied by a natural-language prompt describing the artifact or required fix, and all random effect parameters are stored as metadata. This genre-diverse collection of tens of thousands of prompt–audio pairs underpins SonicMaster training and offers a rigorous benchmark for controllable music-restoration research. Our main contributions are as follows:

- •
We introduce SonicMaster, the first *flow-matching* model to simultaneously address $19$ common degradations, including reverb, EQ imbalance, clipping, dynamic range errors, and stereo artifacts in a *single* generative framework, eliminating sequential processing and cascading error.
- •
Our SonicMaster enables precise user control through natural language conditioning, allowing targeted corrections (e.g., reduce hollow room sound for dereverberation) while maintaining autonomous operation when prompts are unavailable, bridging automated and user-directed restoration paradigms.
- •
We construct and release(^1^11https://github.com/AMAAI-Lab/SonicMaster)the first text-conditioned music-restoration corpus: $25$k high-fidelity Jamendo segments spanning 10 genres, each paired with $7$ degraded versions, detailed metadata, and a natural-language instruction describing the required fix, resulting in $175$k audio pairs.

## 2 Related Work

Restoring and mastering audio spans speech and music enhancement, audio inpainting, and source separation—areas that have mostly been handled separately (Záviška et al., 2020). Diffusion-based generative models and text-guided audio editing (Hou et al., 2025; Jiang et al., 2025; Zhang et al., 2024; Manor & Michaeli, 2024) now let us tackle these problems together. We review these advances, their uses, and the gaps that SonicMaster aims to fill.
Early audio restoration efforts typically focused on single domains or isolated tasks, addressing issues like noise, clipping, or reverb in separation. Speech enhancement (Yousif & Mahmmod, 2025) and music enhancement evolved largely independently, and tasks such as audio inpainting or source separation were treated with specialized methods (Lemercier et al., 2025).

#### Audio Inpainting, Mixing and Declipping:

Early signal-model and interpolation methods could patch only very short gaps ($<10$ ms), leaving longer dropouts unresolved. Deep generative models now bridge that gap: diffusion-based systems convincingly regenerate missing music sections and clipped peaks (Moliner & Välimäki, 2023). The authors in (Wang et al., 2023; Liu et al., 2023) extend this with instruction-guided diffusion for audio inpainting, while VoiceFixer (Liu et al., 2021) jointly denoises, dereverbs, and declipse speech, though it is restricted to voice and does not offer user control. In music, (Imort et al., 2022) removed heavy guitar distortion (including clipping) with neural networks, surpassing sparse-optimization baselines in quality and speed. (Lee et al., 2024) introduce a pruning approach to recover sparse audio effect chains from mixed recordings, essentially reverse-engineering mixing graphs from input/output pairs. Alongside works like (Bhandari et al., 2025) on iterative corruption refinement, (Steinmetz et al., 2021) on differentiable mixing consoles and (Martínez-Ramírez et al., 2022) on out-of-domain mixing generalization, and diffusion restorers such as MaskSR (Li et al., 2024), these highlight emerging methods that bridge audio restoration with controllable, interpretable effect modeling.

#### Equalization and Tonal Restoration:

Research on learning-based equalization is still emerging. In recently proposed work (Mockenhaupt et al., 2024), the authors introduce CNN-based approach to automatically equalize instrument stems by predicting parametric EQ settings, showing improvements over earlier heuristic methods. Notably, the VoiceFixer (Liu et al., 2021) addressed bandwidth extension, essentially restoring high-frequency content as part of its speech restoration, which can be seen as a form of equalization correction. Similarly, diffusion-based restorers like MaskSR (Li et al., 2024) treat low-frequency muffling as a distortion to fix, using discrete token prediction to restore a balanced spectrum.
In Text2FX (Chu et al., 2025), CLAP (Elizalde et al., 2023) is used in inference mode to steer the parameters of EQ and reverb audio effects.

## 3 Method

### 3.1 Dataset

In the absence of text-conditioned music restoration datasets, we generate SonicMaster dataset by pairing high-quality audio with systematically applied degradations and corresponding natural language instructions.
Our source comprises songs from (Roy et al., 2025) and additional content from Jamendo(^2^22[https://www.jamendo.com/](https://www.jamendo.com/)) under Creative Commons licence using the official Jamendo API. In total, we have sourced $580$k recordings. We ensure balanced genre representation by defining $10$ groups, where each group consists of multiple semantically related genre tags, e.g., Hip-Hop genre group containing the following tags: ”rap”, ”hiphop”, ”trap”, ”alternativehiphop”, ”gangstarap”. Complete taxonomies are provided in the Appendix. Track selection employs Audiobox Aesthetics toolbox (Tjandra et al., 2025) for automated production quality assessment. We select $2,500$ songs per genre groups using adaptive production quality thresholds ranging from $6.5$ to $8$ to balance comprehensive sub-genre representation with sufficient production quality. We extract random 30s excerpts from each track, positioned between $15$% and $85$% of its total duration. The complete pipeline is illustrated in Figure [1](https://arxiv.org/html/2508.03448v2#S3.F1). We train SonicMaster by applying $19$ distinct degradations to the audio and pairing each with a matching natural language editing instruction. The degradations span five classes: (i) EQ, (ii) Dynamics, (iii) Reverb, (iv) Amplitude, and (v) Stereo.

Figure: Figure 1: SonicMaster dataset creation pipeline and overview.
Refer to caption: Figures/sonicmaster_detailed_pipeline.drawio.png

Equalization (EQ): Spectral degradations cover $10$ effects targeting perceptual audio characteristics: Brightness, Darkness, Airiness, Boominess, Muddiness, Warmth, Vocals, Clarity, Microphone, and X-band. Brightness, Darkness, Airiness, Boominess, and Warmth are emulated with low- or high-shelf EQ; Clarity with a Butterworth low-pass filter; Vocals and Muddiness with Chebyshev-II band-pass filters. Microphone applies one of $20$ Poliphone transfer functions (Salvi et al., 2025), while X-band uses an $8$–$12$-band, logarithmically spaced peaking EQ with $\textpm 6$ dB gain per band.

Dynamics: Temporal envelope modification via two functions: Compression (feedforward dynamic range compression) and Punch (transient shaping). Both exhibit lossy, non-invertible characteristics, rendering exact restoration mathematically ill-posed and requiring learned approximations.

Reverb: The Reverb category contains four distinct approaches: three of them utilise the Pyroomacoustics library (Scheibler et al., 2018), which simulates acoustic environments with the image source method. We simulate three types of rooms: Small, Big, and Mixed. For our fourth Reverb function, we utilize 12 selected room impulse responses from the openAIR library dataset (Howard & Angus, n.d.), which give us audio with more real-life properties. The resulting impulse responses from all the functions are convoluted with the clean signals.

Amplitude: Two complementary degradations target signal amplitude: Clipping/Volume. Clipping introduces hard nonlinear distortion by constraining peak amplitudes to predefined thresholds; Volume reduction attenuates signals to near-inaudible levels, degrading the signal-to-quantization-noise ratio and simulating poor recording practices.

Stereo: A function to de-stereo the audio recording – tracks undergo stereo content analysis via left-right channel difference standard deviation (threshold: 0.08); qualifying recordings are converted to monophonic by channel summation, simulating poor mixing or playback equipment limitations.

Each ground truth yields $7$ corrupted variants: $4$ with a single, $2$ with double, and $1$ with triple degradation. In multi-degradation, we sample at most one effect from each of the $5$ categories, so an EQ choice, for instance, blocks further EQ picks. To avoid duplicates in the single-degradation set, high-probability effects with narrow parameter ranges: Stereo, Clipping, and Punchare, are used only once. Each degradation is linked to a one-sentence instruction randomly selected from 8–10 expert-written options; these sentences are concatenated into the full prompt, and we store two prompt variants per clip for robustness. We also record every applied effect and its parameters (gain, absorption), supporting tasks such as parameter prediction. For Compression and Reverb, there is a $15$% chance of injecting “hidden clipping” with no corresponding instruction. When neither hidden clipping nor an Amplitude effect is present, the audio is peak-normalised to a random level between $0.8-1.0$. Further details can be found in Appendix.

### 3.2 SonicMaster Architecture

SonicMaster employs a hybrid architecture combining Multimodal Diffusion Transformer (MM-DiT)(Esser et al., 2024) blocks with subsequent Diffusion Transformers (DiT) layers (Peebles & Xie, 2023). As outlined in Figure [2](https://arxiv.org/html/2508.03448v2#S3.F2), stereo waveforms (44.1 kHz) ($x_{t}$) undergo VAE ecoding (Evans et al., 2024) into compact spectro-temporal latent representations. Restoration, therefore, occurs entirely in this learned space, allowing large receptive fields without sample-level overhead. The MM-DiT processes degraded latent representations alongside the text embeddings from a frozen FLAN-T5 encoder (Chung et al., 2024). The resulting conditioned representations pass through subsequent DiT layers to predict flow velocity $v_{t}$, steering the latent toward its clean target $\hat{x}_{t}$. Prompts like ”reduce reverb” biases this prediction trajectory to suppress decay tails, while the downstream DiT layersrefine musical coherence. A pooled-audio branch, active in $25$% of training cases, concatenates a temporally averaged $5–15$s clean cue with the pooled prompt embedding and injects it at every MM-DiT/DiT layer, enabling seamless chaining of $30$s segments for long-form generation while degrading gracefully when no reference is supplied.

