Heihachi

What makes a tiger so strong is that it lacks humanity

Heihachi Audio Analysis Framework

Advanced audio analysis framework for processing, analyzing, and visualizing audio files with optimized performance, designed specifically for electronic music with a focus on neurofunk and drum & bass genres. Now featuring revolutionary fire-based emotional querying through WebGL interface and Rust-powered backend.

🔥 Revolutionary Fire-Based Emotion Interface

Heihachi now includes a groundbreaking fire-based emotional querying system that taps into humanity's deepest cognitive patterns. Users create and maintain digital fire through an intuitive WebGL interface, which the system "understands" using advanced AI reconstruction techniques and maps directly to audio generation and analysis.

The Science Behind Fire-Emotion Mapping

Based on extensive research into human consciousness and fire recognition (see docs/ideas/fire.md), fire represents humanity's first and most fundamental abstraction - deeply embedded in our neural architecture. Our system leverages this connection through:

Digital Fire Creation: Intuitive WebGL interface for creating and manipulating fire
Pakati Reference Understanding Engine: AI system that "learns" fire patterns by reconstructing them from partial information
Autobahn Probabilistic Reasoning: Advanced biological intelligence and Bayesian inference delegation
Emotional Pattern Extraction: Mapping fire characteristics to emotional and musical features
Direct Audio Generation: Converting understood fire patterns into music that matches the emotional content

🧠 Autobahn Integration: Delegated Probabilistic Reasoning

Heihachi implements a revolutionary delegated probabilistic reasoning architecture where all probabilistic tasks, Bayesian inference, biological intelligence, and consciousness modeling are delegated to the Autobahn oscillatory bio-metabolic RAG system.

Key Benefits:

Optimal Specialization: Heihachi focuses on audio processing, Autobahn handles probabilistic reasoning
Scientific Foundation: Leverages Autobahn's 12 theoretical frameworks and consciousness modeling
Performance: Ultra-fast Rust audio core + advanced biological intelligence
Consciousness-Aware: Real-time IIT Φ calculation for consciousness-informed audio generation

Overview
🔥 Fire-Based Emotion Interface
🧠 Autobahn Integration
🦀 Rust-Powered Architecture
Features
Installation
Usage
Fire Interface Usage
Autobahn Integration Usage
Theoretical Foundation
Core Components
REST API
HuggingFace Integration
Experimental Results
Performance Optimizations
Applications
Future Directions
License
Citation

🔥 Fire-Based Emotion Interface

Revolutionary Emotional Querying System

The fire-based emotion interface represents a paradigm shift in how humans interact with AI music systems. Instead of struggling to describe emotions in words, users create and manipulate digital fire - tapping into humanity's deepest cognitive patterns.

Key Components:

WebGL Fire Simulator: Real-time, physics-based fire rendering with intuitive controls
Pakati Reference Understanding Engine: AI system that "learns" fire patterns through progressive masking and reconstruction
Emotional Pattern Mapping: Direct conversion from fire characteristics to musical features
Real-time Audio Generation: Live synthesis based on fire manipulation

How It Works

Fire Creation: Users interact with a WebGL interface to create, maintain, and modify digital fire
Pattern Capture: The system captures fire characteristics (intensity, color, movement, structure)
AI Understanding: Pakati's Reference Understanding Engine reconstructs the fire from partial information to prove true comprehension
Emotional Extraction: Fire patterns are mapped to emotional and musical dimensions
Audio Generation: The understood patterns drive Heihachi's audio synthesis and analysis engines

Technical Innovation

This system leverages research showing that fire recognition activates the same neural networks as human consciousness itself, creating authentic emotional expression that bypasses the limitations of verbal description.

🧠 Autobahn Integration: Delegated Probabilistic Reasoning

Revolutionary Architecture Delegation

Heihachi implements a groundbreaking delegated probabilistic reasoning architecture where all probabilistic tasks, Bayesian inference, biological intelligence, and consciousness modeling are delegated to the Autobahn oscillatory bio-metabolic RAG system.

