arxiv: 2604.09803 · v1 · submitted 2026-04-10 · 💻 cs.SD

Recognition: unknown

MAGE: Modality-Agnostic Music Generation and Editing

Muhammad Usama Saleem , Tejasvi Ravi , Tianyu Xu , Rajeev Nongpiur , Ishan Chatterjee , Mayur Jagdishbhai Patel , Pu Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:24 UTC · model grok-4.3

classification 💻 cs.SD

keywords music generationmultimodal conditioningaudio editingflow-based transformermodality maskingcross-modal alignmentlatent trajectories

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The pith

MAGE lets one model generate and edit music from any combination of text, visuals, or existing audio mixtures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents MAGE as a single framework that handles both creating music from high-level multimodal cues and making targeted edits to existing audio tracks. Most current systems are locked to one task or one fixed set of inputs, which breaks down when cues are missing, misaligned, or incomplete. MAGE instead uses a flow-based transformer that learns controllable paths in latent space, aligns visual evidence to the audio timeline, and applies multiplicative gating from text and visuals to suppress unsupported content. It trains by randomly masking modalities so the same weights work for text-only, visual-only, joint, or mixture-guided cases. If this holds, musicians gain a lightweight tool that accepts whatever inputs are available without retraining or quality drops.

Core claim

MAGE unifies multimodal music generation and mixture-grounded editing in one continuous latent formulation via a Controlled Multimodal FluxFormer. Audio-Visual Nexus Alignment selects temporally consistent visual evidence, while cross-gated modulation applies multiplicative control from aligned cues to audio latents. A dynamic modality-masking curriculum trains the model on text-only, visual-only, joint multimodal, and mixture-guided settings to support inference under any available subset of conditions.

What carries the argument

The Controlled Multimodal FluxFormer, a flow-based Transformer that learns controllable latent trajectories for synthesis and editing under any subset of input conditions.

If this is right

A single set of weights supports music generation and editing across text-only, visual-only, joint, and mixture-guided inputs.
Multiplicative cross-gated control reduces prompt drift and unsupported content compared with additive fusion methods.
Targeted editing of existing mixtures becomes possible while respecting any available visual or textual guidance.
No additional models are required when one or more modalities are unavailable at inference time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same masking-plus-gating pattern could apply to other generative tasks where users supply partial or changing inputs over time.
Multiplicative modulation may prove more reliable than concatenation for keeping generated content grounded in the strongest available cues.
Workflow tools could let users start with a rough text description, add a video reference midway, and continue without restarting the model.

Load-bearing premise

Dynamic modality masking during training plus cross-gated modulation will produce stable outputs when some inputs are absent or misaligned, without needing separate models or introducing unwanted musical content.

What would settle it

Run the model on the MUSIC benchmark with only text prompts or only misaligned visuals and measure whether output quality drops below the multimodal case or introduces spurious notes and rhythms.

Figures

Figures reproduced from arXiv: 2604.09803 by Ishan Chatterjee, Mayur Jagdishbhai Patel, Muhammad Usama Saleem, Pu Wang, Rajeev Nongpiur, Tejasvi Ravi, Tianyu Xu.

**Figure 1.** Figure 1: Overview of MAGE. A modality-agnostic framework for multimodal music generation and mixture-grounded editing. It aligns heterogeneous conditioning signals (e.g., text and visual cues) through a shared interface. This enables both zero-mixture generation and targeted editing while preserving the audio context. Abstract. Multimodal music creation requires models that can both generate audio from high-level c… view at source ↗

**Figure 2.** Figure 2: Overview of the proposed framework for multimodal music generation and separation. (a) MixWavCodec, a mixture-aware audio codec that learns latent tokens z from audio mixtures and reconstructs waveform signals. (b) Controlled Multimodal FluxFormer, a flow-based generative transformer that enables controllable music generation and source separation from mixtures. The model aligns audio, visual scene cues… view at source ↗

**Figure 3.** Figure 3: Qualitative comparison with state-of-the-art separation methods on diverse time–frequency structures. Each row shows the mixture, reference visual frame, groundtruth magnitude, and MAGE prediction. Unlike mask-regression models, MAGE recovers suppressed components (circled) by modeling conditional generative dynamics rather than deterministic masking. labels to construct text prompts, while video frames … view at source ↗

**Figure 1.** Figure 1: Illustration of Audio–Visual Nexus Alignment (AVNA). Video frames and audio events are sampled at different temporal resolutions, leading to a mismatch between the visual stream and the audio timeline. AVNA resolves this by performing nearestneighbor temporal resampling on normalized timestamps, assigning each audio event to the closest visual observation. The aligned visual sequence therefore matches the… view at source ↗

