@villium/echo

Comprehensive audio feature extraction library for signal analysis, visualization, and processing.

🚀 Features

Core Audio Analysis

• RMS (Root Mean Square) - Signal power/volume measurement
• Peak Amplitude - Maximum signal level detection
• Zero Crossing Rate - Signal complexity analysis
• Spectral Centroid - Frequency "brightness" calculation
• Audio Normalization - Dynamic range optimization

Advanced Signal Processing

• Fundamental Frequency - Pitch detection via autocorrelation
• Spectral Rolloff - High-frequency content measurement
• Spectral Flatness - Noise vs. tonal content analysis
• MFCC - Mel-frequency cepstral coefficients
• Envelope Extraction - Amplitude envelope tracking
• Onset Detection - Note/event timing identification

📦 Installation

npm install @villium/echo

🔌 Plugin Ecosystem

• @villium/echo-beat - Beat detection & tempo analysis
• @villium/echo-fx - Real-time audio effects
• @villium/echo-chamber - Audio visualization components

📚 Quick Start

import { 
  rms, peak, normalize, 
  fundamentalFrequency, mfcc, 
  onsetDetection 
} from '@villium/echo';

// Basic signal analysis
const audioBuffer = new Float32Array([/* your audio data */]);
const volume = rms(audioBuffer);
const maxAmplitude = peak(audioBuffer);

// Advanced feature extraction  
const pitch = fundamentalFrequency(audioBuffer, 44100);
const coefficients = mfcc(magnitudeSpectrum, 44100);
const noteOnsets = onsetDetection(audioBuffer);

// Audio processing
normalize(audioBuffer, 0.8); // Normalize to 80% of full scale

🎯 Use Cases

• Music Analysis - Genre classification, mood detection
• Audio Visualization - Real-time waveforms and spectrograms
• Machine Learning - Feature extraction for audio ML models
• Sound Design - Audio effect processing and analysis
• Research - Academic audio signal processing

📖 Documentation

Visit our documentation for detailed API references and examples.

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

📄 License

MIT © Villium