Figure: Figure 2: Overall architecture of SonicMaster.
Refer to caption: Figures/SonicMaster.drawio.png

#### Audio and text encoding:

We adopt the Stable Audio Open VAE (Evans et al., 2024) to encode–decode stereo signals sampled at 44.1 kHz, yielding a compact latent representation while retaining high-fidelity reconstruction. Text instructions are embedded with FLAN-T5 Large (Chung et al., 2024); the resulting tensor $c_{\text{text}}\in\mathbb{R}^{B\times S_{\text{text}}\times D_{\text{text}}}$ (with $D_{\text{text}}=1024$) is used as a conditioning signal.

#### Rectified Flow Training:

SonicMaster employs rectified flow (Liu et al., 2022; Esser et al., 2024), to predict flow velocity from degraded to clean audio in latent space, unlike other models that map noise to output distributions (Fei et al., 2024; Hung et al., 2024).

We assign timestep $t=1$ to the latent representation of the degraded audio $x_{1}$, and $t=0$ to the latent representation of the clean audio target $x_{0}$. During training, we feed the model with samples $x_{t}$, which are linear interpolations between degraded input $x_{1}$ and clean target $x_{0}$:

$$ $x_{t}=tx_{1}+(1-t)x_{0}$ (1) $$

where timestep $t$ is drawn from a skewed distribution $p(t)=0.5U(t)+t,\quad t\in[0,1]$ with increasing probability for higher $t$, where $U$ represents a uniform distribution. This skewed distribution gives emphasis to more degraded inputs given the interpolation of training data in Eq. [1](https://arxiv.org/html/2508.03448v2#S3.E1). The model is trained to predict the flow velocity $v_{t}$ from the current $x_{t}$ to the target clean audio $x_{0}$: $v_{t}=-\frac{dx_{t}}{dt}=x_{0}-x_{1}$.
The model $f_{\theta}$ with parameters $\theta$ estimates the velocity $\hat{v_{t}}$, $f_{\theta}(x_{t},t,c_{text})=\hat{v_{t}}$, where $c_{text}$ is the text condition from the FLAN-T5 model, which is passed to the dual-stream MM-DiT blocks as one of the streams. The $c_{text}$ condition is also passed through a pooled projection and used to control the scale and shift factors of the adaptive layer-norm layers in both MM-DiT and DiT blocks. The training loss is then given as:

$$ $L(\theta)=\mathbb{E}_{t,x_{1},x_{0}}||\hat{v_{t}}-v_{t}||_{2}^{2}=\mathbb{E}_{t,x_{1},x_{0}}||f_{\theta}(x_{t},t,c_{text})-v_{t}||_{2}^{2}$ (2) $$

Inference transforms degraded audio input $x_{1}$ to clean audio output $x_{0}$ by integrating the predicted velocity $\hat{v_{t}}$ using the forward Euler method: $x_{t-h}=x_{t}+h\hat{v_{t}}$, where $h\in[0,1]$ is the step computed as the inverse of the total timesteps dedicated for integration.

Inference:
During inference, SonicMaster takes in an audio input and a text instruction given by the user to perform the desired restoration/mastering operation. Inference is possible without text input in the so-called auto-correction mode.
To process full-length songs, SonicMaster operates on chunks of 30s and then connects the segments together.
After the first segment is inferred, the last 10s of this output are used to condition the next segment inference through the audio pooling branch.
The overlapping regions of the resulting segments are then linearly interpolated over the overlapping 10s to connect the segments together.

## 4 Experimental Setup and Baselines

### 4.1 Baselines and training setup

We train SonicMaster using 5 NVIDIA L40S GPUs for 40 epochs with a total batch size of 80. We adopt classifier-free guidance (Ho & Salimans, 2022) by (i) dropping the text prompt in $10$% of samples and (ii) replacing it in another $10$% with one of four generic phrases (“Make it sound better!”, “Master this track for me, please!”, “Improve this!”, “Can you improve the sound of this song?”). In $25$% of cases, the model is additionally conditioned—via the pooling branch—on the first 10 s of clean audio. Unless stated otherwise, all experiments follow these conditioning settings while comparing multiple SonicMaster variants and baselines.

We compare against recent approaches, alongside ablation studies for different SonicMaster configurations:: (i) Degraded input—the original corrupted audio; (ii) Reconstructed input—the same audio passed through the VAE encoder–decoder; (iii) Text2FX-EQ, an EQ baseline using Text2FX (Chu et al., 2025) with 600 iterations and a 0.01 learning rate to correct EQ degradations via our prompts; (iv) WPE dereverberation, the Weighted Prediction Error algorithm (Nakatani et al., 2010) with a prediction order of 30; (v) HPSS dereverberation, harmonic–percussive source separation (librosa.decompose.hpss) with 6 dB and 12 dB harmonic attenuation; (vi) Mel2Mel + DiffWave (Kandpal et al., 2022) framework that treats mel-spectrogram enhancement as an image-to-image translation followed by diffusion vocoding for music restoration. and (vii) three SonicMaster variants—SonicMaster${}_{\text{{Small}}}$ (2 MM-DiT + 6 DiT), SonicMaster${}_{\text{{Medium}}}$ (4 MM-DiT + 12 DiT *or* 6 MM-DiT + 6 DiT), and SonicMaster${}_{\text{{Large}}}$ (6 MM-DiT + 18 DiT).

**Table 1: EQ Objective evaluation (average absolute error) – the lower, the better.**
| Model (MMDiT/DiT) | Clarity | Boom | Airy | Bright | Dark | Muddy | Warm | Vocals | Microphone | X-band |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| \rowcolorgroupgray      Snippet degraded input |  |  |  |  |  |  |  |  |  |  |
| Degraded input | 0.0238 | 0.3601 | 0.0049 | 0.0143 | 0.0893 | 0.4560 | 0.4345 | 0.2525 | 0.2393 | 0.1782 |
| Reconstructed input | 0.0243 | 0.3717 | 0.0051 | 0.0151 | 0.0728 | 0.4749 | 0.4456 | 0.2525 | 0.2379 | 0.1854 |
| Mel2Mel + Diffwave Kandpal et al. (2022) | 0.0278 | 0.3561 | 0.0049 | 0.0135 | 0.0855 | 0.4705 | 0.4436 | 0.256 | 0.2604 | 0.1885 |
| Text2FX-EQ (Chu et al., 2025) | 0.0219 | 0.3809 | 0.0055 | 0.0276 | 0.2112 | 0.3651 | 0.4955 | 0.2199 | 0.4441 | 0.3419 |
| SonicMasterSmall (2/6) | 0.0100 | 0.0819 | 0.0020 | 0.0064 | 0.0060 | 0.0477 | 0.0590 | 0.0630 | 0.0122 | 0.0408 |
| SonicMasterMedium (4/12) | 0.0105 | 0.0698 | 0.0021 | 0.0067 | 0.0061 | 0.0400 | 0.0592 | 0.0602 | 0.0091 | 0.0383 |
| SonicMasterMedium (6/6) | 0.0225 | 0.2766 | 0.0020 | 0.0067 | 0.0056 | 0.1718 | 0.1737 | 0.2417 | 0.0462 | 0.0762 |
| SonicMasterLarge (6/18) | 0.0114 | 0.0834 | 0.0019 | 0.0059 | 0.0058 | 0.0388 | 0.0617 | 0.0576 | 0.0088 | 0.0358 |
| -w 5s Audio Cond. | 0.0111 | 0.0716 | 0.0021 | 0.0061 | 0.0058 | 0.0386 | 0.0605 | 0.0628 | 0.0124 | 0.0387 |
| -w 15s Audio Cond. | 0.0117 | 0.0750 | 0.0020 | 0.0064 | 0.0063 | 0.0320 | 0.0552 | 0.0525 | 0.0079 | 0.0398 |
| -w/o Audio Cond. (basic) | 0.0099 | 0.0658 | 0.0021 | 0.0064 | 0.0056 | 0.0352 | 0.0595 | 0.0746 | 0.0097 | 0.0434 |
| -w Cond. During Infer | 0.0115 | 0.0840 | 0.0019 | 0.0060 | 0.0058 | 0.0389 | 0.0610 | 0.0572 | 0.0088 | 0.0355 |
| -w/o Text Cond. | 0.0130 | 0.1432 | 0.0032 | 0.0101 | 0.0086 | 0.0448 | 0.0841 | 0.0668 | 0.0154 | 0.0424 |
| \rowcolorgroupgray      Fullsong degraded input |  |  |  |  |  |  |  |  |  |  |
| Degraded Input | 0.0290 | 0.3231 | 0.0048 | 0.0124 | 0.0983 | 0.4606 | 0.4810 | 0.2274 | 0.2403 | 0.1737 |
| SonicMasterLarge (6/18) | 0.0102 | 0.0639 | 0.0021 | 0.0060 | 0.0065 | 0.0329 | 0.0510 | 0.0517 | 0.0070 | 0.0289 |

## 5 Results

### 5.1 Objective Evaluation

Degradation-Specific Performance: Tables [4.1](https://arxiv.org/html/2508.03448v2#S4.SS1) and [5.1](https://arxiv.org/html/2508.03448v2#S5.SS1) demonstrates SonicMaster’s superiority over baselines of Text2FX in EQ, and WPE/HPSS in Reverb. SonicMaster improves in all categories when compared to the degraded and reconstructed inputs. Furthermore, the reconstructed input metrics are overall slightly worse (with exceptions) than those of the ground truth degraded inputs.

