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.
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.
arXiv:2602. 03762v4 Announce Type: replace-cross Abstract: Visually-guided acoustic highlighting seeks to rebalance audio in alignment with the accompanying video, creating a coherent audio-visual experience.
arXiv:2508.03448v4 Announce Type: replace-cross Abstract: Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrow...
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:2506. 20995v4 Announce Type: replace-cross Abstract: We propose a step-by-step video-to-audio (V2A) generation method that provides finer control over the generation process and more realistic audio synthesis.
arXiv:2603. 09234v2 Announce Type: cross Abstract: Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE).
arXiv:2607. 23395v1 Announce Type: cross Abstract: Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications ranging from karaoke and remixing to audio restoration and content production.
arXiv:2510. 02916v2 Announce Type: replace-cross Abstract: We propose SALSA-V, a multimodal video-to-audio generation model capable of synthesizing highly synchronized, high-fidelity long-form audio from silent video content.
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.
The paper introduces a new task called video object segmentation‑aware audio generation, which conditions sound synthesis on object‑level segmentation maps. It presents SAGANet, a multimodal generative model that uses visual segmentation masks, video, and textual cues to produce controllable audio for musical instruments, offering fine‑grained, visually localized control. The authors also release the Segmented Music Solos dataset of instrument performance videos with segmentation information to support this task and demonstrate that SAGANet outperforms current state‑of‑the‑art methods in controllable, high‑fidelity Foley synthesis.
arXiv:2607. 20253v1 Announce Type: cross Abstract: In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descriptions, and musical attributes.
arXiv:2608. 03742v1 Announce Type: cross Abstract: Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability.