4a5cc864-1ab8-4302-aef5-b6e2a33f4b7e20221110062756962naun:naunmdt@crossref.orgMDT DepositInternational Journal of Computers1998-430810.46300/9108http://www.naun.org/cms.action?id=30353420223420221610.46300/9108.2022.16https://npublications.com/journals/computers/2022.phpBhoomiRajdeepComputer Science Engineering Dept. Rai School of engineering, Rai University Ahmedabad, IndiaHardik B. ,PatelComputer Science Engineering Dept. Rai School of Engineering, Rai University Ahmedabad, IndiaSaileshIyerComputer Science Engineering Dept. Rai School of Engineering, Rai University Ahmedabad, IndiaDetection of mood and behavior by voice analysis which helps to detect the speaker’s mood by the voice frequency. Here, I aim to present the mood like happy, and sad and behavior detection devices using machine learning and artificial intelligence which can be detected by voice analysis. Using this device, it detects the user’s mood. Moreover, this device detects the frequency by trained model and algorithm. The algorithm is well trained to catch the frequency where it helps to identify the mood happy or sad of the speaker and behavior. On the other hand, behavior can be predicted in form, it can be either positive or negative. So, this device helps to prevent mental health issues and is used in medical and gaming testing. Furthermore, it is easy to identify a person’s mood by their expression and by their actions in daily activities. But it is effective and challenging to detect mood and behavior by voice frequency because a rich environment affects the most. Thus, this device works as a signal processing.11102022111020228710315https://npublications.com/journals/computers/2022/a302007-015(2022).pdf10.46300/9108.2022.16.15https://npublications.com/journals/computers/2022/a302007-015(2022).pdfShaheen, “Impact of Automatic Feature Extraction in Deep Learning Architecture,” Gold Coast, QLD, Australia, 2016. 10.1109/taslp.2015.2456420Moritz, “An Auditory Inspired Amplitude Modulation Filter Bank for Robust Feature Extraction in Automatic Speech Recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 23, no. 11, pp. 1926-1937, 2015. Evgeniou, “Support Vector Machines: Theory and Applications,” 2001. 10.1016/s1018-3639(18)30850-xShoshan, “Speech and Music Classification and Separation: A Review,” Journal of King Saud University - Engineering Sciences, vol. 19, no. 1, pp. 95-132, 2006. 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