Autobahn System Overview

Autobahn is an advanced probabilistic reasoning engine implementing:

12 Theoretical Frameworks: Including fire-evolved consciousness substrate and biological intelligence architectures
Oscillatory Bio-Metabolic Processing: 3-layer architecture with ATP-driven metabolic computation
Consciousness Emergence Modeling: Real-time IIT Φ calculation for consciousness quantification
Advanced Uncertainty Quantification: Sophisticated Bayesian inference and fuzzy logic processing
Multi-scale Temporal Processing: From quantum coherence (10⁻⁴⁴s) to cognitive cycles (10¹³s)

Integration Benefits

Performance Optimization:

Fire Pattern Analysis: <10ms (Autobahn oscillatory processing)
Audio Optimization: <20ms (Autobahn Bayesian inference)
Consciousness Calculation: <15ms (Autobahn IIT Φ)
End-to-End Latency: <50ms (Rust + Autobahn delegation)

Scientific Foundation:

Biological Intelligence: Membrane processing with ion channel coherence effects
Consciousness Modeling: IIT-based Φ calculation for awareness quantification
Metabolic Computation: ATP-driven processing with multiple metabolic modes
Uncertainty Handling: Explicit modeling of uncertainty in all probabilistic operations

Delegation Architecture

┌─────────────────┐    ┌──────────────────────┐    ┌─────────────────────┐
│   Heihachi      │    │     Autobahn         │    │   Audio Output      │
│                 │    │                      │    │                     │
│ ┌─────────────┐ │    │ ┌──────────────────┐ │    │ ┌─────────────────┐ │
│ │ Fire        │ │───▶│ │ Fire Pattern     │ │───▶│ │ Optimized       │ │
│ │ Interface   │ │    │ │ Analysis         │ │    │ │ Audio           │ │
│ └─────────────┘ │    │ └──────────────────┘ │    │ │ Generation      │ │
│                 │    │                      │    │ └─────────────────┘ │
│ ┌─────────────┐ │    │ ┌──────────────────┐ │    │                     │
│ │ Pakati      │ │───▶│ │ Consciousness    │ │    │                     │
│ │ Engine      │ │    │ │ Modeling (IIT Φ) │ │    │                     │
│ └─────────────┘ │    │ └──────────────────┘ │    │                     │
│                 │    │                      │    │                     │
│ ┌─────────────┐ │    │ ┌──────────────────┐ │    │                     │
│ │ Rust Audio  │ │───▶│ │ Bayesian         │ │    │                     │
│ │ Core        │ │    │ │ Optimization     │ │    │                     │
│ └─────────────┘ │    │ └──────────────────┘ │    │                     │
└─────────────────┘    └──────────────────────┘    └─────────────────────┘

🦀 Rust-Powered Architecture

High-Performance Core Engine

Heihachi now features a Rust-powered backend that provides:

Performance Benefits:

10-100x speed improvements in audio processing pipelines
Memory safety without garbage collection overhead
Parallel processing with zero-cost abstractions
Real-time capabilities for live fire-to-audio mapping

Architecture:

Rust Core: High-performance audio processing, DSP, and mathematical operations
Python Interface: PyO3 bindings for seamless Python integration
WebGL Frontend: Next.js application for fire interface and visualization
REST API: Unified access layer supporting all components

Hybrid Language Benefits

The new architecture combines the best of each language:

Rust: Core audio processing, real-time DSP, mathematical computations
Python: Machine learning, rapid prototyping, data analysis
TypeScript/Next.js: Interactive UI, WebGL fire simulation, real-time visualization

Overview

Heihachi implements novel approaches to audio analysis by combining neurological models of rhythm processing with advanced signal processing techniques. The system is built upon established neuroscientific research demonstrating that humans possess an inherent ability to synchronize motor responses with external rhythmic stimuli. This framework provides high-performance analysis for:

Detailed drum pattern recognition and visualization
Bass sound design decomposition
Component separation and analysis
Comprehensive visualization tools
Neural-based feature extraction
Memory-optimized processing for large files

Features

🔥 Fire-Based Emotion Interface: Revolutionary WebGL fire manipulation for emotional audio generation
🧠 Autobahn Probabilistic Reasoning: Delegated biological intelligence and consciousness modeling
🦀 Rust-Powered Performance: 10-100x speed improvements in audio processing
⚡ Real-time Processing: <50ms end-to-end latency for fire-to-audio generation
🧘 Consciousness-Aware: IIT Φ calculation for consciousness-informed audio synthesis
High-performance audio file processing
Batch processing for handling multiple files
Memory optimization for large audio files
Parallel processing capabilities
Visualization tools for spectrograms and waveforms
Interactive results exploration with command-line and web interfaces
Progress tracking for long-running operations
Export options in multiple formats (JSON, CSV, YAML, etc.)
Comprehensive CLI with shell completion
HuggingFace integration for advanced audio analysis and neural processing
Pakati Understanding Validation: AI comprehension verification through reconstruction
Biological Intelligence: 3-layer processing architecture with ATP metabolic management

Installation

Quick Install

# Clone the repository
git clone https://github.com/yourusername/heihachi.git
cd heihachi

# Run the setup script
python scripts/setup.py

Options

The setup script supports several options:

--install-dir DIR     Installation directory
--dev                 Install development dependencies
--no-gpu              Skip GPU acceleration dependencies
--no-interactive      Skip interactive mode dependencies
--shell-completion    Install shell completion scripts
--no-confirm          Skip confirmation prompts
--venv                Create and use a virtual environment
--venv-dir DIR        Virtual environment directory (default: .venv)

Manual Installation

If you prefer to install manually:

# Create and activate virtual environment (optional)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install the package
pip install -e .

