Work

Neural Directional Filtering: Far-Field Directivity Control with a Small Microphone Array

Year: 2024

Type: conference-paper

Abstract: Capturing audio signals with specific directivity patterns is essential in speech communication. This study presents a deep neural network (DNN)-based approach to directional filtering, alleviating the need for explicit signal models. More specifically, our proposed method uses a DNN to estimate a single-channel complex mask from the signals of a microphone array. This mask is then applied to a reference microphone to render a signal that exhibits a desired directivity pattern. We investigate the training dataset composition and its effect on the directivity realized by the DNN during inference. Using a relatively small DNN, the proposed method is found to approximate the desired directivity pattern closely. Additionally, it allows for the realization of higher-order directivity patterns using a small number of microphones, which is a difficult task for linear and parametric directional filtering.

Source: International Workshop on Acoustic Signal Enhancement

DOI: 10.1109/iwaenc61483.2024.10693965

Authors: Julian Wechsler, Srikanth Raj Chetupalli, Mhd Modar Halimeh, Oliver Thiergart, Emanuël A. P. Habets

Institution: International Audio Laboratories Erlangen

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Award: HochleistungscomputeCluster