A Universal Deep Room Acoustics Estimator

López, Paula Sánchez; Callens, Paul; Cerňak, Miloš

doi:10.1109/waspaa52581.2021.9632738

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…We therefore choose the standard deviation levels of the introduced noise to 1, 10 and 50 cm. In our fourth study, we introduced random Gaussian noise to the reverberation time with a standard deviation of 200 ms, based on reported errors obtained on reverberation estimation procedures [52,53].…”

Section: Metadata Sensitivity Studymentioning

confidence: 99%

Graph Neural Networks for Sound Source Localization on Distributed Microphone Networks

Eric

Brookes

Naylor

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones' coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.

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Section: Metadata Sensitivity Studymentioning

confidence: 99%