Spot, Separate, and Enhance: Fully Generative Approach for Audio Mixing

SSE outperforms existing baselines in both controllability and remixing quality, as shown by extensive experiments. It comes from a paper posted to arXiv on 25 September 2026. We introduce Spot, Separate, and Enhance (SSE), the first multimodal, user-guided generative model for audio remixing and enhancement. SSE enhances video content by rebalancing the audio, removing unwanted audio sources, and reducing reverberation, guided by both video and textual descriptions. To support its training and evaluation, we propose DegradedMix, a new dataset built on the audio remixing benchmark MuddyMix. We also adopt evaluation metrics from generative modeling, which better capture the creative nature of remixing than standard reconstruction-based metrics. Project page: https://sse-ai.notion.site

Why it counts

SSE outperforms existing baselines in both controllability and remixing quality, as shown by extensive experiments. SSE enhances video content by rebalancing the audio, removing unwanted audio sources, and reducing reverberation, guided by both video and textual descriptions. We introduce Spot, Separate, and Enhance (SSE), the first multimodal, user-guided generative model for audio remixing and enhancement.

Sources

Primary source: primary source

What is not known

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