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dc.contributor.advisorTomic, Oliver
dc.contributor.advisorLiland, Kristian
dc.contributor.authorNordby, Jon Opedal
dc.date.accessioned2019-08-29T11:42:30Z
dc.date.available2019-08-29T11:42:30Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11250/2611624
dc.description.abstractNoise is a growing problem in urban areas, and according to the WHO is the second environmental cause of health problems in Europe. Noise monitoring using Wireless Sensor Networks are being applied in order to understand and help mitigate these noise problems. It is desirable that these sensor systems, in addition to logging the sound level, can indicate what the likely sound source is. However, transmitting audio to a cloud system for classification is energy-intensive and may cause privacy issues. It is also critical for widespread adoption and dense sensor coverage that individual sensor nodes are low-cost. Therefore we propose to perform the noise classification on the sensor node, using a low-cost microcontroller. Several Convolutional Neural Networks were designed for the STM32L476 low-power microcontroller using the Keras deep-learning framework, and deployed using the vendorprovided X-CUBE-AI inference engine. The resource budget for the model was set at maximum 50% utilization of CPU, RAM, and FLASH. 10 model variations were evaluated on the Environmental Sound Classification task using the standard Urbansound8k dataset. The best models used Depthwise-Separable convolutions with striding for downsampling, and were able to reach 70.9% mean 10-fold accuracy while consuming only 20% CPU. To our knowledge, this is the highest reported performance on Urbansound8k using a microcontroller. One of the models was also tested on a microcontroller development device, demonstrating the classification of environmental sounds in real-time. These results indicate that it is computationally feasible to classify environmental sound on low-power microcontrollers. Further development should make it possible to create wireless sensor-networks for noise monitoring with on-edge noise source classification.nb_NO
dc.language.isoengnb_NO
dc.publisherNorwegian University of Life Sciences, Åsnb_NO
dc.rightsNavngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal*
dc.rightsNavngivelse-DelPåSammeVilkår 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/deed.no*
dc.subjectConvolutional Neural Networksnb_NO
dc.subjectMachine learningnb_NO
dc.subjectWireless sensor networksnb_NO
dc.subjectNoise monitoringnb_NO
dc.titleEnvironmental sound classification on microcontrollers using Convolutional Neural Networksnb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Technology: 500nb_NO
dc.description.localcodeM-DVnb_NO


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Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell-DelPåSammeVilkår 4.0 Internasjonal