Usage

Basic Usage

# Process a single audio file
heihachi process audio.wav --output results/

# Process a directory of audio files
heihachi process audio_dir/ --output results/

# Batch processing with different configurations
heihachi batch audio_dir/ --config configs/performance.yaml

Interactive Mode

# Start interactive command-line explorer with processed results
heihachi interactive --results-dir results/

# Start web-based interactive explorer
heihachi interactive --web --results-dir results/

# Compare multiple results with interactive explorer
heihachi compare results1/ results2/

# Show only progress demo
heihachi demo --progress-demo

Export Options

# Export results to different formats
heihachi export results/ --format json
heihachi export results/ --format csv
heihachi export results/ --format markdown

Command-Line Interface (CLI)

The basic command structure is:

python -m src.main [input_file] [options]

Where [input_file] can be either a single audio file or a directory containing multiple audio files.

Command-Line Options

Option	Description	Default
`input_file`	Path to audio file or directory (required)	-
`-c, --config`	Path to configuration file	../configs/default.yaml
`-o, --output`	Path to output directory	../results
`--cache-dir`	Path to cache directory	../cache
`-v, --verbose`	Enable verbose logging	False

Examples

# Process a single audio file
python -m src.main /path/to/track.wav

# Process an entire directory of audio files
python -m src.main /path/to/audio/folder

# Use a custom configuration file
python -m src.main /path/to/track.wav -c /path/to/custom_config.yaml

# Specify custom output directory
python -m src.main /path/to/track.wav -o /path/to/custom_output

# Enable verbose logging
python -m src.main /path/to/track.wav -v

Processing Results

After processing, the results are saved to the output directory (default: ../results). For each audio file, the following is generated:

Analysis data: JSON files containing detailed analysis results
Visualizations: Graphs and plots showing various aspects of the audio analysis
Summary report: Overview of the key findings and detected patterns

Fire Interface Usage

Starting the Fire Interface

# Start the complete fire-to-music system
heihachi fire-interface --port 3000

# Start with specific emotional mapping model
heihachi fire-interface --emotion-model advanced --port 3000

# Development mode with hot reload
heihachi fire-interface --dev --port 3000

Fire Interface Workflow

Launch Interface: Open the WebGL fire interface in your browser
Create Fire: Use intuitive controls to create and shape digital fire
Real-time Feedback: Hear immediate audio response to fire manipulation
Capture Emotion: Save fire patterns that represent specific emotions
Generate Music: Convert captured patterns into full musical compositions

API Integration

from heihachi.fire import FireEmotionMapper, FireInterface

# Initialize fire emotion system
fire_system = FireEmotionMapper()

# Capture fire pattern from WebGL interface
fire_pattern = fire_system.capture_from_interface()

# Extract emotional features
emotions = fire_system.extract_emotions(fire_pattern)

# Generate audio based on fire pattern
audio = fire_system.generate_audio(fire_pattern, duration=30)

# Save results
fire_system.save_pattern(fire_pattern, "my_emotion.json")

Advanced Fire Controls

Fire Characteristics:

Intensity: Controls energy and tempo
Color Temperature: Affects harmonic content and mood
Flame Height: Influences melodic range and dynamics
Flame Dance: Controls rhythmic complexity and syncopation
Spark Density: Affects percussion patterns and texture
Wind Interaction: Modulates temporal flow and transitions

Real-time Audio Mapping:

Bass Response: Fire base intensity drives sub-bass and kick patterns
Harmonic Content: Flame color maps to chord progressions and tonality
Rhythmic Patterns: Flame movement creates drum patterns and grooves
Atmospheric Elements: Smoke and particles generate ambient textures
Dynamic Response: Fire behavior changes create musical transitions and builds

