# Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones

arXiv: https://arxiv.org/abs/2402.04866 · HTML: https://mlsanity.com/paper/2402.04866/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Complex-Valued vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data](https://mlsanity.com/paper/2009.08340/) (2020)
- [Sound field reconstruction in rooms: inpainting meets super-resolution](https://mlsanity.com/paper/2001.11263/) (2020)
- [Room impulse response reconstruction with physics-informed deep learning](https://mlsanity.com/paper/2401.01206/) (2024)
- [Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses](https://mlsanity.com/paper/2306.11509/) (2023)
- [Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses](https://mlsanity.com/paper/2102.01993/) (2021)
- [End-to-End Complex-Valued Multidilated Convolutional Neural Network for Joint Acoustic Echo Cancellation and Noise Suppression](https://mlsanity.com/paper/2110.00745/) (2021)
- [Kernel interpolation of acoustic transfer functions with adaptive kernel for directed and residual reverberations](https://mlsanity.com/paper/2303.03869/) (2023)
- [Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset](https://mlsanity.com/paper/2102.06455/) (2021)
- [Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks](https://mlsanity.com/paper/2205.12872/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Physics-Informed Neural Network-Based Approach for the Spatial Upsampling of Spherical Microphone Arrays](https://mlsanity.com/paper/2407.18732/) (2024)

## Abstract (arXiv)

Reconstructing the room transfer functions needed to calculate the complex sound field in a room has several important real-world applications. However, an unpractical number of microphones is often required. Recently, in addition to classical signal processing methods, deep learning techniques have been applied to reconstruct the room transfer function starting from a very limited set of measurements at scattered points in the room. In this paper, we employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances, using a few irregularly distributed microphones. To the best of our knowledge, this is the first time that complex-valued neural networks are used to estimate room transfer functions. To analyze the benefits of applying complex-valued optimization to the considered task, we compare the proposed technique with a state-of-the-art kernel-based signal processing approach for sound field reconstruction, showing that the proposed technique exhibits relevant advantages in terms of phase accuracy and overall quality of the reconstructed sound field. For informative purposes, we also compare the model with a similarly-structured data-driven approach that, however, applies a real-valued neural network to reconstruct only the magnitude of the sound field.