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.

翻译：社交氛围描述了社交互动发生的语境，可通过语音音频中同时说话者数量的统计进行量化。该测量方法已赋能多种心理健康追踪与人本物联网应用。尽管设备端社交氛围测量（SAM）对保障用户隐私、从而促进前述应用的广泛部署至关重要，但现有基于深度神经网络（DNN）的先进SAM方案所需计算复杂度，与移动设备常受限的资源条件形成矛盾。此外，受隐私约束与人工标注成本限制，临床环境下的SAM任务仅能获取有限标注数据，这进一步制约了设备端SAM方案可达到的精度。为此，我们提出专用于高能效实时SAM（ERSAM）的神经架构搜索框架。具体而言，ERSAM框架能自动搜索出可突破移动端SAM方案精度与硬件效率权衡边界的DNN架构。例如，在Pixel 3手机上处理5秒音频片段时，ERSAM生成的DNN仅消耗40毫瓦×12小时能量，处理延迟为0.05秒，同时在基于LibriSpeech构建的社交氛围数据集上实现14.3%的误差率。我们预期ERSAM框架能为日益增长的普适化设备端SAM解决方案开辟道路。