Audible Audio Emotional Prediction using ML in Python Projects

Audible Audio Emotional Prediction using ML in Python
Abstract

The Audible Audio Emotional Prediction System is a Machine Learning-based application designed to identify and predict human emotions from speech and audio signals. Human emotions such as happiness, sadness, anger, fear, surprise, and neutrality can be detected by analyzing acoustic features like pitch, tone, frequency, energy, and speech patterns. The system uses audio preprocessing techniques, feature extraction methods such as MFCC (Mel Frequency Cepstral Coefficients), Chroma, and Spectral Contrast, and applies Machine Learning algorithms to classify emotions. This project can be utilized in healthcare, customer service, virtual assistants, education, and human-computer interaction systems to improve communication and user experience.

Existing System
Description

Traditional emotion recognition systems mainly rely on manual observation, questionnaires, or basic signal processing techniques. These systems often require human intervention and may produce inconsistent results.

Drawbacks of Existing System
Low accuracy in emotion detection.
Requires manual analysis and interpretation.
Limited scalability for real-time applications.
Difficult to process large volumes of audio data.
Sensitive to background noise and recording quality.
Inability to learn and improve from new data automatically.
Proposed System
Description

The proposed system uses Machine Learning algorithms to automatically recognize emotions from audio recordings. The system extracts meaningful audio features and trains classification models to predict emotions accurately.

Working Process
Audio Input Collection.
Audio Preprocessing and Noise Reduction.
Feature Extraction using:
MFCC
Chroma Features
Spectral Contrast
Zero Crossing Rate
Mel Spectrogram
Dataset Preparation and Labeling.
Model Training using Machine Learning algorithms.
Emotion Prediction and Classification.
Display Predicted Emotion Result.
Advantages of Proposed System
Higher prediction accuracy.
Fully automated emotion recognition.
Real-time emotion detection capability.
Handles large datasets efficiently.
Reduces human effort and errors.
Adaptable to different languages and speakers.
Can be integrated with AI assistants and customer support systems.

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