Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
- URL: http://arxiv.org/abs/2402.04866v3
- Date: Tue, 11 Jun 2024 14:54:45 GMT
- Title: Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones
- Authors: Francesca Ronchini, Luca Comanducci, Mirco Pezzoli, Fabio Antonacci, Augusto Sarti,
- Abstract summary: We employ complex-valued neural networks to estimate room transfer functions in the frequency range of the first room resonances.
This is the first time that complex-valued neural networks are used to estimate room transfer functions.
- Score: 15.396703290586418
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Neural Experts: Mixture of Experts for Implicit Neural Representations [41.395193251292895]
Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction.
We propose a mixture of experts (MoE) implicit neural representation approach that enables learning local piece-wise continuous functions.
We show that incorporating a mixture of experts architecture into existing INR formulations provides a boost in speed, accuracy, and memory requirements.
arXiv Detail & Related papers (2024-10-29T01:11:25Z) - Generative adversarial networks with physical sound field priors [6.256923690998173]
This paper presents a deep learning-based approach for learns-temporal reconstruction of sound fields using Generative Adversa Networks (GANs)
The proposed method uses a plane wave basis and the underlying statistical distributions of pressure in rooms to reconstruct sound fields from a limited number of measurements.
The results suggest that this approach provides a promising approach to sound field reconstruction using generative models that allow for a physically informed acoustics prior to problems.
arXiv Detail & Related papers (2023-08-01T10:11:23Z) - Joint Channel Estimation and Feedback with Masked Token Transformers in
Massive MIMO Systems [74.52117784544758]
This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
The entire encoder-decoder network is utilized for channel compression.
Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
arXiv Detail & Related papers (2023-06-08T06:15:17Z) - Transformer Meets Boundary Value Inverse Problems [4.165221477234755]
Transformer-based deep direct sampling method is proposed for solving a class of boundary value inverse problem.
A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and reconstructed images.
arXiv Detail & Related papers (2022-09-29T17:45:25Z) - Three-Way Deep Neural Network for Radio Frequency Map Generation and
Source Localization [67.93423427193055]
Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies.
In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
arXiv Detail & Related papers (2021-11-23T22:25:10Z) - Over-and-Under Complete Convolutional RNN for MRI Reconstruction [57.95363471940937]
Recent deep learning-based methods for MR image reconstruction usually leverage a generic auto-encoder architecture.
We propose an Over-and-Under Complete Convolu?tional Recurrent Neural Network (OUCR), which consists of an overcomplete and an undercomplete Convolutional Recurrent Neural Network(CRNN)
The proposed method achieves significant improvements over the compressed sensing and popular deep learning-based methods with less number of trainable parameters.
arXiv Detail & Related papers (2021-06-16T15:56:34Z) - SignalNet: A Low Resolution Sinusoid Decomposition and Estimation
Network [79.04274563889548]
We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples.
We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions.
In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
arXiv Detail & Related papers (2021-06-10T04:21:20Z) - PILOT: Introducing Transformers for Probabilistic Sound Event
Localization [107.78964411642401]
This paper introduces a novel transformer-based sound event localization framework, where temporal dependencies in the received multi-channel audio signals are captured via self-attention mechanisms.
The framework is evaluated on three publicly available multi-source sound event localization datasets and compared against state-of-the-art methods in terms of localization error and event detection accuracy.
arXiv Detail & Related papers (2021-06-07T18:29:19Z) - Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL
Sound Field Dataset [0.0]
This paper extends evaluations of sound field reconstruction at low frequencies by introducing a dataset with measurements from four real rooms.
The paper advances on a recent deep learning-based method for sound field reconstruction using a very low number of microphones.
arXiv Detail & Related papers (2021-02-12T11:34:18Z) - Estimation of the Mean Function of Functional Data via Deep Neural
Networks [6.230751621285321]
We propose a deep neural network method to perform nonparametric regression for functional data.
The proposed method is applied to analyze positron emission tomography images of patients with Alzheimer disease.
arXiv Detail & Related papers (2020-12-08T17:18:16Z) - Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by
Spiking Neural Network [68.43026108936029]
We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment.
We implement this algorithm in a real-time robotic system with a microphone array.
The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
arXiv Detail & Related papers (2020-07-07T08:22:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.