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Computational Analysis of Sound Scenes and Events pp 335–371Cite as

Audio Event Recognition in the Smart Home

Abstract

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.

Keywords

  • Smart home applications
  • Audio event recognition
  • Modelling audio events
  • Computational power
  • Embedded sound recognition
  • Audio quality
  • Open-set recognition
  • User interface
  • Objective and subjective evaluation
  • Ethics
  • Privacy

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Fig. 12.1
Fig. 12.2

Notes

  1. 1.

    The term audio event recognition (AER) in this chapter corresponds to what is referred to as sound event detection in other chapters of this book. Whereas consensus is currently forming amongst the academic research community around the latter term, the industry prefers AER for marketing reasons: firstly because it establishes a parallel with automatic speech recognition, and secondly because “recognition” makes the system feel more intelligent by referring to semantics and meaning, than “detection” which refers to “plain automation.”

  2. 2.

    This suggestion may be reminiscent of speech recognition techniques, where the acoustic models and the language model contribute almost equally to speech recognition accuracy [54]. However, the problem may be different in AER: the proportion of silence or interruption versus target acoustic frames may be much smaller, and thus have less effect on the general acoustic probabilities, in continuous speech than in the case of short interrupted audio events such as, e.g., smoke alarms or baby cries. Besides, the structure of non-speech audio events or audio scenes may not be of a linguistic nature according to the strict definition of language as a system of communication, thus questioning the structural nature of non-speech audio events at a deeper level of cognitive concepts. More discussion on “acoustic language models” can be found in Chap. 8.

  3. 3.

    A requirement to be able to cross-check the triggering audio may seem to contradict the privacy and eavesdropping concerns analyzed further down in Sect. 12.4.4. However, in practice there is less of a privacy concern about transmitting short audio snippets, with pre and post bracketing time kept to a minimum around the triggering audio event, than there is about streaming someone’s personal audio to the cloud in a continuous 24/7 manner.

  4. 4.

    False alarm (FA) is synonymous to false positive (FP), and missed detection (MD) is synonymous to false negatives (FN). Usage varies across domains in the literature, e.g., speaker recognition tends to use the former more often.

  5. 5.

    Many of the definitions in this section are quoted from the ICO’s documentation [69, 70] and the UK Data Service’s website [64]. Both services are local to the UK and government funded, and they aim at helping researchers and businesses understanding the legal requirements set forth into the UK’s Data Protection Act 1998. Because the UK’s DPA 1998 in itself seeks to implement European recommendations and directives, the notions quoted in this list are echoing the general definitions set forth in the various European laws and recommendations.

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Acknowledgements

The research work exposed in Sect. 12.2.3 has been developed as a collaboration between Queen Mary University of London and Audio Analytic, supported by InnovateUK grant nr.131604 and EPSRC grants EP/M507088/1 & EP/N014111/1, as well as private funding from Audio Analytic Ltd. Prof. Mark Plumbley, currently at University of Surrey, has supervised the work of Dr. Siddarth Sigtia, currently with Apple, and Dr. Adam Stark, currently with Mi.mu Gloves, on the academic side of this research work. Tamara Sword from Audio Analytic has contributed some of the wording about use cases and marketing aspects. The author would like to thank Dr. Tuomas Virtanen and Dr. Juan Bello for their insightful comments on this chapter.

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Krstulović, S. (2018). Audio Event Recognition in the Smart Home. In: Virtanen, T., Plumbley, M., Ellis, D. (eds) Computational Analysis of Sound Scenes and Events. Springer, Cham. https://doi.org/10.1007/978-3-319-63450-0_12

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