**Figure 2.** Figure 2: Architecture of the proposed Controlled Multimodal FluxFormer. Each FluxFormer Block applies multi-head self-attention over the latent audio tokens followed by a feedforward layer, with residual connections and layer normalization. Multimodal conditioning is injected through prepended text tokens and cross-gated modulation from aligned visual features. The full backbone stacks N such blocks and injects th… view at source ↗

**Figure 3.** Figure 3: Comparison of multimodal fusion strategies. Left: Additive fusion directly combines audio latents h with projected visual features Wss¯, injecting visual cues uniformly into the audio representation. Middle: Gated residual fusion modulates the visual contribution through a tanh gate before adding it to the audio features. Right: The proposed Cross-Gated Modulation (CGM) applies multiplicative control h⊙σ(W… view at source ↗

read the original abstract

Multimodal music creation requires models that can both generate audio from high-level cues and edit existing mixtures in a targeted manner. Yet most multimodal music systems are built for a single task and a fixed prompting interface, making their conditioning brittle when guidance is ambiguous, temporally misaligned, or partially missing. Common additive fusion or feature concatenation further weakens cross-modal grounding, often causing prompt drift and spurious musical content during generation and editing. We propose MAGE, a modality-agnostic framework that unifies multimodal music generation and mixture-grounded editing within a single continuous latent formulation. At its core, MAGE uses a Controlled Multimodal FluxFormer, a flow-based Transformer that learns controllable latent trajectories for synthesis and editing under any available subset of conditions. To improve grounding, we introduce Audio-Visual Nexus Alignment to select temporally consistent visual evidence for the audio timeline, and a cross-gated modulation mechanism that applies multiplicative control from aligned visual and textual cues to the audio latents, suppressing unsupported components rather than injecting them. Finally, we train with a dynamic modality-masking curriculum that exposes the model to text-only, visual-only, joint multimodal, and mixture-guided settings, enabling robust inference under missing modalities without training separate models. Experiments on the MUSIC benchmark show that MAGE supports effective multimodal-guided music generation and targeted editing, achieving competitive quality while offering a lightweight and flexible interface tailored to practical music workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MAGE unifies music generation and editing in one latent space with gating and a masking curriculum, but the abstract gives no numbers or ablations to show the mechanisms actually prevent drift or handle missing modalities reliably.

read the letter

MAGE puts both generation from multimodal cues and targeted editing of mixtures into a single continuous latent formulation. The main technical pieces are the Controlled Multimodal FluxFormer, Audio-Visual Nexus Alignment to keep visual evidence timed to the audio, cross-gated modulation that multiplies control signals into the audio latents, and a dynamic modality-masking curriculum that trains on text-only, visual-only, joint, and mixture-guided cases together.

Referee Report

3 major / 2 minor

Summary. The paper proposes MAGE, a modality-agnostic framework unifying multimodal music generation and mixture-grounded editing in a single continuous latent formulation. It centers on a Controlled Multimodal FluxFormer that learns controllable trajectories, augmented by Audio-Visual Nexus Alignment for temporal consistency, cross-gated modulation for multiplicative control from aligned cues, and a dynamic modality-masking curriculum to support inference from any subset of text, visual, or mixture conditions while suppressing unsupported content. Experiments on the MUSIC benchmark are claimed to show competitive quality and a lightweight, flexible interface for practical workflows.

Significance. If the robustness and suppression claims hold, MAGE could meaningfully advance practical multimodal music tools by replacing multiple task-specific models with one that handles partial, ambiguous, or misaligned inputs without prompt drift. The emphasis on multiplicative gating over additive fusion and the curriculum-based training addresses a recurring pain point in conditional audio synthesis.

major comments (3)

[§3] §3 (Controlled Multimodal FluxFormer and cross-gated modulation): The central claim that multiplicative gating from aligned visual/textual cues suppresses unsupported components (rather than causing the prompt drift criticized in additive fusion) is load-bearing for the modality-agnostic property, yet no equations, gate computation details, or comparison to additive baselines are supplied to show how suppression is enforced.
[§4] §4 (Experiments on MUSIC benchmark): The assertion of effective multimodal-guided generation, targeted editing, and robust inference under missing modalities lacks any reported ablations, misalignment tests, drift metrics, error bars, or subset-specific results; without these, the claim that the curriculum plus gating enables reliable performance from arbitrary condition subsets cannot be evaluated.
[§3.3] §3.3 (dynamic modality-masking curriculum): The curriculum is presented as the mechanism that eliminates the need for separate models while preventing leakage when cues conflict temporally, but no schedule details, masking probabilities, or comparative performance numbers (full vs. partial modalities) are given to substantiate that it actually produces consistent trajectories.