**Table 2: Objective scores: Reverb, Dynamics, Amplitude, and Stereo. Clip are multiplied by 1000.**
| Model (MMDiT/DiT) | Reverb | Dynamics | Amplitude | Stereo |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Small | Big | Mix | Real | Compressor | Punch | Clip | Volume |  |  |
| \rowcolorgroupgray      Snippet degraded input |  |  |  |  |  |  |  |  |  |
| Degraded input | 0.4457 | 0.4243 | 0.5045 | 0.4639 | 0.0496 | 0.1200 | 5.122 | 0.1813 | 0.4183 |
| Reconstructed input | 0.4686 | 0.4507 | 0.5433 | 0.4908 | 0.0494 | 0.0590 | 3.871 | 0.1810 | 0.4181 |
| HPSS 6 dB | 0.4419 | 0.4240 | 0.4970 | 0.4537 | - | - | - | - | - |
| HPSS 12 dB | 0.4971 | 0.4739 | 0.5333 | 0.4814 | - | - | - | - | - |
| WPE (Nakatani et al., 2010) | 0.4849 | 0.4732 | 0.5207 | 0.4854 | - | - | - | - | - |
| Mel2Mel + Diffwave (Kandpal et al., 2022) | 0.4404 | 0.4387 | 0.4361 | 0.4368 | - | - | - | - | - |
| SonicMasterSmall (2/6) | 0.3812 | 0.3826 | 0.4050 | 0.3277 | 0.0172 | 0.0859 | 2.363 | 0.0457 | 0.1536 |
| SonicMasterMedium (4/12) | 0.3683 | 0.3700 | 0.3934 | 0.3138 | 0.0147 | 0.0891 | 2.455 | 0.0409 | 0.1028 |
| SonicMasterMedium (6/6) | 0.3952 | 0.3916 | 0.4422 | 0.4255 | 0.0366 | 0.0833 | 2.905 | 0.1228 | 0.4180 |
| SonicMasterLarge (6/18) | 0.3663 | 0.3726 | 0.3935 | 0.3109 | 0.0193 | 0.0871 | 1.506 | 0.0468 | 0.1058 |
| -w 5s Audio Cond. | 0.3717 | 0.3658 | 0.3919 | 0.3079 | 0.0164 | 0.0893 | 1.779 | 0.0430 | 0.0918 |
| -w 15s Audio Cond. | 0.3676 | 0.3682 | 0.3901 | 0.3093 | 0.0172 | 0.0895 | 1.633 | 0.0485 | 0.1008 |
| -w/o Audio Cond. During Training | 0.3620 | 0.3682 | 0.3888 | 0.3067 | 0.0146 | 0.0850 | 2.055 | 0.0455 | 0.1015 |
| -w Cond. During inference | 0.3664 | 0.3724 | 0.3934 | 0.3112 | 0.0172 | 0.0870 | 1.455 | 0.0412 | 0.1060 |
| -w/o Text Cond. | 0.3732 | 0.3805 | 0.4012 | 0.3264 | 0.0157 | 0.0730 | 2.812 | 0.0465 | 0.1416 |
| \rowcolorgroupgray      Full song degraded input |  |  |  |  |  |  |  |  |  |
| Degraded input | 0.3667 | 0.3654 | 0.4706 | 0.3852 | 0.0598 | 0.1103 | 6.363 | 0.1829 | 0.4133 |
| SonicMasterLarge (6/18) | 0.3954 | 0.4511 | 0.4191 | 0.4066 | 0.0258 | 0.1101 | 3.734 | 0.0424 | 0.0850 |

### 5.2 Ablation Studies

Audio Conditioning Duration: We evaluated SonicMasterLarge with different conditioning lengths (5s, 10s, 15s), finding comparable performance across configurations. The 10-second setting balances computational efficiency with temporal overlap for long-form processing.

Conditioning Strategy Analysis: The no-conditioning variant achieves optimal Boom correction (0.0658 absolute error) but poor Clip restoration (2.055 vs. standard variants), highlighting multi-modal guidance importance for challenging tasks.

Text Prompt Dependency:Inference without text prompts maintains comparable FAD, SSIM, and PQ but shows degraded KL divergence (0.917 vs. 0.696). Critical drops occur in Clip restoration (2.812 vs. 1.506) and Stereo processing (0.1416 vs. 0.1058), with elevated EQ errors. This confirms text conditioning enables targeted restoration rather than generic improvements.

**Table 4: Comparison of mean across metrics CE, CU, PC, and PQ.**
| Method | CE $\uparrow$ | CU $\uparrow$ | PC $\uparrow$ | PQ $\uparrow$ |
| --- | --- | --- | --- | --- |
| Original | $6.94_{\pm 0.48}$ | $7.29_{\pm 0.43}$ | $3.45_{\pm 0.36}$ | $6.70_{\pm 0.50}$ |
| LTAS-EQ | $6.77_{\pm 0.54}$ | $7.04_{\pm 0.57}$ | $3.75_{\pm 0.45}$ | $6.49_{\pm 0.57}$ |
| BEHM-GAN | $6.82_{\pm 0.43}$ | $7.19_{\pm 0.44}$ | $3.47_{\pm 0.35}$ | $6.63_{\pm 0.56}$ |
| BABE | $\textbf{6.96}_{\pm 0.37}$ | $\textbf{7.32}_{\pm 0.37}$ | $3.32_{\pm 0.29}$ | $6.79_{\pm 0.36}$ |
| BABE-2 | $6.79_{\pm 0.34}$ | $7.16_{\pm 0.29}$ | $3.46_{\pm 0.28}$ | $\textbf{7.05}_{\pm 0.27}$ |
| SonicMaster | $6.87_{\pm 0.55}$ | $7.25_{\pm 0.50}$ | $\textbf{3.86}_{\pm 0.39}$ | $6.93_{\pm 0.52}$ |

Long-Form Audio Evaluation: Full-song evaluations confirm SonicMaster’s effectiveness, with substantial improvements in EQ-related metrics (Table [4.1](https://arxiv.org/html/2508.03448v2#S4.SS1)) and most degradation functions (Table [5.1](https://arxiv.org/html/2508.03448v2#S5.SS1)). Reverberation metrics show mixed results, likely due to increased complexity of spatial processing in extended musical contexts where room acoustics interact with diverse instrumental timbres and dynamic variations. SSIM and FAD decrease compared to degraded inputs, except for FAD in multi-degradation samples, indicating SonicMaster’s ability to handle compound degradations.

### 5.3 Piano Recordings Evaluation

To test SonicMaster generalization, we evaluate historical solo piano pieces(^3^33http://research.spa.aalto.fi/publications/papers/dafx-babe2/) using established baselines: LTAS-EQ, BEHM-GAN (Moliner & Välimäki, 2023) model for bandwidth extension, and BABE/BABE-2 diffusion-based generative equalizers (Moliner et al., 2024; Moliner & Välimäki, 2023). BABE-2 represents a state-of-the-art specialized method for old recordings, it uses a diffusion prior to restore lost high frequencies and remove coloration, and has shown impressive improvements in archival music (Moliner et al., 2024). Despite lacking domain-specific training, SonicMaster came surprisingly close to these specialized baselines. In objective evaluations, SonicMaster restored samples achieved a PQ of $6.93$, nearly matching the $7.05$ obtained by BABE-2.

