arXiv AI
SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
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arXiv AI
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Spot, Separate, and Enhance: Fully Generative Approach for Audio Mixing
Spot, Separate, and Enhance (SSE) is a multimodal, user‑guided generative model for audio remixing and enhancement. It rebalances audio, removes unwanted sources, and reduces reverberation using video and textual guidance. The authors introduce the DegradedMix dataset and adopt generative evaluation metrics, showing SSE outperforms existing baselines in controllability and remixing quality.
Hugging Face Trending Papers
4d ago
Spot, Separate, and Enhance: Fully Generative Approach for Audio Mixing
Spot, Separate, and Enhance (SSE) is the first multimodal, user‑guided generative model for audio remixing and enhancement. It rebalances audio, removes unwanted sources, and reduces reverberation in video content, guided by both video and textual descriptions. The authors introduce the DegradedMix dataset, built on MuddyMix, and use generative‑model evaluation metrics to demonstrate SSE’s superior controllability and remixing quality compared to existing baselines.
arXiv AI
Aug 6
InvFlowFD: Reference-Free and Background-Set-Free Perceptual Music Quality Metric with Flow Matching Inversion
arXiv:2608. 04142v1 Announce Type: cross Abstract: Existing reference-free methods for evaluating music perceptual quality alleviate the need for paired noisy-clean data, but they still rely on a background set, which is used to compute aggregated statistics of clean audio samples.
arXiv AI
Aug 18
Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task
arXiv:2608. 14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited.
arXiv Machine Learning
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Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation
arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.
arXiv AI
Jul 24
OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model
arXiv:2602. 12304v5 Announce Type: replace-cross Abstract: Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual prompts.