| Paper ID | AUD-33.6 |
| Paper Title |
A GENERAL NETWORK ARCHITECTURE FOR SOUND EVENT LOCALIZATION AND DETECTION USING TRANSFER LEARNING AND RECURRENT NEURAL NETWORK |
| Authors |
Thi Ngoc Tho Nguyen, Nanyang Technological University, Singapore; Ngoc Khanh Nguyen, Motional, Singapore; Huy Phan, Queen Mary University of London, United Kingdom; Lam Pham, Austrian Institute of Technology, Austria; Kenneth Ooi, Nanyang Technological University, Singapore; Douglas L. Jones, University of Illinois at Urbana-Champaign, United States; Woon-Seng Gan, Nanyang Technological University, Singapore |
| Session | AUD-33: Topics in Deep Learning for Speech and Audio |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation Time: | Friday, 11 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-CLAS] Detection and Classification of Acoustic Scenes and Events |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Polyphonic sound event detection and localization (SELD) task is challenging because it is difficult to jointly optimize sound event detection (SED) and direction-of-arrival (DOA) estimation in the same network. We propose a general network architecture for SELD task in which the SELD network comprises sub-networks that are pre-trained to solve SED and DOA estimation independently, and a recurrent layer that combines the SED and DOA estimation outputs into SELD outputs. The recurrent layer does the alignment between the sound classes and DOAs of sound events while being unaware of how these outputs are produced by the upstream SED and DOA estimation algorithms. This simple network architecture is compatible with many different existing SED and DOA estimation algorithms. It is highly practical because the sub-networks can be improved independently. The experimental results using the DCASE 2020 SELD dataset show that the performances of our proposed network architecture using different SED and DOA estimation algorithms and different audio formats are competitive with other state-of-the-art SELD algorithms. The source code for the proposed SELD network architecture is available at Github. |