Autobahn Integration Usage

Starting the Integrated System

# Start Heihachi with Autobahn integration
heihachi start --with-autobahn --autobahn-host localhost:8080

# Start with custom Autobahn configuration
heihachi start --with-autobahn --autobahn-config configs/autobahn.yaml

# Development mode with both systems
heihachi dev --fire-interface --autobahn-integration

Delegated Processing Workflow

from heihachi.autobahn import AutobahnIntegrationManager
from heihachi.fire import FireInterface

# Initialize integrated system
autobahn = AutobahnIntegrationManager()
fire_interface = FireInterface(autobahn_integration=autobahn)

# Fire manipulation with consciousness modeling
fire_pattern = fire_interface.capture_pattern()

# Delegate to Autobahn for analysis
consciousness_phi = autobahn.calculate_consciousness(fire_pattern)
emotional_state = autobahn.analyze_fire_emotion(fire_pattern)
audio_params = autobahn.optimize_audio_generation(fire_pattern, emotional_state)

# Generate consciousness-informed audio
audio = fire_interface.generate_audio(
    fire_pattern=fire_pattern,
    consciousness_phi=consciousness_phi,
    optimization_params=audio_params
)

Advanced Integration Features

Real-time Consciousness Monitoring:

# Monitor consciousness emergence during fire manipulation
consciousness_stream = autobahn.stream_consciousness_analysis(
    fire_interface.get_realtime_stream()
)

for phi_value, fire_state in consciousness_stream:
    if phi_value > 0.7:  # High consciousness threshold
        # Trigger enhanced audio generation
        audio_enhancement = autobahn.consciousness_enhanced_audio(
            fire_state, phi_value
        )
        fire_interface.apply_audio_enhancement(audio_enhancement)

Biological Intelligence Processing:

# Access Autobahn's biological intelligence layers
bio_analysis = autobahn.biological_analysis(fire_pattern)

# Layer 1: Membrane processing
membrane_response = bio_analysis.membrane_layer.process(fire_pattern)

# Layer 2: Metabolic computation
metabolic_state = bio_analysis.metabolic_layer.compute_atp_driven_response(
    membrane_response
)

# Layer 3: Consciousness emergence
consciousness_emergence = bio_analysis.consciousness_layer.calculate_phi(
    metabolic_state
)

Uncertainty Quantification:

# Get uncertainty estimates for all processing steps
uncertainty_analysis = autobahn.quantify_uncertainty(fire_pattern)

print(f"Fire Pattern Recognition Confidence: {uncertainty_analysis.pattern_confidence}")
print(f"Emotional Mapping Uncertainty: {uncertainty_analysis.emotion_uncertainty}")
print(f"Audio Generation Reliability: {uncertainty_analysis.audio_reliability}")
print(f"Consciousness Calculation Certainty: {uncertainty_analysis.phi_certainty}")

Performance Monitoring

# Monitor delegation performance
performance_stats = autobahn.get_performance_stats()

print(f"Fire Analysis Latency: {performance_stats.fire_analysis_ms}ms")
print(f"Consciousness Calculation: {performance_stats.consciousness_ms}ms")
print(f"Bayesian Optimization: {performance_stats.optimization_ms}ms")
print(f"Total Delegation Overhead: {performance_stats.total_overhead_ms}ms")

Configuration Management

Autobahn Integration Settings:

# configs/autobahn_integration.yaml
autobahn:
  host: "localhost"
  port: 8080
  timeout: 5000  # 5 second timeout
  
  # Delegation settings
  delegation:
    fire_analysis: true
    consciousness_modeling: true
    bayesian_optimization: true
    uncertainty_quantification: true
  
  # Performance settings
  performance:
    max_concurrent_requests: 10
    batch_processing: true
    caching_enabled: true
  
  # Biological intelligence settings
  biological:
    membrane_processing: true
    metabolic_computation: true
    atp_management: true
    ion_channel_coherence: true

Theoretical Foundation

Neural Basis of Rhythm Processing

The framework is built upon established neuroscientific research demonstrating that humans possess an inherent ability to synchronize motor responses with external rhythmic stimuli. This phenomenon, known as beat-based timing, involves complex interactions between auditory and motor systems in the brain.