**Table 5: Listening study - SonicMaster’s performance on specific degradations – MOS 95% CI.**
| Category | Text relevance | Quality1 | Quality2 | Consistency | Preference |
| --- | --- | --- | --- | --- | --- |
| EQ | $5.11_{\pm 0.55}$ | $4.55_{\pm 0.44}$ | $5.01_{\pm 0.52}$ | $5.05_{\pm 0.42}$ | $4.84_{\pm 0.49}$ |
| Reverb | $5.59_{\pm 0.57}$ | $4.25_{\pm 0.68}$ | $5.28_{\pm 0.57}$ | $5.31_{\pm 0.54}$ | $5.27_{\pm 0.75}$ |
| Dynamics | $4.56_{\pm 0.80}$ | $4.09_{\pm 0.54}$ | $5.00_{\pm 0.81}$ | $5.00_{\pm 0.84}$ | $4.91_{\pm 1.01}$ |
| Amplitude | $6.19_{\pm 0.66}$ | $3.66_{\pm 0.43}$ | $5.38_{\pm 0.77}$ | $5.59_{\pm 0.81}$ | $5.75_{\pm 0.84}$ |
| Stereo | $5.50_{\pm 1.05}$ | $4.62_{\pm 0.83}$ | $5.50_{\pm 0.63}$ | $5.38_{\pm 0.70}$ | $5.25_{\pm 0.77}$ |
| Mixed degradations | $5.28_{\pm 0.51}$ | $3.85_{\pm 0.88}$ | $4.60_{\pm 0.59}$ | $4.65_{\pm 0.62}$ | $4.93_{\pm 0.57}$ |

### 5.4 Subjective Evaluation

Table [5](https://arxiv.org/html/2508.03448v2#S5.T5) shows results of the first subjective evaluation test. Text relevance ratings are highest in the Amplitude (6.19) and Reverb categories (5.59), indicating effective declipping, volume increase, and dereverberation. These two categories also show the highest improvement in quality, as well as the highest consistency and preference ratings.
EQ shows the fourth-best text relevance, but the worst preference ratings. Overall, SonicMaster samples are rated higher in quality compared to inputs and preferred across the board.
A paired $t$-test on Quality1 and Quality2 ratings shows statistically significant differences in Reverb, Amplitude, Stereo, and mixed degradations.

**Table 6: Listening study - comparative evaluation results - 13 participants × 10 samples.**
| Category | SonicMaster | Mel2Mel + Diffwave ^(Kandpal et al., 2022) | Text2FX ^(Chu et al., 2025) |
| --- | --- | --- | --- |
| Reverberation | 128/130 (98.5%) | 2/130 (1.5%) | — |
| EQ (X-band) | 122/130 (93.8%) | 6/130 (4.6%) | 2/130 (1.5%) |

The comparative evaluation against existing baselines demonstrates SonicMaster’s superior performance across both reverb and EQ degradation categories (Table [6](https://arxiv.org/html/2508.03448v2#S5.T6)). For reverb artifacts, participants overwhelmingly preferred SonicMaster over Mel2Mel + Diffwave (Kandpal et al., 2022), selecting our method in 128 out of 130 total comparisons (10 samples × 13 participants), with Mel2Mel + Diffwave chosen only twice. In the EQ category, SonicMaster achieved similarly strong results with 122 out of 130 preferences, while Mel2Mel + Diffwave received 6 votes and Text2FX (Chu et al., 2025) garnered 2 votes. These results underscore SonicMaster’s effectiveness in addressing both spatial acoustic degradations and spectral imbalances compared to specialized baseline methods.

## 6 Discussion

Experiments confirm that SonicMaster’s generative approach is effective when trained on a large corpus with a suitable objective. The historical piano experiment demonstrated SonicMaster’s strong generalization: even on out-of-domain, severely degraded audio, it produced enhancements close to the best specialized solution, BABE-2. This highlights the potential of general-purpose audio restoration AI. However, a key limitation is that the lossy latent representation can introduce artifacts, such as robotic vocals or muted instruments, especially in certain genres. The observed decrease in SonicMaster’s performance on full songs in SSIM and Reverb metrics could be related to the way neighbouring segments are connected together. Improving on this aspect could increase the objective performance further. Evaluating reverberation in dense music is challenging, and how SonicMaster removes it in latent space is not explicitly observable, making metric selection difficult. A deeper study of this issue would benefit the community.

## 7 Conclusion

We introduced SonicMaster, the first unified text-guided generative model for music restoration and mastering, capable of handling 19 diverse degradations within a single framework. Our contributions further include the creation of a paired degraded–clean dataset with textual annotations, the introduction of a flow-matching paradigm for directly learning restoration mappings, and the integration of natural language conditioning for precise and flexible control. Evaluations show that SonicMaster consistently improves the audio quality, outperforming baselines in terms of objective metrics and listener studies. It also achieved strong zero-shot performance on old piano recordings, highlighting its versatility suggesting a path toward a generalist restoration framework–one capable of addressing diverse challenges through prompt guidance while approaching the quality of specialist methods.

## Acknowledgements

This work has received support from SUTD’s Kickstart Initiative under grant number SKI 2021 04 06, MOE Tier 2 under grant number MOE-T2EP20124-0014, and SMU-SUTD grant number 2023_02_04.

