Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions

• Main Authors: Di Carlo, Diego, Nugraha, Aditya Arie, Fontaine, Mathieu, Yoshii, Kazuyoshi
• Format: Journal Article
• Language: English
• Published: 07-05-2023
• Subjects: Computer Science - Sound
• DOI: 10.48550/arxiv.2305.04447

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 resource-intensive 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.