# Automatic Contextual Audio Denoising

arXiv: https://arxiv.org/abs/2605.22262 · HTML: https://mlsanity.com/paper/2605.22262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 14 of 14)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [CochlScene: Acquisition of acoustic scene data using crowdsourcing](https://mlsanity.com/paper/2211.02289/) (2022)
- [My lips are concealed: Audio-visual speech enhancement through obstructions](https://mlsanity.com/paper/1907.04975/) (2019)
- [A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings](https://mlsanity.com/paper/2202.08702/) (2022)
- [MaD TwinNet: Masker-Denoiser Architecture with Twin Networks for Monaural Sound Source Separation](https://mlsanity.com/paper/1802.00300/) (2018)
- [Target Sound Extraction with Variable Cross-modality Clues](https://mlsanity.com/paper/2303.08372/) (2023)
- [Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction](https://mlsanity.com/paper/2310.07284/) (2023)
- [Deep Denoising for Hearing Aid Applications](https://mlsanity.com/paper/1805.01198/) (2018)
- [Multi-Level Speaker Representation for Target Speaker Extraction](https://mlsanity.com/paper/2410.16059/) (2024)
- [SoundSculpt: Direction and Semantics Driven Ambisonic Target Sound Extraction](https://mlsanity.com/paper/2506.00273/) (2025)
- [Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns](https://mlsanity.com/paper/2509.05079/) (2025)

## Abstract (arXiv)

Audio context determines which sound components and sources are relevant and which can be perceived as irrelevant (noise) by listeners. For example, traffic noise is informative in urban surveillance but noise for a phone call at the same location. Most current audio denoising systems apply fixed target-noise definitions, often removing useful components in one context while failing to suppress irrelevant components. To address this, we introduce the concept automatic contextual audio denoising (ACAD) which defines target and noise based on the inferred context. In this work, we restrict context to be associated with an acoustic scene class. We label sound events outside the event distribution of a scene class (noise) as out-of-context (OC) and events typical for that scene as in-context (IC). We implement a deep learning method that automatically infers the context of the audio signal and removes OC components, and benchmark it against variants: without context inference, with oracle context, and with separately provided uninformative context. On paired clean/noisy data across diverse contexts, where OC components in one context may be IC in another, our proposed method outperforms other approaches across standard objective metrics, indicating that the model can infer context and context-dependent processing can enhance denoising.