## References

- Bhandari et al. (2025)
Keshav Bhandari, Sungkyun Chang, Tongyu Lu, Fareza R Enus, Louis B Bradshaw, Dorien Herremans, and Simon Colton.
Improvnet–generating controllable musical improvisations with iterative corruption refinement.
*arXiv preprint arXiv:2502.04522*, 2025.
- Chu et al. (2025)
Annie Chu, Patrick O’Reilly, Julia Barnett, and Bryan Pardo.
Text2fx: Harnessing clap embeddings for text-guided audio effects.
In *ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 1–5. IEEE, 2025.
- Chung et al. (2024)
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al.
Scaling instruction-finetuned language models.
*Journal of Machine Learning Research*, 25(70):1–53, 2024.
- Deruty & Tardieu (2014)
Emmanuel Deruty and Damien Tardieu.
About dynamic processing in mainstream music.
*Journal of the Audio Engineering Society*, 62(1/2):42–55, 2014.
- Dormand & Prince (1980)
John R Dormand and Peter J Prince.
A family of embedded runge-kutta formulae.
*Journal of computational and applied mathematics*, 6(1):19–26, 1980.
- Elizalde et al. (2023)
Benjamin Elizalde, Soham Deshmukh, Mahmoud Al Ismail, and Huaming Wang.
Clap learning audio concepts from natural language supervision.
In *ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 1–5. IEEE, 2023.
- Esser et al. (2024)
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al.
Scaling rectified flow transformers for high-resolution image synthesis.
In *Forty-first international conference on machine learning*, 2024.
- Evans et al. (2024)
Zach Evans, CJ Carr, Josiah Taylor, Scott H Hawley, and Jordi Pons.
Fast timing-conditioned latent audio diffusion.
In *Forty-first International Conference on Machine Learning*, 2024.
- Fei et al. (2024)
Zhengcong Fei, Mingyuan Fan, Changqian Yu, and Junshi Huang.
Flux that plays music.
*arXiv:2409.00587*, 2024.
- Ho & Salimans (2022)
Jonathan Ho and Tim Salimans.
Classifier-free diffusion guidance.
*arXiv:2207.12598*, 2022.
- Hou et al. (2025)
Siyuan Hou, Shansong Liu, Ruibin Yuan, Wei Xue, Ying Shan, Mangsuo Zhao, and Chao Zhang.
Editing music with melody and text: Using controlnet for diffusion transformer.
In *ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 1–5, 2025.
doi: 10.1109/ICASSP49660.2025.10890309.
- Howard & Angus (n.d.)
David M. Howard and James A. S. Angus.
Open acoustic impulse response (open air) library.
[https://www.openair.hosted.york.ac.uk/](https://www.openair.hosted.york.ac.uk/), n.d.
Accessed: 2025-07-06.
- Hung et al. (2024)
Chia-Yu Hung, Navonil Majumder, Zhifeng Kong, Ambuj Mehrish, Amir Ali Bagherzadeh, Chuan Li, Rafael Valle, Bryan Catanzaro, and Soujanya Poria.
Tangoflux: Super fast and faithful text to audio generation with flow matching and clap-ranked preference optimization.
*arXiv:2412.21037*, 2024.
- Imort et al. (2022)
Johannes Imort, Giorgio Fabbro, Marco A Martínez Ramírez, Stefan Uhlich, Yuichiro Koyama, and Yuki Mitsufuji.
Distortion audio effects: Learning how to recover the clean signal.
*arXiv:2202.01664*, 2022.
- Jiang et al. (2025)
Xilin Jiang, Cong Han, Yinghao Aaron Li, and Nima Mesgarani.
Listen, chat, and remix: Text-guided soundscape remixing for enhanced auditory experience.
*IEEE Journal of Selected Topics in Signal Processing*, 2025.
- Kandpal et al. (2022)
Nikhil Kandpal, Oriol Nieto, and Zeyu Jin.
Music enhancement via image translation and vocoding.
In *ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 3124–3128. IEEE, 2022.
- Lee et al. (2024)
Sungho Lee, Marco A Martínez-Ramírez, Wei-Hsiang Liao, Stefan Uhlich, Giorgio Fabbro, Kyogu Lee, and Yuki Mitsufuji.
Searching for music mixing graphs: A pruning approach.
*arXiv preprint arXiv:2406.01049*, 2024.
- Lemercier et al. (2025)
Jean-Marie Lemercier, Julius Richter, Simon Welker, Eloi Moliner, Vesa Välimäki, and Timo Gerkmann.
Diffusion models for audio restoration: A review [special issue on model-based and data-driven audio signal processing].
*IEEE Signal Processing Magazine*, 41(6):72–84, 2025.
- Li et al. (2024)
Xu Li, Qirui Wang, and Xiaoyu Liu.
Masksr: Masked language model for full-band speech restoration.
*arXiv:2406.02092*, 2024.
- Liu et al. (2021)
Haohe Liu, Qiuqiang Kong, Qiao Tian, Yan Zhao, DeLiang Wang, Chuanzeng Huang, and Yuxuan Wang.
Voicefixer: Toward general speech restoration with neural vocoder.
*arXiv:2109.13731*, 2021.
- Liu et al. (2023)
Haohe Liu, Zehua Chen, Yi Yuan, Xinhao Mei, Xubo Liu, Danilo Mandic, Wenwu Wang, and Mark D Plumbley.
Audioldm: Text-to-audio generation with latent diffusion models.
In *International Conference on Machine Learning*, pp. 21450–21474. PMLR, 2023.
- Liu et al. (2024)
Haohe Liu, Yi Yuan, Xubo Liu, Xinhao Mei, Qiuqiang Kong, Qiao Tian, Yuping Wang, Wenwu Wang, Yuxuan Wang, and Mark D Plumbley.
Audioldm 2: Learning holistic audio generation with self-supervised pretraining.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, 32:2871–2883, 2024.
- Liu et al. (2022)
Xingchao Liu, Chengyue Gong, and Qiang Liu.
Flow straight and fast: Learning to generate and transfer data with rectified flow.
*arXiv:2209.03003*, 2022.
- Manor & Michaeli (2024)
Hila Manor and Tomer Michaeli.
Zero-shot unsupervised and text-based audio editing using ddpm inversion.
*arXiv preprint arXiv:2402.10009*, 2024.
- Martínez-Ramírez et al. (2022)
Marco A Martínez-Ramírez, Wei-Hsiang Liao, Giorgio Fabbro, Stefan Uhlich, Chihiro Nagashima, and Yuki Mitsufuji.
Automatic music mixing with deep learning and out-of-domain data.
*arXiv preprint arXiv:2208.11428*, 2022.
- Melechovsky et al. (2024)
Jan Melechovsky, Zixun Guo, Deepanway Ghosal, Navonil Majumder, Dorien Herremans, and Soujanya Poria.
Mustango: Toward controllable text-to-music generation.
In *Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*, pp. 8286–8309, 2024.
- Mockenhaupt et al. (2024)
Florian Mockenhaupt, Joscha Simon Rieber, and Shahan Nercessian.
Automatic equalization for individual instrument tracks using convolutional neural networks.
*arXiv:2407.16691*, 2024.
- Moliner & Välimäki (2023)
Eloi Moliner and Vesa Välimäki.
Diffusion-based audio inpainting.
*arXiv:2305.15266*, 2023.
- Moliner & Välimäki (2023)
Eloi Moliner and Vesa Välimäki.
Behm-gan: Bandwidth extension of historical music using generative adversarial networks.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, 31:943–956, 2023.
doi: 10.1109/TASLP.2022.3190726.
- Moliner et al. (2024)
Eloi Moliner, Filip Elvander, and Vesa Välimäki.
Blind audio bandwidth extension: A diffusion-based zero-shot approach.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, 2024.
- Mourgela et al. (2024)
Angeliki Mourgela, Elio Quinton, Spyridon Bissas, Joshua D Reiss, and David Ronan.
Exploring trends in audio mixes and masters: Insights from a dataset analysis.
*arXiv:2412.03373*, 2024.
- Nakatani et al. (2010)
Tomohiro Nakatani, Takuya Yoshioka, Keisuke Kinoshita, Masato Miyoshi, and Biing-Hwang Juang.
Speech dereverberation based on variance-normalized delayed linear prediction.
*IEEE Transactions on Audio, Speech, and Language Processing*, 18(7):1717–1731, 2010.
- Peebles & Xie (2023)
William Peebles and Saining Xie.
Scalable diffusion models with transformers.
In *Proceedings of the IEEE/CVF international conference on computer vision*, pp. 4195–4205, 2023.
- Roy et al. (2025)
Abhinaba Roy, Renhang Liu, Tongyu Lu, and Dorien Herremans.
Jamendomaxcaps: A large scale music-caption dataset with imputed metadata.
*Proceedings of IJCNN*, 2025.
- Salvi et al. (2025)
Davide Salvi, Daniele Ugo Leonzio, Antonio Giganti, Claudio Eutizi, Sara Mandelli, Paolo Bestagini, and Stefano Tubaro.
Poliphone: A dataset for smartphone model identification from audio recordings.
*IEEE Access*, 2025.
- Scheibler et al. (2018)
Robin Scheibler, Eric Bezzam, and Ivan Dokmanić.
Pyroomacoustics: A python package for audio room simulation and array processing algorithms.
In *2018 IEEE international conference on acoustics, speech and signal processing (ICASSP)*, pp. 351–355. IEEE, 2018.
- Steinmetz et al. (2021)
Christian J Steinmetz, Jordi Pons, Santiago Pascual, and Joan Serrà.
Automatic multitrack mixing with a differentiable mixing console of neural audio effects.
In *ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 71–75. IEEE, 2021.
- Tjandra et al. (2025)
Andros Tjandra, Yi-Chiao Wu, Baishan Guo, John Hoffman, Brian Ellis, Apoorv Vyas, Bowen Shi, Sanyuan Chen, Matt Le, Nick Zacharov, et al.
Meta audiobox aesthetics: Unified automatic quality assessment for speech, music, and sound.
*arXiv:2502.05139*, 2025.
- Wang et al. (2023)
Yuancheng Wang, Zeqian Ju, Xu Tan, Lei He, Zhizheng Wu, Jiang Bian, et al.
Audit: Audio editing by following instructions with latent diffusion models.
*Advances in Neural Information Processing Systems*, 36:71340–71357, 2023.
- Wilson & Fazenda (2016)
Alex Wilson and Bruno M Fazenda.
Perception of audio quality in productions of popular music.
*Journal of the Audio Engineering Society*, 64(1/2):23–34, 2016.
- Yousif & Mahmmod (2025)
Sally Taha Yousif and Basheera M Mahmmod.
Speech enhancement algorithms: A systematic literature review.
*Algorithms*, 18(5):272, 2025.
- Yuan et al. (2025)
Yi Yuan, Xubo Liu, Haohe Liu, Mark D Plumbley, and Wenwu Wang.
Flowsep: Language-queried sound separation with rectified flow matching.
In *ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, pp. 1–5. IEEE, 2025.
- Zang et al. (2025)
Yongyi Zang, Zheqi Dai, Mark D Plumbley, and Qiuqiang Kong.
Music source restoration.
*arXiv:2505.21827*, 2025.
- Záviška et al. (2020)
Pavel Záviška, Pavel Rajmic, Alexey Ozerov, and Lucas Rencker.
A survey and an extensive evaluation of popular audio declipping methods.
*IEEE Journal of Selected Topics in Signal Processing*, 15(1):5–24, 2020.
- Zhang et al. (2024)
Yixiao Zhang, Yukara Ikemiya, Gus Xia, Naoki Murata, Marco A Martínez-Ramírez, Wei-Hsiang Liao, Yuki Mitsufuji, and Simon Dixon.
Musicmagus: zero-shot text-to-music editing via diffusion models.
In *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, pp. 7805–7813, 2024.

## Appendix A Appendix

### A.1 The Use of Large Language Models

We employed a Large Language Model to assist with reducing wordy paragraphs to help the paper fit in the page limit.

### A.2 Genre tags

We grouped genre tags into genre groups, as depicted in Table [7](https://arxiv.org/html/2508.03448v2#A1.T7).
Each row links a coarse “Group” label—such as Rock, Electronic, or Jazz/Blues—to the fine-grained “Genre tags” that appear in the metadata. These tags enumerate substyles (e.g., progressiverock, deephouse, acidjazz), which allows us to aggregate diverse representations inside each of the genre groups.

**Table 7: Genre groupings by metadata tags used in our dataset.**
| Group | Genre tags |
| --- | --- |
| Rock | rock, alternativerock, poprock, classicrock, hardrock, progressiverock, stoner, psychedelicrock, garage, indierock |
| Pop | pop, electropop, dancepop, dance, alternativepop, adultcontemporary, indiepop |
| Electronic | electronic, house, techno, trance, edm, electrohouse, deephouse, progressivehouse, electroswing, synthwave, electronica |
| Hip-Hop | rap, hiphop, trap, alternativehiphop, gangstarap |
| Folk | folk, singersongwriter, americana, country, bluegrass, folklore |
| Metal | metal, deathmetal, blackmetal, thrashmetal, heavymetal, numetal, metalcore, hardcore, alternativemetal, doommetal |
| World | world, latin, reggaeton, afrobeat, african, indian, oriental, celtic, salsa, flamenco, jpop, middleeastern, asian, reggae |
| Jazz/Blues | jazz, blues, funk, acidjazz, jazzfusion, smoothjazz, jazzfunk, soul, swing, rnb, alternativernb |
| Chill | ambient, downtempo, chillout, chillhop, lofi, newage, darkambient, triphop, chillwave, idm, dreampop |
| Classical | classical, filmscore, neoclassical, symphonic, opera, baroque, medieval, avantgarde, production, choral |

### A.3 Degradation functions

To create the SonicMaster dataset, we used a set of 19 degradation functions. The details of their implementation and parameter range are described in Table [8](https://arxiv.org/html/2508.03448v2#A1.T8). Each of the groups, and subsequently each of the functions inside the groups, have their own probabilities/weights to be picked in our data creation pipeline. These are documented in Table [9](https://arxiv.org/html/2508.03448v2#A1.T9).

