  Joint sound event localization and detection (SELD) is an integral part of
developing context awareness into communication interfaces of mobile robots,
smartphones, and home assistants. For example, an automatic audio focus for
video capture on a mobile phone requires robust detection of relevant acoustic
events around the device and their direction. Existing SELD approaches have
been evaluated using material produced in controlled indoor environments, or
the audio is simulated by mixing isolated sounds to different spatial
locations. This paper studies SELD of speech in diverse everyday environments,
where the audio corresponds to typical usage scenarios of handheld mobile
devices. In order to allow weighting the relative importance of localization
vs. detection, we will propose a two-stage hierarchical system, where the first
stage is to detect the target events, and the second stage is to localize them.
  The proposed method utilizes convolutional recurrent neural network (CRNN)
and is evaluated on a database of manually annotated microphone array
recordings from various acoustic conditions. The array is embedded in a
contemporary mobile phone form factor. The obtained results show good speech
detection and localization accuracy of the proposed method in contrast to a
non-hierarchical flat classification model.