minor comments (2)

[Abstract] The abstract and method descriptions use terms such as 'targeted editing' and 'mixture-grounded editing' without clarifying the precise editing operations supported or how the latent trajectory formulation differs between generation and editing modes.
Figure or pseudocode illustrating the Audio-Visual Nexus Alignment and cross-gated modulation would help clarify the temporal selection and multiplicative control steps.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments correctly identify areas where additional technical details and experimental evidence are needed to fully support the claims regarding the cross-gated modulation, the dynamic curriculum, and the robustness of multimodal inference. We address each major comment below and will make substantial revisions to the manuscript, including new equations, ablations, and metrics, to strengthen the presentation.

read point-by-point responses

Referee: [§3] §3 (Controlled Multimodal FluxFormer and cross-gated modulation): The central claim that multiplicative gating from aligned visual/textual cues suppresses unsupported components (rather than causing the prompt drift criticized in additive fusion) is load-bearing for the modality-agnostic property, yet no equations, gate computation details, or comparison to additive baselines are supplied to show how suppression is enforced.

Authors: We agree that the suppression mechanism requires explicit mathematical formulation and empirical validation to substantiate the modality-agnostic claims. In the revised manuscript, §3.2 will be expanded with the precise equations for cross-gated modulation: given aligned cues c = concat(v_aligned, t_aligned), the gate is g = σ(MLP(c)) where σ is the sigmoid function, and the modulated latent is z' = z ⊙ g (element-wise multiplication). This design enables suppression by attenuating unsupported dimensions toward zero. We will also add a new ablation study comparing multiplicative gating against additive fusion baselines (z' = z + α·c), reporting quantitative differences in prompt drift (via CLAP alignment deviation) and content suppression on the MUSIC benchmark. revision: yes
Referee: [§4] §4 (Experiments on MUSIC benchmark): The assertion of effective multimodal-guided generation, targeted editing, and robust inference under missing modalities lacks any reported ablations, misalignment tests, drift metrics, error bars, or subset-specific results; without these, the claim that the curriculum plus gating enables reliable performance from arbitrary condition subsets cannot be evaluated.

Authors: The current experimental section indeed lacks the granular analysis required to evaluate the robustness claims. In the revised §4 and supplementary material, we will include: (i) ablations across modality subsets (text-only, visual-only, joint, mixture-guided), (ii) misalignment tests with temporally shifted or conflicting conditions, (iii) drift metrics such as feature consistency scores and perceptual deviation, (iv) error bars from multiple random seeds, and (v) subset-specific tables comparing generation quality and editing precision. These additions will directly demonstrate the effectiveness of the curriculum and gating under partial or ambiguous inputs. revision: yes
Referee: [§3.3] §3.3 (dynamic modality-masking curriculum): The curriculum is presented as the mechanism that eliminates the need for separate models while preventing leakage when cues conflict temporally, but no schedule details, masking probabilities, or comparative performance numbers (full vs. partial modalities) are given to substantiate that it actually produces consistent trajectories.

Authors: We acknowledge that the curriculum description in §3.3 is high-level and requires implementation specifics and supporting numbers. The revised section will detail the schedule: masking probabilities for each modality begin at 0.25 and linearly increase to 0.75 over 80 epochs, with explicit probabilities for text-only (0.3), visual-only (0.3), multimodal (0.2), and mixture (0.2) configurations. We will also add comparative results in §4 showing trajectory consistency (e.g., via latent path smoothness) and performance metrics for full versus partial modality settings, confirming that the curriculum enables consistent behavior without separate models. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architectural proposals are validated on external benchmarks rather than derived by construction

full rationale

The paper introduces MAGE as a proposed framework consisting of a Controlled Multimodal FluxFormer, Audio-Visual Nexus Alignment, cross-gated modulation, and a dynamic modality-masking curriculum. These elements are presented as design choices to address prompt drift and support modality-agnostic inference, with effectiveness demonstrated via experiments on the MUSIC benchmark. No equations, derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the text. The central claims reduce to empirical validation against external data rather than any self-referential reduction or ansatz smuggled via prior work by the same authors. This is the normal case of a methods paper whose derivation chain is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only view prevents identification of concrete free parameters or axioms; the framework implicitly assumes that flow-based latent trajectories can be controllably modulated by aligned multimodal cues without introducing spurious content.

