Audio Event Recognition in the Smart Home
Audio Event Recognition in the Smart Home
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DOI：
10.1007/978-3-319-63450-0_12
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发表时间：
2018
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影响因子：
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通讯作者：
Sacha Krstulovic
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作者：
Sacha Krstulovic
在简要概述了在智能家居市场中部署自动音频事件识别（AER）的相关性和价值之后，本章回顾了AER产品化的三个方面，这些方面在开发基础研究和“现实世界”应用出口之间的影响途径时非常重要。在第一部分中，它表明，应用程序引入了各种实际的限制，引发了该领域的新的研究课题：澄清声音事件的定义，从而表明对时间模式和中断的显式建模的兴趣;在24/7声音检测设置中运行和评估AER，这表明将问题改写为开集识别;以及在音频质量和计算能力有限的消费者设备上运行AER应用，从而引发对可扩展性和鲁棒性的兴趣。第二部分探讨了AER用户体验的定义。在报告系统错误影响用户体验的方式的实地观察后，建议将意见评分引入AER评估方法。然后，标准AER性能指标和主观用户体验指标之间的联系正在探索，并注意到F分数指标实际上将声学区分的客观评估与应用程序相关操作点的主观选择相结合。介绍了系统评估中区分和校准分离的解决方案，从而使声学建模优化与应用相关的用户体验更明确地分离。最后，最后一节分析了部署AER系统所涉及的伦理和法律的问题，AER系统在任何时候都在“监听”用户的私人空间。对欧洲数据和隐私保护法所依据的关键概念进行了审查，质疑这些概念是否以及何时适用于音频数据，提出了一套准则，这些准则概括为通过充分告知用户其数据的使用情况，以及采取合理的信息安全措施来保护用户的个人数据，从而赋予用户同意的权力。
After giving a brief overview of the relevance and value of deploying automatic audio event recognition (AER) in the smart home market, this chapter reviews three aspects of the productization of AER which are important to consider when developing pathways to impact between fundamental research and “real-world” applicative outlets. In the first section, it is shown that applications introduce a variety of practical constraints which elicit new research topics in the field: clarifying the definition of sound events, thus suggesting interest for the explicit modeling of temporal patterns and interruption; running and evaluating AER in 24/7 sound detection setups, which suggests to recast the problem as open-set recognition; and running AER applications on consumer devices with limited audio quality and computational power, thus triggering interest for scalability and robustness. The second section explores the definition of user experience for AER. After reporting field observations about the ways in which system errors affect user experience, it is proposed to introduce opinion scoring into AER evaluation methodology. Then, the link between standard AER performance metrics and subjective user experience metrics is being explored, and attention is being drawn to the fact that F-score metrics actually mash up the objective evaluation of acoustic discrimination with the subjective choice of an application-dependent operation point. Solutions to the separation of discrimination and calibration in system evaluation are introduced, thus allowing the more explicit separation of acoustic modeling optimization from that of application-dependent user experience. Finally, the last section analyses the ethical and legal issues involved in deploying AER systems which are “listening” at all times into the users’ private space. A review of the key notions underpinning European data and privacy protection laws, questioning if and when these apply to audio data, suggests a set of guidelines which summarize into empowering users to consent by fully informing them about the use of their data, as well as taking reasonable information security measures to protect users’ personal data.