| Paper ID | AUD-16.6 |
| Paper Title |
EFFICIENT TRAINING DATA GENERATION FOR PHASE-BASED DOA ESTIMATION |
| Authors |
Fabian Hübner, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany; Wolfgang Mack, Emanuël Habets, AudioLabs Erlangen, Germany |
| Session | AUD-16: Modeling, Analysis and Synthesis of Acoustic Environments 2: Spatial Audio |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-ASAP] Acoustic Sensor Array Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Deep learning (DL) based direction of arrival (DOA) estimation is an active research topic and currently represents the state-of-the-art. Usually, DL-based DOA estimators are trained with recorded data or computationally expensive generated data. Both data types require significant storage and excessive time to, respectively, record or generate. We propose a low complexity online data generation method to train DL models with a phase-based feature input. The data generation method models the phases of the microphone signals in the frequency domain by employing a deterministic model for the direct path and a statistical model for the late reverberation of the room transfer function. By an evaluation using data from measured room impulse responses, we demonstrate that a model trained with the proposed training data generation method performs comparably to models trained with data generated based on the source-image method. |
