Seminar: A sequence matching network for sound event localization and detection

When: Wednesday, November 25, 2020, 11:00 AM - 12:00 PM
Where: Zoom - link to register: https://qmul-ac-uk.zoom.us/meeting/register/tZIkceyhpzsrG9LtSTke-tK-GhQ-IUDuxnuQ

Speaker: Tho Nguyen (Nanyang Technological University)

Centre for Digital Music Seminar Series

Open to students, staff, alumni, public; all welcome. Admission is FREE, no pre-booking required.

Title: A sequence matching network for sound event localization and detection

Abstract:
Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or phase differences between microphones. Previous approaches use the same input features for sound event detection and direction-of-arrival estimation, and train the two tasks jointly or in a two-stage transfer-learning manner. We propose a two-step approach that decouples the learning of the sound event detection and directional-of-arrival estimation systems. In the first step, we detect the sound events and estimate the directions-of-arrival separately to optimize the performance of each system. In the second step, we train a deep neural network to match the two output sequences of the event detector and the direction-of-arrival estimator. This modular and hierarchical approach allows the flexibility in the system design, and increase the performance of the whole sound event localization and detection system. The experimental results using the DCASE 2019 and DCASE 2020 sound event localization and detection dataset show an improved performance compared to the previous state-of-the-art solutions.

Biography:
Tho Nguyen is currently working towards a Ph.D. degree at the Nanyang Technological University (NTU), Singapore. Prior to joining NTU, she worked with the University of Illinois Research Center, Singapore for five years as a Research Engineer. Her research interests are audio signal processing, deep learning, microphone array signal processing, and real-time processing.