Localization, Detection and Tracking of Multiple Moving Sound Sources with a Convolutional Recurrent Neural Network
This addresses the challenge of sound source tracking in varying acoustic conditions, but it is incremental as it adapts an existing CRNN for moving sources.
The paper tackled the problem of jointly localizing, detecting, and tracking multiple moving sound sources using a convolutional recurrent neural network (CRNN), showing that the CRNN tracks sources more consistently than a parametric method but with higher localization error.
This paper investigates the joint localization, detection, and tracking of sound events using a convolutional recurrent neural network (CRNN). We use a CRNN previously proposed for the localization and detection of stationary sources, and show that the recurrent layers enable the spatial tracking of moving sources when trained with dynamic scenes. The tracking performance of the CRNN is compared with a stand-alone tracking method that combines a multi-source (DOA) estimator and a particle filter. Their respective performance is evaluated in various acoustic conditions such as anechoic and reverberant scenarios, stationary and moving sources at several angular velocities, and with a varying number of overlapping sources. The results show that the CRNN manages to track multiple sources more consistently than the parametric method across acoustic scenarios, but at the cost of higher localization error.