Human Sentiment Sound Rnn Emotional Analysis With Django interface

Abstract

Human Sentiment Sound RNNs For Emotional Audio Analysis With A Django Interface" Explores The Intersection Of Deep Learning And Human-computer Interaction By Developing A Recurrent Neural Network (RNN) Model Tailored To Analyze And Classify Human Emotions From Audio Signals. Our Primary Objective Was To Build A Robust And Accurate System Capable Of Capturing And Interpreting Various Emotional Tones In Audio Data Using Advanced RNN Architectures, Particularly Long Short-Term Memory (LSTM) Units.To Ensure Broad Accessibility And Ease Of Use, We Integrated The Trained Model Into A Django-based Web Interface That Allows Users To Upload Audio Files, Interact With The System, And Visualize Sentiment Analysis Results In Real-time. We Further Extended The Platform’s Functionality By Incorporating Real-time Audio Recording Directly Through The Interface, Enabling Live Emotional Feedback On Spoken Input. The Output Is Presented Through Intuitive Visualizations, Displaying Classified Emotional States Such As Happiness, Sadness, Anger, And Neutrality, With Confidence Scores For Transparency. Our Project Also Addresses Challenges Such As Background Noise, Speaker Variability, And Latency In Live Inference.This Work Aims To Contribute Significantly To The Field Of Human-computer Interaction (HCI) By Enabling Intelligent Systems To Comprehend And Respond Empathetically To Human Emotions. Potential Real-world Applications Include Integration Into Virtual Assistants, Tools For Mental Health Monitoring, And Customer Service Solutions That Adapt Responses Based On Detected Sentiment, Ultimately Enhancing User Experience And Communication Efficiency

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