**Table 8: Detailed description of degradation functions used to create our dataset.**
| Degradation group | Degradation type | Description | Prompt example (inverse) |
| --- | --- | --- | --- |
| EQ | X-band EQ | Apply 8 to 12 band parametric EQ with $-6$ to $+6$ range for each band. | Correct the unnatural frequency emphasis. |
| Microphone transfer function | Convolve the audio with one of 20 phone microphone transfer functions. | Reduce the coloration added by the microphone. |  |
| Brightness | Reduce brightness using a high-shelf filter at 6 kHz by 6–15 dB. | Give the mix more shine and sparkle. |  |
| Darkness | Increase perceived brightness with a high-shelf filter at 6 kHz by 6–15 dB. | Make the tone fuller and less sharp. |  |
| Airiness | Reduce airiness via a high-shelf filter at 10 kHz by 10–20 dB. | Add more air and openness to the sound. |  |
| Boominess | Reduce boominess with a low-shelf filter at 120 Hz by 10–20 dB. | Give the audio more roar and low-end power. |  |
| Clarity | Degrade clarity using a Butterworth low-pass filter (order 3–5) with cutoff at 2 kHz. | Increase the clarity of this song by emphasizing treble frequencies. |  |
| Muddiness | Increase muddiness with a 2nd-order Chebyshev Type II bandpass (200–500 Hz) by 6–15 dB. | Make the mix sound less boxy and congested. |  |
| Warmth | Reduce warmth with a low-shelf filter at 400 Hz by 6–20 dB. | Make the sound warmer and more inviting. |  |
| Vocals | Attenuate vocal-range frequencies using a 2nd-order Chebyshev Type II bandpass (350–3500 Hz) by 6–20 dB. | Make the vocals stand out more. |  |
| Dynamics | Compression | Apply a feedforward compressor with attack 3–80 ms, release 80–250 ms, threshold $-45$ to $-38$ dB, ratio 6–45, and make-up gain 16–25 dB. | Let the audio breathe more and improve the dynamics. |
| Punch | Apply a feedforward transient shaper with attack 3 ms, release 150 ms, adaptive threshold, and reduction of 8–15 dB. | Add more impact and dynamic punch to the sound. |  |
| Reverb | Small room | Convolve with Pyroomacoustics simulated IR: room size $(7\text{--}15,8\text{--}18,4\text{--}14)$ m, absorption coefficient 0.05–0.30. | Clean this off any echoes! |
| Big room | Convolve with Pyroomacoustics IR: room size $(4\text{--}8,4\text{--}7,2.5\text{--}3.5)$ m, 1–2 absorptive walls, frequency-dependent absorption. | Can you remove the excess reverb in this audio, please? |  |
| Mixed material room | Convolve with Pyroomacoustics IR: room size $(3\text{--}7,3\text{--}9,2.5\text{--}4)$ m, absorption coefficient 0.05–0.30. | Remove excess reverb and make it sound cleaner. |  |
| Real RIR | Apply one of twelve real impulse responses from the openAIR library. | Please, reduce the strong echo in this song. |  |
| Amplitude | Clipping | Modify the audio level to a maximum amplitude of {2,3,5} and apply clipping. | Reduce the clipping and reconstruct the lost audio, please. |
| Volume | Adjust the audio gain to a maximum amplitude of {0.001, 0.003, 0.01, 0.05}. | Enhance the loudness without distorting the signal. |  |
| Stereo | Stereo | Combine the left and right channels to erase the spatial image. | Add depth and separation between left and right. |

**Table 9: Degradation groups with assigned probabilities and option weights.**
| Group (weight) | Option | Probability / Weight |
| --- | --- | --- |
| EQ (0.4) | xband | 7.0 |
| mic | 5.0 |  |
| bright | 3.0 |  |
| dark | 3.0 |  |
| airy | 2.0 |  |
| boom | 2.0 |  |
| clarity | 3.0 |  |
| mud | 3.0 |  |
| warm | 3.0 |  |
| vocal | 4.0 |  |
| Dynamics (0.125) | comp | 2.5 |
| punch | 1.0 |  |
| Reverb (0.225) | small | 0.15 |
| big | 0.15 |  |
| mix | 0.30 |  |
| real | 0.40 |  |
| Amplitude (0.125) | clip | 3.0 |
| volume | 1.0 |  |
| Stereo (0.125) | stereo | 1.0 |

Figure: (a) Original (clean).
Refer to caption: Figures/spectrograms/mic1_orig.png

### A.4 Spectrogram examples

We visualize time–frequency structure in spectrograms to provide qualitative evidence of restoration behavior. Each figure shows the clean reference, the degraded input (e.g., reverberation-induced smearing or clipping distortion), and the output of SonicMaster. Figures [3](https://arxiv.org/html/2508.03448v2#A1.F3), [4](https://arxiv.org/html/2508.03448v2#A1.F4), [5](https://arxiv.org/html/2508.03448v2#A1.F5), [6](https://arxiv.org/html/2508.03448v2#A1.F6), [7](https://arxiv.org/html/2508.03448v2#A1.F7), and [8](https://arxiv.org/html/2508.03448v2#A1.F8) compare clean, degraded, and restored spectrograms across selected scenarios (reverb, clipping, microphone transfer function, and clarity EQ).

Figure: (a) Ground truth audio.
Refer to caption: Figures/spectrograms/reverb1d_orig.png

Figure: (a) Original (clean).
Refer to caption: Figures/spectrograms/reverb1d_orig.png

Figure: (a) Original (clean).
Refer to caption: Figures/spectrograms/clarity1_orig2.png

Figure: (a) Original (clean).
Refer to caption: Figures/spectrograms/clip1_orig.png

Figure: (a) Original (clean).
Refer to caption: Figures/spectrograms/clip2_original.png

**Table 10: EQ Objective evaluation (average absolute error) – the lower, the better.**
| Model (MMDiT/DiT) | Clarity | Boom | Airy | Bright | Dark | Muddy | Warm | Vocals | Microphone | X-band |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| \rowcolorgroupgray      Snippet degraded input |  |  |  |  |  |  |  |  |  |  |
| SonicMasterLarge (6/18) | 0.0114 | 0.0834 | 0.0019 | 0.0059 | 0.0058 | 0.0388 | 0.0617 | 0.0576 | 0.0088 | 0.0358 |
| -w Euler $1$ Step | 0.0100 | 0.1146 | 0.0019 | 0.0059 | 0.0061 | 0.0425 | 0.0668 | 0.0498 | 0.0141 | 0.0384 |
| -w Euler $100$ Steps | 0.0136 | 0.1540 | 0.0033 | 0.0100 | 0.0091 | 0.0540 | 0.0915 | 0.0749 | 0.0162 | 0.0444 |
| -w Runge-Kutta $10$ Steps | 0.0120 | 0.0810 | 0.0019 | 0.0058 | 0.0058 | 0.0402 | 0.0630 | 0.0590 | 0.0083 | 0.0374 |

### A.5 Ablation on ODE Solvers

We evaluated Euler solvers with $1$, $10$ (baseline), and $100$ steps, plus a $10$-step 4th order Runge–Kutta (Dormand & Prince, 1980) solver. Tables [A.4](https://arxiv.org/html/2508.03448v2#A1.SS4), [A.4](https://arxiv.org/html/2508.03448v2#A1.SS4), and [A.4](https://arxiv.org/html/2508.03448v2#A1.SS4) outline the results and highlight the trade-off across degradation categories. Euler-1 matches the baseline overall but is weaker on Boom, Microphone, Clip, all Reverb subtasks, and shows higher KL. Euler-100 boosts Reverb and Punch yet lowers every EQ score versus the 1-/10-step runs. Runge–Kutta-10 equals Euler-10 on most metrics and tops Clip, but its inference is significantly slower.

### A.6 Prompts for each degradation type

Prompt instructions for each degradation type are grouped by audio attribute in Table [13](https://arxiv.org/html/2508.03448v2#A1.T13); for example, entries for Xband, microphone coloration, clarity, brightness, darkness, airiness, boominess, warmth, muddiness, vocals, compression, punch, reverb, volume, clipping, and stereo give natural-language commands that steer the restoration model. These instructions act as conditioning signals—e.g., “remove excess reverb and make it sound cleaner,” “raise the level of the vocals,” or “make this sound brighter”—so that the generative restoration trajectory emphasizes or suppresses specific signal characteristics.

