  Within the audio research community and the industry, keyword spotting (KWS)
and audio tagging (AT) are seen as two distinct tasks and research fields.
However, from a technical point of view, both of these tasks are identical:
they predict a label (keyword in KWS, sound event in AT) for some fixed-sized
input audio segment. This work proposes UniKW-AT: An initial approach for
jointly training both KWS and AT. UniKW-AT enhances the noise-robustness for
KWS, while also being able to predict specific sound events and enabling
conditional wake-ups on sound events. Our approach extends the AT pipeline with
additional labels describing the presence of a keyword. Experiments are
conducted on the Google Speech Commands V1 (GSCV1) and the balanced Audioset
(AS) datasets. The proposed MobileNetV2 model achieves an accuracy of 97.53% on
the GSCV1 dataset and an mAP of 33.4 on the AS evaluation set. Further, we show
that significant noise-robustness gains can be observed on a real-world KWS
dataset, greatly outperforming standard KWS approaches. Our study shows that
KWS and AT can be merged into a single framework without significant
performance degradation.
