Privacy-preserving crowd density analysis finds application across a wide range of scenarios, substantially enhancing smart building operation and management while upholding privacy expectations in various spaces. We propose a non-speech audio-based approach for crowd analytics, leveraging a transformer-based model. Our results demonstrate that non-speech audio alone can be used to conduct such analysis with remarkable accuracy. To the best of our knowledge, this is the first time when non-speech audio signals are proposed for predicting occupancy. As far as we know, there has been no other similar approach of its kind prior to this. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. The proposed non-speech-based approach outperformed the thermal camera-based model and all other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.

翻译：隐私保护的人群密度分析广泛应用于各类场景，在保障不同空间隐私期望的同时，显著提升了智能建筑的运营管理水平。我们提出了一种基于非语音音频的人群分析方法，采用Transformer架构模型。研究结果表明，仅凭非语音音频即可实现高精度的人群分析。据我们所知，这是首次提出利用非语音音频信号进行占用率预测的方法，在此之前尚不存在任何类似技术路线。为开展此项研究，我们在获得机构审查委员会（IRB）批准后，于某大型医院候诊室部署了基于传感器的采集平台，历时数月采集非语音音频和热成像数据以训练并评估模型。所提出的非语音音频方法在性能上优于热成像相机模型及所有其他基线方法。除在不依赖语音音频条件下展现卓越性能外，我们还进一步采用差分隐私技术进行分析，以提供额外的隐私保障。总体而言，本研究证明了非语音音频数据在实现高精度占用估计方面的可行性，同时确保了语音相关内容的排除，并通过差分隐私机制提供了强有力的隐私保护。