NEURAL STEERER: NOVEL STEERING VECTOR SYNTHESIS WITH A CAUSAL NEURAL FIELD OVER FREQUENCY AND DIRECTION - Laboratoire Traitement et Communication de l'Information Access content directly
Conference Papers Year : 2024

NEURAL STEERER: NOVEL STEERING VECTOR SYNTHESIS WITH A CAUSAL NEURAL FIELD OVER FREQUENCY AND DIRECTION

Abstract

We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in reducing the resourceintensive measurements required for precise sound source separation and localization, essential as the front-end of speech recognition. Classical approaches to interpolation rely on linear weighting of nearby measurements in space on a fixed, discrete set of frequencies. Drawing inspiration from the success of neural fields for novel view synthesis in computer vision, we introduce the neural steerer, a continuous complex-valued function that takes both frequency and direction as input and produces the corresponding steering vector. Importantly, it incorporates inter-channel phase difference information and a regularization term enforcing filter causality, essential for accurate steering vector modeling. Our experiments, conducted using a dataset of real measured steering vectors, demonstrate the effectiveness of our resolution-free model in interpolating such measurements.
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Dates and versions

hal-04479188 , version 1 (27-02-2024)

Identifiers

  • HAL Id : hal-04479188 , version 1

Cite

Diego Di Carlo, Aditya Arie Nugraha, Mathieu Fontaine, Yoshiaki Bando, Kazuyoshi Yoshii. NEURAL STEERER: NOVEL STEERING VECTOR SYNTHESIS WITH A CAUSAL NEURAL FIELD OVER FREQUENCY AND DIRECTION. ICASSP, Apr 2024, Seoul (Korea), South Korea. ⟨hal-04479188⟩
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