**Table 13: User instructions grouped by audio attribute.**
| Attribute | Example Instructions |
| --- | --- |
| Xband | Can you please correct the equalization?; Improve the balance in the audio by fixing the chaotic equalizer, please.; Make this sound balanced, please.; Balance the EQ, please.; Balance the tonal spectrum of the audio.; Correct the unnatural frequency emphasis.; Make the EQ curve smoother and more natural.; Even out the EQ.; Adjust the tonal balance for a more pleasing sound. |
| Microphone | This audio was recorded with a phone, can you fix that, please?; Please make this sound better than a phone recording.; Balance the EQ, please.; Improve the balance in this song.; Make the audio sound like it was recorded with a higher-quality microphone.; Reduce the coloration added by the microphone.; Make the tone more neutral and balanced.; Improve the naturalness of the recording.; Remove the harshness or boxiness from the mic coloration. |
| Clarity | Increase the clarity!; Can you please make this song sound more clear?; Increase the clarity of this song by emphasizing treble frequencies.; Make the audio clearer and more intelligible.; Sharpen the overall sound.; Bring more focus and definition to the details.; Make the mix sound less cloudy.; Tighten the articulation in the sound. |
| Brightness | Can you please make this sound brighter?; Increase the brightness!; Make this audio sound brighter by emphasizing the high frequencies.; Add some brightness to the high end.; Make the sound more vivid and lively.; Give the mix more shine and sparkle.; Lift the treble for a more open tone.; Enhance the presence of the upper frequencies. |
| Darkness | Make this sound darker!; Can you reduce the brightness, please?; Make the audio darker by suppressing the higher frequencies.; Bring in more low-mid richness to make the sound darker.; Make the tone fuller and less sharp.; Smooth out the highs with deeper low-end support.; Round out the sound with more body.; Soften the harshness with a warmer tone. |
| Airiness | Make this sound more fresh and airy by emphasizing the high end frequencies.; Make this feel more airy, please.; Increase the perceived airiness, please.; Give this a light sense of spaciousness by amplifying the higher frequencies.; Add more air and openness to the sound.; Make the audio feel more spacious and extended.; Enhance the sense of space in the highs.; Lift the top end for a more open character.; Give the mix a breathier, more open feel. |
| Boominess | Make it boom!; Make this song sound more boomy by amplifying the low end bass frequencies.; Increase the boominess, please!; Give me more bass!; Can you make this more bassy, please?; Give the audio more roar and low-end power.; Make the bass more impactful and solid.; Add weight and depth to the bottom end.; Reinforce the low frequencies for more energy.; Boost the bass presence. |
| Warmth | Can you make this song sound warmer, please?; Increase the warmth, please.; Emphasize the bass and low-mid frequencies to give this a more warm feel.; Make the sound warmer and more inviting.; Add some low-mid warmth to the mix.; Soften the tone with a bit more body.; Give the audio a warm analog feel.; Enhance the warmth for a fuller sound. |
| Muddiness | Can you make this song sound less muddy, please?; Decrease the muddiness!; Reduce the level of muddiness in this audio by lowering the low-mid frequencies.; Clean up the muddiness in the low-mids.; Make the mix sound less boxy and congested.; Improve definition by reducing mud.; Clear up the low-mid buildup.; Make the audio tighter and less murky. |
| Vocals | Raise the level of the vocals, please.; Can you amplify the vocals, please?; Emphasize the vocals by raising the level of the mid frequencies specific for vocals.; Bring the vocals forward in the mix.; Make the voice clearer and more present.; Increase the vocal presence by enhancing the midrange.; Make the vocals stand out more.; Strengthen the vocal clarity and focus. |
| Compression | Increase the dynamic range.; Decompress the audio, please.; Remove the compression, please.; Can you fix the strong compression effect in this audio by expanding the dynamic range?; Restore the dynamics of the audio.; Make the sound less squashed and more open.; Reduce the over-compression for a more natural feel.; Bring back the contrast in volume.; Let the audio breathe more and improve the dynamics. |
| Punch | Give this song a punch!; Make the transients sharper, please.; Increase the punchiness of the song by emphasizing the transients.; Make the audio more punchy and energetic.; Bring back the snap and attack of transients.; Add more impact and dynamic punch to the sound.; Make drums and hits sound more aggressive and tight.; Increase the percussive clarity and definition. |
| Reverb | Can you remove the excess reverb in this audio, please?; Please, dereverb this audio.; Remove the echo!; Please, reduce the strong echo in this song.; Remove the church effect, please.; Clean this off any echoes!; This song has too much reverb present, can you reduce it?; Make the audio sound more dry and direct.; Reduce the roominess or echo.; Remove excess reverb and make it sound cleaner.; Bring the sound closer and more focused.; Tighten the spatial feel of the audio. |
| Volume | The volume is low, make this louder please!; Can you make this sound louder, please?; Increase the amplitude.; Normalize the audio volume.; Make the audio louder and more powerful.; Increase the overall level.; Boost the volume without distorting the signal. |
| Clipping | This audio is clipping, can you please remove it?; Remove the loud hissing in this song?; Remove the clipping.; Reduce the clipping and reconstruct lost audio.; Clean up noisiness.; Make the audio smoother and less distorted.; Reduce the gritty or crushed character.; Fix digital distortion. |
| Stereo | Make it sound spacious!; Can you make this audio stereo, please?; Alter left/right channels to give spatial feel.; Widen the stereo image.; Add depth and separation between left and right.; Enhance the stereo field for immersive sound. |

## Peer Reviews

confidence: 5
contribution: 2
initial_rating: 4
presentation: 4
questions: 1. Can you include AUDIT (might be very similar) and MusicMagus, or any other recent audio editing models as baselines re-trained under your data and prompts? Even if limited to key operators (EQ/reverb/stereo), this would ground the comparison in modern generative-base editing models.

2. How sensitive is the system to wording? For example, if prompts underspecify targets (e.g., “reduce reverb”), how are some metrics like gain/RT60 change decided?

3. Can you evaluate on real studio/home recordings with annotated fixes to demonstrate transfer beyond synthetic degradations? And Also can you provide some results of out-of-domain prompts (from the vague one "make the music overall sounds better" to a specific non-trained one "remove the distortion")?

4. Is there any architecture-wise novelty you would like to emphasize compared to previous editing models? Or does the task definition novelty and the data simulation yield most contributions of the paper?
soundness: 2
strengths: The strengths of this paper lie in two parts.

1. Task Novelty. The paper draws a clear line between music restoration/mastering and adjacent areas (e.g., source separation, generation, enhancement), and argues for a single controllable model that handles multiple artifact types prompted by text instruction in an editing mode. This framing is insightful and latest for Audio AI, which has recently focused on diffusion/RFM generation and instruction-guided editing.

2. The analysis on the degradation taxonomy and the corresponding solution on simulation are well-specified. The dataset design is methodical: 19 degradations across five groups with explicit implementations, parameter ranges, and prompt templates. These definitions drive both training and evaluation, creating consistent interfaces between what’s degraded and what the model should fix. The model choice, conditional modules design, and experimental designs are well-organized. The rectified-flow-matching model is sensible for enhancement and editing in a generative manner. And the text conditioning via FLAN-T5 and audio pooling branch for segment stitching are practical, reproducible design decisions. 

3. The experimental design is comprehensive. Ablation studies cover audio-text conditioning, sequence length, and model scale variants. Beyond global fidelity (FAD, KL, SSIM, PQ), the authors compute targeted artifact metrics and run two subjective listening test. This breadth reflects the ambition of authors to address the task in multiple angles.
summary: The paper proposes a unified, text-guided generative system for controllable music restoration and mastering. The proposed model, SonicMaster, attempts to correct a broad set of music degradations in EQ, dynamics, reverb, amplitude, and stereo, using a rectified-flow model built with MM-DiT + DiT blocks operating in a VAE latent space. Evaluation includes degradation-aware metrics, global fidelity measures, and subjective listening tests, demonstrating that SonicMaster generally improves degradation inputs and is preferred by listeners against prior baselines.
weaknesses: However, the paper has three critical weaknesses that are worth discussing during the rebuttal process.

1. Despite the task novelty, the methodological novelty at the architecture/learning level is thin for a top-tier AI conference. Specifically, the model uses known blocks (a.k.a., VAE + MM-DiT/DiT + text encoder; rectified-flow objective; and classifier-free guidance), but does not propose new conditional interfaces (e.g., operator-aware adapters, band-mask tokens, time-varying schedules) nor new inference-time optimization strategies that specifically target enhancement failure modes in music context.

2. Despite the comprehensive experimental design, the comparisons to baselines miss close peers in instructional audio editing. Baselines emphasize classic signal-processing (WPE, HPSS) and a mel-spec enhancement pipeline (Mel2Mel+DiffWave) + Text2FX (EQ only). However, some advanced models such as AUDIT and MusicMagus, and more related instruction-guided models are only mentioned in passing without reporting results on implementations under the same data and prompts. This limits claims about data simulation effectiveness and model advantage over modern generative-based editing models. While category-specific metrics are valuable, several are proxy measures that can disagree with perception in dense mixes. Listening tests are helpful but small (8 for MOS; 13 for pairwise study). Generalization beyond the simulation (e.g., real recording faults, overall enhancement) remains under-explored to see if the model contains great generalization capabilities to understand "what is a good music quality" after learning millions of simulations in music degradation.

