Feature Overview for Joint Modeling of Sound Event Detection and Localization Using a Microphone Array

Abstract

In this paper, we present a comparative study of a number of features and time-frequency signal representations for the task of joint sound event detection and localization using a state-of-the-art model based on a convolutional recurrent neural network. Experiments are performed for a dataset consisting of the recordings made using a tetrahedral microphone array. Several feature inputs specific to the task of sound event detection and sound source localization are combined and subsequently tested, with the aim to achieve joint performance of both tasks for multiple overlapping sound events using a single model based on a deep neural network architecture. Apart from providing a comprehensive comparison of various state-of-the-art acoustic features such as generalized cross-correlation, and inter-channel level and phase differences, we propose new features that have not been used for this task before such as eigenvectors of the microphone covariance matrix or sines and cosines of phase differences between the channels. Results for all combinations of input features are analyzed and discussed, followed by conclusions.