  Social ambiance describes the context in which social interactions happen,
and can be measured using speech audio by counting the number of concurrent
speakers. This measurement has enabled various mental health tracking and
human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is
highly desirable to ensure user privacy and thus facilitate wide adoption of
the aforementioned applications, the required computational complexity of
state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at
odds with the often constrained resources on mobile devices. Furthermore, only
limited labeled data is available or practical when it comes to SAM under
clinical settings due to various privacy constraints and the required human
effort, further challenging the achievable accuracy of on-device SAM solutions.
To this end, we propose a dedicated neural architecture search framework for
Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework
can automatically search for DNNs that push forward the achievable accuracy vs.
hardware efficiency frontier of mobile SAM solutions. For example,
ERSAM-delivered DNNs only consume 40 mW x 12 h energy and 0.05 seconds
processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only
achieving an error rate of 14.3% on a social ambiance dataset generated by
LibriSpeech. We can expect that our ERSAM framework can pave the way for
ubiquitous on-device SAM solutions which are in growing demand.
