About the Project
This research project will seek to explore and redefine the state of the art in intelligent audio monitoring for both fault detection and fault prediction in and out of the smart home environment. Research will be undertaken that will explore and extend the current methods of audio capture with a view to enabling low cost, non-intrusive real-time 3D sound field analysis and modelling.
Audio event classification often works very well if presented with audio similar to the training sets provided but tend to underperform in less-than-ideal listening environments. AI will play a crucial role in improving performance and will feature heavily in the research project.
Noise ingress and sound source monitoring/detection within the smart home environment will enable optimisations to be made with respect to build mechanisms and usage. Services (Gas, Water, etc.) and appliance audio monitoring solutions will be explored which will result in a complete audio / time signature of the home to be created, both in terms of energy usage, fault prediction and detection.
The project will focus on methods in which audio can be captured, in particular on low cost / low power distributed and connected methods. Cloud utilisation will enable the exploration of condition monitoring and control remotely. The project will look to extend the methods explored to wider use, for example, remote monitoring and fault diagnosis of plant systems.
The advent of low power but highly optimised processing devices now enables edge-based processing of audio to be performed. This greatly reduces throughput of data, offers much better price/performance, and offers greater accessibility at lower cost. This combined with the ability to connect smart audio devices remotely allows for huge potential in their usage to support and predict service usage, appliance usage and potential faults.
Current 3D mapping of acoustic spaces is generally performed with specialist and expensive equipment. The proliferation of low power transducers combined with the rise in edge-based technology opens up the possibility of more intelligent solutions that offer greater depth of data to the end user.
Conducting this study in the Smart Home facility will be essential to ensure the ecological validity of the result from the study. Real world data with respect to audio capture, response, processing would all be possible in a controlled but relevant scenario.
The Huddersfield Smart House Research Facility is being developed as a collaborative hub for industry, academia and government organisations. It is being developed to accelerate research and development for smart products and services to be used in the building environment with an aim to bring transformational improvements in key performance indicators corresponding to 21st century houses and living conditions. For this purpose, a well instrumented two storey dwelling is being constructed that will provide facilities for a range of novel and innovative investigations to be carried out.
Smart technologies can help us in reducing carbon footprints as well as having positive energy balance through improved energy performance of homes and buildings. We can achieve greater energy efficiency, cut carbon emissions and support more intelligent and flexible management of energy supply and demand. By incorporating use of smart technologies, the health and wellbeing can be significantly improved through better management of internal environments, safety and security. Smart technologies have potential to offer significant improvements in wellbeing of the occupants by allowing control through voice and mobile apps as well as using automation and artificial intelligence to support and predict our changing needs.
HSHRF aims to bring researchers, practitioners, industries and government organisations together to design, develop and implement holistic solutions to current and future societal challenges associated with building environment and its use.
Applications must be made through the University of Huddersfield Online Application portal:
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