3. The demos in the website are not attractive and reflect some mis-leading in the optimization target.In the paper’s own analysis, reverb metrics on full-length songs are mixed, and SSIM/FAD sometimes decrease relative to inputs. But stereo and some EQ categories do not consistently improve. The observations in the demo website reinforce this: (1) de-reverberation sometimes adds tail energy rather than removing it (4th, 6th, 12th examples); (2) stereo edits can devolve into delays rather than proper image widening (9th, 13th examples); and (3) EQ correction can miss the target on more complex mixes (3st examples). (4) only simple operations such as leveling and bandwidth extensions perform relatively effective, while prior generative works can also lead the performance and this should also require a comparison to them. These gaps suggest simulation-to-real mismatch and insufficient inductive bias in the conditioning to constrain physically plausible edits. 

4. Another minor comment on the data quality. Although the dataset is nominally sampled at 44.1 kHz, many training and evaluation tracks appear to have effective bandwidths only up to 32–36 kHz, showing striping artifacts that are typical of MP3 compression. This limits the model’s exposure to true full-band audio and its ability to restore or generate high-frequency details (e.g., brightness, air spot, and spatial shimmer). A controlled high-band test or uncompressed dataset evaluation is needed to confirm performance on the real full-band music.

---

confidence: 4
contribution: 4
initial_rating: 8
presentation: 4
questions: 1. Would it be possible to include a subjective evaluation of the ground truth for comparison?
soundness: 4
strengths: The paper introduces the first unified model for prompt-based multi-artifact music restoration, addressing 19 types of degradations via flow-matching and classifier-free guidance. This unified formulation represents a significant step toward scalable, controllable, and generalizable audio restoration.
summary: This paper presents SonicMaster, a unified generative framework for controllable music restoration and mastering. The model addresses nineteen common degradation types (e.g., reverb, clipping, EQ imbalance) with a flow-matching architecture guided by natural-language prompts. It introduces a self-constructed text-conditioned dataset of degraded–clean audio pairs for training. Experiments show clear advantages over existing systems in both objective metrics and human listening tests.
weaknesses: 1.  Objective evaluation has some counter-intuitive metric behavior. For example, in Snippet single degradation in Table 3, SonicMaster's best FAD ($0.069$) and SSIM ($0.625$) scores are less favorable than the Degraded Input's FAD ($0.061$) and SSIM ($0.838$). 

2. The model exhibits a clear performance drop in full-song inference compared with snippet in most metrics across Table 2 and Table 3.

3. Subjective evaluations (Table 5 and 6) do not include a quality rating for the Ground Truth (clean audio). The absolute quality rating in Table 5 does not include any SOTA baseline models for direct comparison.

---

confidence: 4
contribution: 4
initial_rating: 6
presentation: 3
questions: 1. Does the performance hold on real world scenario where the recording is more complex?

2. How much the proposed model sensitive to wording in the text conditioning? 

3. What is the latency and the memory usage of the proposed model? Does the model size has trade off with the quality?
soundness: 4
strengths: 1. The proposed SonicMaster is a single generative model that can handles a lots of artifact with guided text which is very practical solution 

2. The dataset design is well organised, with 19 degradations type across five groups - EQ, dynamics, reverb, amplitude, stereo and also instruction alignment. 

3. The paper used the well known Rectified Flow architecture with MM-DiT dual-stream that condition with FLAN-T5. 

4. The results of the proposed architecture is very good compare to pervious works which can be seen in the multiple metrics - FAD, KL, SSIM and PQ
summary: The paper proposed a neural network called SonicMaster which is an unified generative model for music restoration and mastering. The proposed architecture is conditioned on text with instructions.
In order to train the model, SonicMaster, they build a dataset which contain paired degraded and high-quality audio examples by simulating them with nineteen degradation functions types.

The proposed architecture use flow-matching generative model that maps the input and the text prompts into the wanted audio file. The proposed model show improved sound quality across categories which also evaluate with human listeners.
weaknesses: 1. The human evaluation has very low amount of participants - only 13, more participants can strength the results

2. The results on EQ has the weakest performance among all categories which may be results of a problem of balancing 

3. The proposed network trained only on synthetic data which may limit the results on real world data.

---

confidence: 5
contribution: 2
initial_rating: 2
presentation: 2
questions: - why only use 1 type of effect for multi-degradation? This doesn’t make much sense for linear transformations (e.g., EQ, Stereo), since a single transformation could achieve the same result.
    - have the authors tried on multi-degradations on different degradations?
- line 183: “expert-written options” → explanations?
- line 185: any validation on the task of “parameter prediction”?
- line 186: what is “hidden clipping” and why is this added and needed?
- line 187: what is the purpose of “peak-normalisation” and how is it computed?
soundness: 2
strengths: - The paper’s attempt to combine multiple restoration tasks within a single controllable model is an interesting and ambitious direction, particularly when paired with natural-language conditioning.
- The proposed dataset covers up to 19 degradation types across 5 categories, with textual prompts and metadata. If released, this dataset could become a valuable benchmark for controllable music-restoration research.
- The multimodal aspect represents a step toward human-interpretable control over restoration on different degradations, which is a relevant trend for both research and creative workflows.
summary: This paper presents a text-conditioned generative model for music restoration. It addresses diverse degradations using a single rectified-flow architecture that maps degraded inputs to restored versions guided by natural-language prompts. To train the model, the authors construct a dataset, where each data is degraded by simulated effects and paired with corresponding textual descriptions.
weaknesses: **Ill-defined task formulation:** 

The paper lacks a precise definition of what constitutes a “degraded” versus “clean” distribution. The chosen degradation types: EQ, dynamics, reverb, amplitude, stereo are actually very standard operations during music mastering procedure rather than authentic degradations. This conceptual framing blurs the line between restoration and stylized mastering.

- For instance, Figures 7 & 8 waveforms resemble legitimate mastered outputs of modern music rather than degraded signals.
- From the reviewer’s understanding, the target “clean” distribution appears to be the Audiobox-filtered Jamendo subset, but the assumption that this represents the true clean reference is not justified clearly in the manuscript.
- The paper does not ensure non-overlap between degraded and clean distributions, which undermines the validity of “restoration” as the learning objective.
- Many realistic degradations (e.g., environmental or background noise, bandwidth reduction, codec artifacts) are absent, further questioning whether the task genuinely represents *restoration*.

**Choice and fairness of baselines:**

WPE and HPSS (from 2010) are outdated and not meaningful as “recent” baselines. The paper omits several directly relevant systems, such as (but not limited to):

- BABE-2 (for historical bandwidth extension),
- Music Source Restoration (MSR) (Zang et al., 2025),
- general purpose audio effects removal models or multi-effect restoration approaches [1]
    
    Consequently, the reported improvements may largely reflect baseline weakness rather than true superiority.
    

**Evaluation clarity and presentation:**

- Tables 1 and 2 mix different metrics across degradations without explicitly listing metric definitions; equations or references should be provided for each metric.
- Large tables are difficult to interpret—boldface patterns are inconsistent, and averages across metrics (from Table 1 and 2) are missing.
- The comparison with Text2FX is conceptually unfair: Text2FX is a prompt-optimization framework, not a restoration model targeting a fixed “clean” reference. If the authors used the same text prompt as the proposed, I’d say this is not a fair comparison as the objective is different.

**Insufficient analysis of results:**

The paper reports extensive quantitative results but little discussion or visualization to explain which aspects of the signal actually improve. The subjective results (≈ 8 listeners) are too small to draw strong conclusions. I would show only the results that you would highlight and provide more discussions. It’s hard to catch each ablation method’s purpose.

**Overall impression:**

The direction is promising, but the core task definition, baseline choice, and experimental analysis require substantial clarification before the work meets ICLR standards.

**Papers worth mentioning:**

[1] Rice, Matthew, et al. "General purpose audio effect removal." *2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)*. IEEE, 2023.

[2] Han, Bing, et al. "InstructME: An Instruction Guided Music Edit Framework with Latent Diffusion Models.” IJCAI, 2024

**Minor comments:**

- for some citations, instead use \cite{} or \citet{} than \citep{}. this will remove the brackets and be more natural to read
- line 136: correct the quotation marks: “ → `` (this also happens in other parts of the manuscript)
- typo @line 182: Punchare → Punch
- typo @line 200: layersrefine →layers refine
- I’d recommend placing the tables on the top of the page for better presentation; on the proper page. The evaluation and results section especially lack significant readability.
- Table 4 is never referred in the text
- no bolden performance on Table 1: Bright and Vocals, Table 2: Dynamics and Clip, Table 3: Single deg’s PQ, and Table 5


Decision: Reject