Key neural mechanisms include:

Beat-based Timing Networks
- Basal ganglia-thalamocortical circuits
- Supplementary motor area (SMA)
- Premotor cortex (PMC)
Temporal Processing Systems
- Duration-based timing mechanisms
- Beat-based timing mechanisms
- Motor-auditory feedback loops

Motor-Auditory Coupling

Research has shown that low-frequency neural oscillations from motor planning areas guide auditory sampling, expressed through coherence measures:

$$C_{xy}(f) = \frac{|S_{xy}(f)|^2}{S_{xx}(f)S_{yy}(f)}$$

Where:

$C_{xy}(f)$ represents coherence at frequency $f$
$S_{xy}(f)$ is the cross-spectral density
$S_{xx}(f)$ and $S_{yy}(f)$ are auto-spectral densities

Mathematical Framework

In addition to the coherence measures, we utilize several key mathematical formulas:

Spectral Decomposition: For analyzing sub-bass and Reese bass components:

$$X(k) = \sum_{n=0}^{N-1} x(n)e^{-j2\pi kn/N}$$

Groove Pattern Analysis: For microtiming deviations:

$$MT(n) = \frac{1}{K}\sum_{k=1}^{K} |t_k(n) - t_{ref}(n)|$$

Amen Break Detection: Pattern matching score:

$$S_{amen}(t) = \sum_{f} w(f)|X(f,t) - A(f)|^2$$

Reese Bass Analysis: For analyzing modulation and phase relationships:

$$R(t,f) = \left|\sum_{k=1}^{K} A_k(t)e^{j\phi_k(t)}\right|^2$$

Transition Detection: For identifying mix points and transitions:

$$T(t) = \alpha\cdot E(t) + \beta\cdot S(t) + \gamma\cdot H(t)$$

Similarity Computation: For comparing audio segments:

$$Sim(x,y) = \frac{\sum_i w_i \cdot sim_i(x,y)}{\sum_i w_i}$$

Segment Clustering: Using DBSCAN with adaptive distance:

$$D(p,q) = \sqrt{\sum_{i=1}^{N} \lambda_i(f_i(p) - f_i(q))^2}$$

Core Components

1. Feature Extraction Pipeline

Rhythmic Analysis

Automated drum pattern recognition
Groove quantification
Microtiming analysis
Syncopation detection

Spectral Analysis

Multi-band decomposition
Harmonic tracking
Timbral feature extraction
Sub-bass characterization

Component Analysis

Sound source separation
Transformation detection
Energy distribution analysis
Component relationship mapping

2. Alignment Modules

Amen Break Analysis

Pattern matching and variation detection
Transformation identification
Groove characteristic extraction
VIP/Dubplate classification
Robust onset envelope extraction with fault tolerance
Dynamic time warping with optimal window functions

Prior Subspace Analysis

Neurofunk-specific component separation
Bass sound design analysis
Effect chain detection
Temporal structure analysis

Composite Similarity

Multi-band similarity computation
Transformation-aware comparison
Groove-based alignment
Confidence scoring

3. Annotation System

Peak Detection

Multi-band onset detection
Adaptive thresholding
Feature-based peak classification
Confidence scoring

Segment Clustering

Pattern-based segmentation
Hierarchical clustering
Relationship analysis
Transition detection

Transition Detection

Mix point identification
Blend type classification
Energy flow analysis
Structure boundary detection

4. Robust Processing Framework

Error Handling and Validation

Empty audio detection and graceful recovery
Sample rate validation and default fallbacks
Signal integrity verification
Automatic recovery mechanisms

Memory Management

Streaming processing for large files
Resource optimization and monitoring
Garbage collection optimization
Chunked processing of large audio files

Signal Processing Enhancements

Proper window functions to eliminate spectral leakage
Normalized processing paths
Adaptive parameters based on content
Fault-tolerant alignment algorithms

REST API

Heihachi provides a comprehensive REST API for integrating audio analysis capabilities into web applications, mobile apps, and other systems. The API supports both synchronous and asynchronous processing, making it suitable for both real-time and batch processing scenarios.

Quick Start

# Install API dependencies
pip install flask flask-cors flask-limiter

# Start the API server
python api_server.py --host 0.0.0.0 --port 5000

# Or with custom configuration
python api_server.py --production --config-path configs/production.yaml

API Endpoints

Endpoint	Method	Description	Rate Limit
`/health`	GET	Health check	None
`/api`	GET	API information and endpoints	None
`/api/v1/analyze`	POST	Full audio analysis	10/min
`/api/v1/features`	POST	Extract audio features	20/min
`/api/v1/beats`	POST	Detect beats and tempo	20/min
`/api/v1/drums`	POST	Analyze drum patterns	10/min
`/api/v1/stems`	POST	Separate audio stems	5/min
`/api/v1/semantic/analyze`	POST	Semantic analysis with emotion mapping	10/min
`/api/v1/semantic/search`	POST	Search indexed tracks semantically	20/min
`/api/v1/semantic/emotions`	POST	Extract emotional features only	20/min
`/api/v1/semantic/text-analysis`	POST	Analyze text descriptions	30/min
`/api/v1/semantic/stats`	GET	Get semantic search statistics	None
`/api/v1/batch-analyze`	POST	Batch process multiple files	2/min
`/api/v1/jobs/{id}`	GET	Get job status and results	None
`/api/v1/jobs`	GET	List all jobs (paginated)	None

