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This thesis explores the feasibility of using handclaps as a means of biometric identification and control in the context of home appliances. Utilizing the acoustic properties of handclaps, the study proposes a machine learning-based solution capable of identifying the individual performing the handclap as well as distinguishing between single, double, and triple claps. The solution utilizes well