pith-pipeline@v0.9.0 · 5574 in / 997 out tokens · 25793 ms · 2026-05-10T16:24:01.760546+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

34 extracted references · 7 canonical work pages

[1]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Chatterjee, M., Le Roux, J., Ahuja, N., Cherian, A.: Visual scene graphs for audio source separation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1204–1213 (2021)

2021
[2]

In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Chen,B.,Wu,C.,Zhao,W.:Sepdiff:Speechseparationbasedondenoisingdiffusion model. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2023)

2023
[3]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Chen, J., Zhang, R., Lian, D., Yang, J., Zeng, Z., Shi, J.: iquery: Instruments as queries for audio-visual sound separation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14675–14686 (2023)

2023
[4]

In: Proceedings of the ISMIR 2021 Workshop on Music Source Separation (2021)

Défossez, A.: Hybrid spectrogram and waveform source separation. In: Proceedings of the ISMIR 2021 Workshop on Music Source Separation (2021)

2021
[5]

Music source separation in the waveform domain,

Défossez, A., Usunier, N., Bottou, L., Bach, F.: Music source separation in the waveform domain. arXiv preprint arXiv:1911.13254 (2019)

work page arXiv 1911
[6]

In: Proceedings of International Conference on Learning Representations (ICLR) (2023)

Dong, H.W., Takahashi, N., Mitsufuji, Y., McAuley, J., Berg-Kirkpatrick, T.: Clipsep: Learning text-queried sound separation with noisy unlabeled videos. In: Proceedings of International Conference on Learning Representations (ICLR) (2023)

2023
[7]

In: ICASSP 2025-2025 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP)

Dong, J., Wang, X., Mao, Q.: Edsep: An effective diffusion-based method for speech source separation. In: ICASSP 2025-2025 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2025)

2025
[8]

ACM Transactions on Graphics (TOG)37(4), 1–11 (2018)

Ephrat, A., Mosseri, I., Lang, O., Dekel, T., Wilson, K., Hassidim, A., Free- man, W.T., Rubinstein, M.: Looking to listen at the cocktail party: a speaker- independent audio-visual model for speech separation. ACM Transactions on Graphics (TOG)37(4), 1–11 (2018)

2018
[9]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Gan,C.,Huang,D.,Zhao,H.,Tenenbaum,J.,Torralba,A.:Musicgestureforvisual sound separation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10478–10487 (2020)

2020
[10]

In: Proceedings of the European Conference on Computer Vision (ECCV)

Gao, R., Feris, R., Grauman, K.: Learning to separate object sounds by watching unlabeled video. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 35–53 (2018)

2018
[11]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

Gao, R., Grauman, K.: Co-separating sounds of visual objects. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3879– 3888 (2019)

2019
[12]

In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP)

Gemmeke, J.F., Ellis, D.P., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M.: Audio set: An ontology and human-labeled dataset for audio events. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). pp. 776–780. IEEE (2017)

2017
[13]

In: Proceedings of the Asian Conference on Computer Vision (ACCV)

Huang, C., Liang, S., Tian, Y., Kumar, A., Xu, C.: High-quality visually-guided sound separation from diverse categories. In: Proceedings of the Asian Conference on Computer Vision (ACCV). pp. 35–49 (2024)

2024
[14]

arXiv preprint arXiv:2509.22063 (2025)

Huang, C., Liang, S., Tian, Y., Kumar, A., Xu, C.: High-quality sound separation across diverse categories via visually-guided generative modeling. arXiv preprint arXiv:2509.22063 (2025)

work page arXiv 2025
[15]

arXiv preprint arXiv:2305.07447 (2023)

Kong, Q., Chen, K., Liu, H., Du, X., Berg-Kirkpatrick, T., Dubnov, S., Plumb- ley, M.D.: Universal source separation with weakly labelled data. arXiv preprint arXiv:2305.07447 (2023)

work page arXiv 2023
[16]

In: Salakhutdinov, R., Kolter, Z., 16 M

Li, K., Yang, R., Sun, F., Hu, X.: IIANet: An intra- and inter-modality atten- tion network for audio-visual speech separation. In: Salakhutdinov, R., Kolter, Z., 16 M. U. Saleem et al. Heller, K., Weller, A., Oliver, N., Scarlett, J., Berkenkamp, F. (eds.) Proceedings of the 41st International Conference on Machine Learning. Proceedings of Machine Learni...