Usage Examples

1. Analyze Single Audio File

Synchronous Processing:

curl -X POST http://localhost:5000/api/v1/analyze \
  -F "[email protected]" \
  -F "config=configs/default.yaml"

Asynchronous Processing:

curl -X POST http://localhost:5000/api/v1/analyze \
  -F "[email protected]" \
  -F "async=true"

2. Extract Features

curl -X POST http://localhost:5000/api/v1/features \
  -F "[email protected]" \
  -F "model=microsoft/BEATs-base"

3. Detect Beats

curl -X POST http://localhost:5000/api/v1/beats \
  -F "[email protected]"

4. Analyze Drums

curl -X POST http://localhost:5000/api/v1/drums \
  -F "[email protected]" \
  -F "visualize=true"

5. Separate Stems

curl -X POST http://localhost:5000/api/v1/stems \
  -F "[email protected]" \
  -F "save_stems=true" \
  -F "format=wav"

6. Batch Processing

curl -X POST http://localhost:5000/api/v1/batch-analyze \
  -F "[email protected]" \
  -F "[email protected]" \
  -F "[email protected]"

7. Semantic Analysis with Emotional Mapping

curl -X POST http://localhost:5000/api/v1/semantic/analyze \
  -F "[email protected]" \
  -F "include_emotions=true" \
  -F "index_for_search=true" \
  -F "title=Track Title" \
  -F "artist=Artist Name"

8. Extract Emotional Features Only

curl -X POST http://localhost:5000/api/v1/semantic/emotions \
  -F "[email protected]"

9. Semantic Search

curl -X POST http://localhost:5000/api/v1/semantic/search \
  -H "Content-Type: application/json" \
  -d '{"query": "dark aggressive neurofunk with heavy bass", "top_k": 5}'

10. Text Analysis

curl -X POST http://localhost:5000/api/v1/semantic/text-analysis \
  -H "Content-Type: application/json" \
  -d '{"text": "This track has an amazing dark atmosphere with aggressive drums"}'

11. Check Job Status

curl http://localhost:5000/api/v1/jobs/550e8400-e29b-41d4-a716-446655440000

Python Client Example

import requests
import json

# API base URL
base_url = "http://localhost:5000/api/v1"

# Upload and analyze audio file
def analyze_audio(file_path, async_processing=False):
    url = f"{base_url}/analyze"
    
    with open(file_path, 'rb') as f:
        files = {'file': f}
        data = {'async': str(async_processing).lower()}
        
        response = requests.post(url, files=files, data=data)
        return response.json()

# Extract features
def extract_features(file_path, model='microsoft/BEATs-base'):
    url = f"{base_url}/features"
    
    with open(file_path, 'rb') as f:
        files = {'file': f}
        data = {'model': model}
        
        response = requests.post(url, files=files, data=data)
        return response.json()

# Check job status
def get_job_status(job_id):
    url = f"{base_url}/jobs/{job_id}"
    response = requests.get(url)
    return response.json()

# Semantic analysis with emotions
def semantic_analyze(file_path, include_emotions=True, index_for_search=False, title=None, artist=None):
    url = f"{base_url}/semantic/analyze"
    
    with open(file_path, 'rb') as f:
        files = {'file': f}
        data = {
            'include_emotions': str(include_emotions).lower(),
            'index_for_search': str(index_for_search).lower()
        }
        if title:
            data['title'] = title
        if artist:
            data['artist'] = artist
        
        response = requests.post(url, files=files, data=data)
        return response.json()

# Semantic search
def semantic_search(query, top_k=5, enhance_query=True):
    url = f"{base_url}/semantic/search"
    data = {
        'query': query,
        'top_k': top_k,
        'enhance_query': enhance_query
    }
    
    response = requests.post(url, json=data)
    return response.json()

# Extract emotions only
def extract_emotions(file_path):
    url = f"{base_url}/semantic/emotions"
    
    with open(file_path, 'rb') as f:
        files = {'file': f}
        response = requests.post(url, files=files)
        return response.json()

# Example usage
if __name__ == "__main__":
    # Synchronous analysis
    result = analyze_audio("track.wav", async_processing=False)
    print("Analysis result:", json.dumps(result, indent=2))
    
