# Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks

arXiv: https://arxiv.org/abs/2202.03416 · HTML: https://mlsanity.com/paper/2202.03416/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [Filtered Noise Shaping for Time Domain Room Impulse Response Estimation From Reverberant Speech](https://mlsanity.com/paper/2107.07503/) (2021)

## Built on by (most cited first, 20 of 20)

- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis](https://mlsanity.com/paper/2205.14807/) (2022)
- [Novel-View Acoustic Synthesis](https://mlsanity.com/paper/2301.08730/) (2023)
- [MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes](https://mlsanity.com/paper/2205.09248/) (2022)
- [Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields](https://mlsanity.com/paper/2309.15977/) (2023)
- [Hearing Anything Anywhere](https://mlsanity.com/paper/2406.07532/) (2024)
- [Modeling and Driving Human Body Soundfields through Acoustic Primitives](https://mlsanity.com/paper/2407.13083/) (2024)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [Sounding Bodies: Modeling 3D Spatial Sound of Humans Using Body Pose and Audio](https://mlsanity.com/paper/2311.06285/) (2023)
- [Differentiable Room Acoustic Rendering with Multi-View Vision Priors](https://mlsanity.com/paper/2504.21847/) (2025)
- [End-to-End Binaural Speech Synthesis](https://mlsanity.com/paper/2207.03697/) (2022)
- [Neural Fourier Shift for Binaural Speech Rendering](https://mlsanity.com/paper/2211.00878/) (2022)
- [SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding](https://mlsanity.com/paper/2504.05576/) (2025)
- [Solving Room Impulse Response Inverse Problems Using Flow Matching with Analytic Wiener Denoiser](https://mlsanity.com/paper/2602.00652/) (2026)
- [Bayesian inference and neural estimation of acoustic wave propagation](https://mlsanity.com/paper/2305.17749/) (2023)
- [Zero-Shot Mono-to-Binaural Speech Synthesis](https://mlsanity.com/paper/2412.08356/) (2024)
- [SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field](https://mlsanity.com/paper/2406.11006/) (2024)
- [VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification](https://mlsanity.com/paper/2502.07205/) (2025)
- [Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation](https://mlsanity.com/paper/2509.15210/) (2025)

## Abstract (arXiv)

Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning. Our framework is driven by a carefully designed neural network that jointly estimates the impulse response and the (apriori unknown) spectral noise characteristics of an observed signal given the source signal. We demonstrate robustness in estimation, even under low signal-to-noise ratios, and show strong results when learning from spatio-temporal real-world speech data. Our framework provides a natural way to interpolate impulse responses on a spatial grid, while also allowing for efficiently compressing and storing them for real-time rendering applications in augmented and virtual reality.