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Joint sound event localization and detection (SELD) is an integral part ofdeveloping context awareness into communication interfaces of mobile robots,smartphones, and home assistants. For example, an automatic audio focus forvideo capture on a mobile phone requires robust detection of relevant acousticevents around the device and their direction. Existing SELD approaches havebeen evaluated using material produced in controlled indoor environments, orthe audio is simulated by mixing isolated sounds to different spatiallocations. This paper studies SELD of speech in diverse everyday environments,where the audio corresponds to typical usage scenarios of handheld mobiledevices. In order to allow weighting the relative importance of localizationvs. detection, we will propose a two-stage hierarchical system, where the firststage 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 arrayrecordings from various acoustic conditions. The array is embedded in acontemporary mobile phone form factor. The obtained results show good speechdetection and localization accuracy of the proposed method in contrast to anon-hierarchical flat classification model.

Mobile Microphone Array Speech Detection and Localization in Diverse Everyday Environments