2024
[17]

In: Proc

Liu, X., Liu, H., Kong, Q., Mei, X., Zhao, J., Huang, Q., Plumbley, M.D., Wang, W.: Separate what you describe: Language-queried audio source separation. In: Proc. Interspeech 2022. pp. 1801–1805 (2022)

2022
[18]

In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Lu, W.T., Wang, J.C., Kong, Q., Hung, Y.N.: Music source separation with band- split rope transformer. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 481–485. IEEE (2024)

2024
[19]

Multi-source diffusion models for simultaneous mu- sic generation and separation,

Mariani, G., Tallini, I., Postolache, E., Mancusi, M., Cosmo, L., Rodolà, E.: Multi- source diffusion models for simultaneous music generation and separation. arXiv preprint arXiv:2302.02257 (2023)

work page arXiv 2023
[20]

In: International conference on machine learning

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PmLR (2021)

2021
[21]

Journal of machine learning research21(140), 1–67 (2020)

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research21(140), 1–67 (2020)

2020
[22]

Rafii, Z., Liutkus, A., Stöter, F.R., Mimilakis, S.I., Bittner, R.: The MUSDB18 cor- pus for music separation (Dec 2017).https://doi.org/10.5281/zenodo.1117372, https://doi.org/10.5281/zenodo.1117372

work page doi:10.5281/zenodo.1117372 2017
[23]

In: ICASSP 23 (2023)

Rouard, S., Massa, F., Défossez, A.: Hybrid transformers for music source separa- tion. In: ICASSP 23 (2023)

2023
[24]

arXiv preprint arXiv:2505.16119 (2025)

Scheibler,R.,Hershey,J.R.,Doucet,A.,Li,H.:Sourceseparationbyflowmatching. arXiv preprint arXiv:2505.16119 (2025)

work page arXiv 2025
[25]

In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Scheibler, R., Ji, Y., Chung, S.W., Byun, J., Choe, S., Choi, M.S.: Diffusion-based generative speech source separation. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2023)

2023
[26]

Sam audio: Segment anything in audio

Shi, B., Tjandra, A., Hoffman, J., Wang, H., Wu, Y., Gao, L., Richter, J., Le, M., Vyas, A., Chen, S., Feichtenhofer, C., Dollár, P., Hsu, W., Lee, A.: Sam audio: Segment anything in audio. arXiv preprint arXiv:2512.18099 (2025)

work page arXiv 2025
[27]

In: Proceedings of the 12th International Conference on Digital Audio Effects

Spiertz, M., Gnann, V.: Source-filter based clustering for monaural blind source separation. In: Proceedings of the 12th International Conference on Digital Audio Effects. vol. 4, p. 6 (2009)

2009
[28]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Tian, Y., Hu, D., Xu, C.: Cyclic co-learning of sounding object visual grounding and sound separation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2745–2754 (2021)

2021
[29]

In: ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP)

Wang, H., Hai, J., Lu, Y.J., Thakkar, K., Elhilali, M., Dehak, N.: Soloaudio: Target sound extraction with language-oriented audio diffusion transformer. In: ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Pro- cessing (ICASSP). pp. 1–5. IEEE (2025)

2025
[30]

In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Wu, Y., Chen, K., Zhang, T., Hui, Y., Berg-Kirkpatrick, T., Dubnov, S.: Large- scale contrastive language-audio pretraining with feature fusion and keyword-to- caption augmentation. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–5. IEEE (2023)

2023
[31]

In: ICASSP 2025-2025 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP)

Yuan, Y., Liu, X., Liu, H., Plumbley, M.D., Wang, W.: Flowsep: Language-queried sound separation with rectified flow matching. In: ICASSP 2025-2025 IEEE Inter- national Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1–

2025
[32]

IEEE (2025) MAGE: Modality-Agnostic Music Generation and Editing 17

2025
[33]

In: Proceedings of the European Conference on Computer Vision (ECCV)

Zhao, H., Gan, C., Rouditchenko, A., Vondrick, C., McDermott, J.H., Torralba, A.: The sound of pixels. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 570–586 (2018)

2018
[34]

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

Zhu, L., Rahtu, E.: Visually guided sound source separation and localization using self-supervised motion representations. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1289–1299 (2022) 18 M. U. Saleem et al. 1 Supplementary Material A Implementation Details We implementMAGEin PyTorch and train on MUSIC using fi...

2022