Saha, Rony KumerDas, BristyMuhiuddun, Md. Muhiul IslamLabib Al-Barr, SyedSarker, MehdiShangram, Mansiv HamidImran, Md. Talha Bin2026-04-192026-04-1920252025-09ID 21321024ID 21321081ID 21321032ID 21221018http://hdl.handle.net/10361/27937Cataloged from PDF version of final year design project.Includes bibliographical references (page 57).This final year design project is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2025.Indoor noise pollution has become a critical public health concern particularly due to rapid urbanization. However, existing Passive Noise Control (PNC) solutions inherently compromise Indoor Environmental Quality (IEQ) by impeding air flow and blocking sunlight, while the traditional Active Noise Control (ANC) methods remain cost-prohibitive for widespread deployment. This project introduces an indoor sound minimization system that utilizes a Machine Learning-based ANC framework. The system integrates microphones, speakers and microcontrollers with a Convolutional Recurrent Network (CRN) algorithm to predict and generate effective real-time anti-noise signals. Performance evaluation of a working prototype in diverse acoustic environments demonstrates an average noise reduction of 9.4 decibel (dB) in the frequency range of 20 to 2000 Hertz (Hz), achieved within 80 milliseconds (ms). The system prioritizes scalability and low power consumption, positioning it as a potentially viable and sustainable acoustic solution for residential homes and healthcare facilities.110 pagesenBRAC University project reports are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Active noise controlConvolutional recurrent networkIndoor environmental qualityAnti-noise generationActive noise and vibration control.Project Report