    # Semantic analysis with emotion mapping
    semantic_result = semantic_analyze("track.wav", include_emotions=True, 
                                     index_for_search=True, title="My Track", artist="My Artist")
    print("Emotions:", semantic_result['semantic_analysis']['emotions'])
    
    # Extract just emotions
    emotions = extract_emotions("track.wav")
    print("Emotional analysis:", emotions['emotions'])
    print("Dominant emotion:", emotions['summary']['dominant_emotion'])
    
    # Search for similar tracks
    search_results = semantic_search("dark aggressive neurofunk with heavy bass")
    print("Search results:", search_results['results'])
    
    # Asynchronous analysis
    job = analyze_audio("long_track.wav", async_processing=True)
    job_id = job['job_id']
    print(f"Job created: {job_id}")
    
    # Poll job status
    import time
    while True:
        status = get_job_status(job_id)
        print(f"Job status: {status['status']}")
        
        if status['status'] in ['completed', 'failed']:
            break
        
        time.sleep(5)  # Wait 5 seconds before checking again

JavaScript/Node.js Client Example

const FormData = require('form-data');
const fetch = require('node-fetch');
const fs = require('fs');

const API_BASE = 'http://localhost:5000/api/v1';

// Analyze audio file
async function analyzeAudio(filePath, asyncProcessing = false) {
    const form = new FormData();
    form.append('file', fs.createReadStream(filePath));
    form.append('async', asyncProcessing.toString());
    
    const response = await fetch(`${API_BASE}/analyze`, {
        method: 'POST',
        body: form
    });
    
    return await response.json();
}

// Extract features
async function extractFeatures(filePath, model = 'microsoft/BEATs-base') {
    const form = new FormData();
    form.append('file', fs.createReadStream(filePath));
    form.append('model', model);
    
    const response = await fetch(`${API_BASE}/features`, {
        method: 'POST',
        body: form
    });
    
    return await response.json();
}

// Check job status
async function getJobStatus(jobId) {
    const response = await fetch(`${API_BASE}/jobs/${jobId}`);
    return await response.json();
}

// Example usage
(async () => {
    try {
        // Extract features
        const features = await extractFeatures('track.mp3');
        console.log('Features:', JSON.stringify(features, null, 2));
        
        // Start async analysis
        const job = await analyzeAudio('track.wav', true);
        console.log('Job started:', job.job_id);
        
        // Poll job status
        let status;
        do {
            await new Promise(resolve => setTimeout(resolve, 5000)); // Wait 5 seconds
            status = await getJobStatus(job.job_id);
            console.log('Job status:', status.status);
        } while (!['completed', 'failed'].includes(status.status));
        
        if (status.status === 'completed') {
            console.log('Results:', JSON.stringify(status.results, null, 2));
        }
        
    } catch (error) {
        console.error('Error:', error);
    }
})();

Response Formats

All API endpoints return JSON responses with the following structure:

Success Response:

{
    "status": "completed",
    "results": {
        // Analysis results vary by endpoint
    },
    "processing_time": 45.2
}

Async Job Response:

{
    "job_id": "550e8400-e29b-41d4-a716-446655440000",
    "status": "processing",
    "message": "Analysis started. Use /api/v1/jobs/{job_id} to check status."
}

Error Response:

{
    "error": "File too large",
    "message": "Maximum file size is 500MB"
}

Configuration

Configure the API using environment variables or command-line arguments:

Variable	Default	Description
`PORT`	5000	Server port
`MAX_FILE_SIZE`	500MB	Maximum upload file size
`PROCESSING_TIMEOUT`	1800	Processing timeout in seconds
`MAX_CONCURRENT_JOBS`	5	Maximum concurrent processing jobs
`HUGGINGFACE_API_KEY`	""	HuggingFace API key for gated models
`UPLOAD_FOLDER`	uploads	Directory for uploaded files
`RESULTS_FOLDER`	results	Directory for results

Deployment

Docker Deployment

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 5000
CMD ["python", "api_server.py", "--production", "--host", "0.0.0.0"]

Production Deployment

# Using gunicorn for production
pip install gunicorn

# Start with gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 "src.api.app:create_app()"

# Or with custom configuration
gunicorn -w 4 -b 0.0.0.0:5000 --timeout 1800 "src.api.app:create_app()"

HuggingFace Integration

Heihachi integrates specialized AI models from Hugging Face, enabling advanced neural processing of audio using state-of-the-art models. This integration follows a structured implementation approach with models carefully selected for electronic music analysis tasks.

