Emotion Based Ambiance and Music Regulation Using Deep Learning

Abstract

Human emotions play a significant role in determining mood, productivity, and overall well-being. Music and environmental lighting are two important factors that can influence emotional states and psychological comfort. With recent advancements in artificial intelligence and deep learning, it has become possible to automatically detect human emotions through facial expressions. This research proposes an intelligent system that detects human emotions using facial recognition techniques and automatically regulates music and room ambiance to improve the user's emotional experience. The system uses a Convolutional Neural Network (CNN) to identify emotions from facial images captured through a webcam. Based on the detected emotion, the system generates a suitable music playlist and adjusts lighting conditions to create a comfortable environment. The model is trained using the FER2013 dataset and implemented using Python along with deep learning frameworks such as TensorFlow and Keras. Experimental results show that the proposed system can successfully recognize emotions and dynamically control music and ambiance, thereby enhancing user comfort and mood.