kokieto/NovelSep

audio-to-audio model by kokieto

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NovelSep: Bridging Optimization-Based Separation and Deep Neural Refinement for Novelty Detection with Listenable Explanations Koki Shoda, Jun Younes Louhi Kasahara, Qi An, and Atsushi Yamashita — The University of Tokyo. Code and installation · Project page and audio examples NovelSep separates an observed recording into normal and novel sound components. A normal dictionary first produces an optimization-based separation. NovelSep uses the encoded components to initialize a learned flow that refines both estimates. The energy of the estimated novel component provides the novelty score. The accompanying Normal Region Exclusion geometry filters training sound candidates that are too close to the normal reference region; this filtering stage is separate from inference. This repository contains the learned adapters and supporting artifacts used in the accompanying research manuscript. Each environment contains: The base SAM-Audio weights are downloaded separately from Meta's model repository. Access to that repository and agreement to its terms may be required.…

kokieto/NovelSep on Hugging Face A short preview, not the full document.

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  1. NovelSep
  2. Released environments
  3. Usage
  4. Training and numerical settings
  5. Intended use and limitations
  6. Licenses and acknowledgments
  7. Integrity and provenance

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Model overview

kokieto/NovelSep is a audio-to-audio model repository published by kokieto on Hugging Face. A library was not reported.

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Model facts

Task
audio-to-audio
Library
Not reported
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
other
Access
Not gated by Hugging Face
Created
2026-09-28T06:25:30.000Z
Last modified
2026-09-28T06:25:42.000Z

Compatibility and lineage

Reported languages: en

Reported base models: facebook/sam-audio-small

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Source: Hugging Face Hub. Metadata observed 2026-09-28T06:38:00.545Z. Daily imports are snapshots, not real-time monitoring.

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