Daljit Josh, John-Anthony Elenis, Heman Muresan, P. Spachos, S. Gregori
{"title":"Low-Power Low-Cost Audio Front-End for Keyword Spotting","authors":"Daljit Josh, John-Anthony Elenis, Heman Muresan, P. Spachos, S. Gregori","doi":"10.1109/CCECE47787.2020.9255693","DOIUrl":null,"url":null,"abstract":"This paper presents a low power audio front end for keyword spotting. A multi-stage approach is used to reduce the power consumption of the system by only using different stages when they are required. A working prototype was created and tested to verify its functionality. The effectiveness of the multistage approach is shown by comparing the power consumption of the system in its idle state to the systems active state. The prototype has a power consumption of 4.1 mW in the idle state that can be reduced below 3 mW with a keyword detection accuracy of 87 %.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCECE47787.2020.9255693","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper presents a low power audio front end for keyword spotting. A multi-stage approach is used to reduce the power consumption of the system by only using different stages when they are required. A working prototype was created and tested to verify its functionality. The effectiveness of the multistage approach is shown by comparing the power consumption of the system in its idle state to the systems active state. The prototype has a power consumption of 4.1 mW in the idle state that can be reduced below 3 mW with a keyword detection accuracy of 87 %.