G.O.D Framework

Introduction

The ai_resonant_voice.py module brings the power of sound and voice resonance into the G.O.D Framework. It enables AI systems to process, analyze, and adapt based on vocal patterns, sound frequencies, and resonance attributes of audio data. This functionality is crucial for applications such as voice recognition, emotional context identification, and human-device interaction through sound.

Purpose

This module is specifically designed to:

• Process audio data to extract meaningful features such as pitch, tone, resonance, and frequency profiles.
• Analyze voice input to identify emotions, latent resonance patterns, and anomalies in vocal data.
• Support voice-triggered actions within the G.O.D Framework and integrate seamlessly with other AI components.
• Provide a platform for musical resonance recognition, tone analysis, and auditory event detection.

Key Features

• Voice Resonance Analysis: Detects tonal and frequency-based characteristics of voice input.
• Emotion Detector: Uses patterns in voice pitch, tone, and pace to classify emotions.
• Audio Feature Extraction: Extracts high-level audio properties for use in machine learning models.
• Trigger Events: Listens for specific auditory cues or commands to trigger system functions.
• Speech-to-Text Integration: Optional integration with third-party tools for STT processing.

Logic and Implementation

The script primarily relies on libraries such as librosa for analyzing sound input and extracting features, and a combination of lightweight machine learning models for emotion and resonance detection. Below is an illustrative example:

        import librosa
        import numpy as np
        from sklearn.ensemble import RandomForestClassifier

        class ResonantVoiceAnalyzer:
            """
            A class for analyzing the resonance and emotion behind voice input.
            """
            def __init__(self):
                self.model = RandomForestClassifier()  # Placeholder model for emotion classification

            def extract_features(self, audio_file):
                """
                Extracts audio features such as MFCCs (Mel-frequency cepstral coefficients) from input audio.

                Args:
                    audio_file (str): Path to the audio file.

                Returns:
                    np.ndarray: Extracted feature set.
                """
                y, sr = librosa.load(audio_file, sr=None)  # Load raw audio data
                mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)  # Extract MFCCs
                spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)  # Extract spectral centroid
                features = np.hstack([mfcc.mean(axis=1), spectral_centroid.mean(axis=0)])  # Combine features
                return features

            def classify_emotion(self, features):
                """
                Predicts emotion based on extracted features.

                Args:
                    features (np.ndarray): Audio feature set.

                Returns:
                    str: Predicted emotion.
                """
                emotion_map = {0: "Neutral", 1: "Happy", 2: "Sad", 3: "Angry"}  # Example mapping
                prediction = self.model.predict([features])[0]
                return emotion_map.get(prediction, "Unknown")

        # Example Usage
        if __name__ == "__main__":
            analyzer = ResonantVoiceAnalyzer()
            sample_audio = "input/sample_audio.wav"  # Sample file path
            audio_features = analyzer.extract_features(sample_audio)
            print("Audio Features Extracted:", audio_features)
            emotion = analyzer.classify_emotion(audio_features)
            print("Detected Emotion:", emotion)
        

Dependencies

• librosa: Library for audio and music analysis.
• numpy: Fundamental library for numerical computations and feature processing.
• sklearn: Provides machine learning algorithms, in this case, used for classifiers.

Integration with G.O.D Framework

The ai_resonant_voice.py script is tightly integrated with related G.O.D components for maximum synergy:

• ai_emotion_analyzer.py: Shares overlapping objectives for advanced emotional state recognition.
• ai_feedback_loop.py: Helps incorporate user interactions based on detected auditory cues.
• ai_visual_dashboard.py: Displays visual representation of auditory data and emotional analysis.

Future Enhancements

Potential future improvements include:

• Integration with real-time voice streaming and resonance calibration.
• Advanced neural networks for speech feature extraction and emotional recognition.
• Support for multiple languages via resonance analysis of multilingual data.
• Adding music genre classification based on resonance and tone analysis.