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Social ambiance describes the context in which social interactions happen,and can be measured using speech audio by counting the number of concurrentspeakers. This measurement has enabled various mental health tracking andhuman-centric IoT applications. While on-device Socal Ambiance Measure (SAM) ishighly desirable to ensure user privacy and thus facilitate wide adoption ofthe aforementioned applications, the required computational complexity ofstate-of-the-art deep neural networks (DNNs) powered SAM solutions stands atodds with the often constrained resources on mobile devices. Furthermore, onlylimited labeled data is available or practical when it comes to SAM underclinical settings due to various privacy constraints and the required humaneffort, further challenging the achievable accuracy of on-device SAM solutions.To this end, we propose a dedicated neural architecture search framework forEnergy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM frameworkcan 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 secondsprocessing latency for a 5 seconds audio segment on a Pixel 3 phone, while onlyachieving an error rate of 14.3% on a social ambiance dataset generated byLibriSpeech. We can expect that our ERSAM framework can pave the way forubiquitous on-device SAM solutions which are in growing demand.

ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement