Audio event recognition in the smart home

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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 "realworld" 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.




Krstulović, S. (2017). Audio event recognition in the smart home. In Computational Analysis of Sound Scenes and Events (pp. 335–371). Springer International Publishing.

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