  Sound event detection (SED) and Acoustic scene classification (ASC) are two
widely researched audio tasks that constitute an important part of research on
acoustic scene analysis. Considering shared information between sound events
and acoustic scenes, performing both tasks jointly is a natural part of a
complex machine listening system. In this paper, we investigate the usefulness
of several spatial audio features in training a joint deep neural network (DNN)
model performing SED and ASC. Experiments are performed for two different
datasets containing binaural recordings and synchronous sound event and
acoustic scene labels to analyse the differences between performing SED and ASC
separately or jointly. The presented results show that the use of specific
binaural features, mainly the Generalized Cross Correlation with Phase
Transform (GCC-phat) and sines and cosines of phase differences, result in a
better performing model in both separate and joint tasks as compared with
baseline methods based on logmel energies only.