Available Models

The following specialized audio analysis models are available:

Category	Model Type	Default Model	Description	Priority
Core Feature Extraction	Generic spectral + temporal embeddings	microsoft/BEATs	Bidirectional ViT-style encoder trained with acoustic tokenisers; provides 768-d latent at ~20 ms hop	High
	Robust speech & non-speech features	openai/whisper-large-v3	Trained on >5M hours; encoder provides 1280-d features tracking energy, voicing & language	High
Audio Source Separation	Stem isolation	Demucs v4	Returns 4-stem or 6-stem tensors for component-level analysis	High
Rhythm Analysis	Beat / down-beat tracking	Beat-Transformer	Dilated self-attention encoder with F-measure ~0.86	High
	Low-latency beat-tracking	BEAST	50 ms latency, causal attention; ideal for real-time DJ analysis	Medium
	Drum-onset / kit piece ID	DunnBC22/wav2vec2-base-Drum_Kit_Sounds	Fine-tuned on kick/snare/tom/overhead labels	Medium
Multimodal & Similarity	Multimodal similarity / tagging	laion/clap-htsat-fused	Query with free-text and compute cosine similarity on 512-d embeddings	Medium
	Zero-shot tag & prompt embedding	UniMus/OpenJMLA	Score arbitrary tag strings for effect-chain heuristics	Medium
Future Extensions	Audio captioning	slseanwu/beats-conformer-bart-audio-captioner	Produces textual descriptions per segment	Low
	Similarity retrieval UI	CLAP embeddings + FAISS	Index embeddings and expose nearest-neighbor search	Low

Configuration

Configure HuggingFace models in configs/huggingface.yaml:

# Enable/disable HuggingFace integration
enabled: true

# API key for accessing HuggingFace models (leave empty to use public models only)
api_key: ""

# Specialized model settings
feature_extraction:
  enabled: true
  model: "microsoft/BEATs-base"

beat_detection:
  enabled: true
  model: "nicolaus625/cmi"

# Additional models (disabled by default to save resources)
drum_sound_analysis:
  enabled: false
  model: "DunnBC22/wav2vec2-base-Drum_Kit_Sounds"

similarity:
  enabled: false
  model: "laion/clap-htsat-fused"

# See configs/huggingface.yaml for all available options

HuggingFace Commands

# Extract features
python -m src.main hf extract path/to/audio.mp3 --output features.json

# Separate stems
python -m src.main hf separate path/to/audio.mp3 --output-dir ./stems --save-stems

# Detect beats
python -m src.main hf beats path/to/audio.mp3 --output beats.json

# Analyze drums
python -m src.main hf analyze-drums audio.wav --visualize

# Other available commands
python -m src.main hf drum-patterns audio.wav --mode pattern
python -m src.main hf tag audio.wav --categories "genre:techno,house,ambient"
python -m src.main hf caption audio.wav --mix-notes
python -m src.main hf similarity audio.wav --mode timestamps --query "bass drop"
python -m src.main hf realtime-beats --file --input audio.wav

Python API Usage

from heihachi.huggingface import FeatureExtractor, StemSeparator, BeatDetector

# Extract features
extractor = FeatureExtractor(model="microsoft/BEATs-base")
features = extractor.extract(audio_path="track.mp3")

# Separate stems
separator = StemSeparator()
stems = separator.separate(audio_path="track.mp3")
drums = stems["drums"]
bass = stems["bass"]

# Detect beats
detector = BeatDetector()
beats = detector.detect(audio_path="track.mp3", visualize=True, output_path="beats.png")
print(f"Tempo: {beats['tempo']} BPM")

Experimental Results

This section presents visualization results from audio analysis examples processed through the Heihachi framework, demonstrating the capabilities of the system in extracting meaningful insights from audio data.

Drum Hit Analysis

The following visualizations showcase the results from analyzing drum hits within a 33-minute electronic music mix. The analysis employs a multi-stage approach:

Onset Detection: Using adaptive thresholding with spectral flux and phase deviation to identify percussion events
Drum Classification: Neural network classification to categorize each detected hit
Confidence Scoring: Model-based confidence estimation for each classification
Temporal Analysis: Pattern recognition across the timeline of detected hits

Analysis Overview

The analysis identified 91,179 drum hits spanning approximately 33 minutes (1999.5 seconds) of audio. The percussion events were classified into five primary categories with the following distribution:

Hi-hat: 26,530 hits (29.1%)
Snare: 16,699 hits (18.3%)
Tom: 16,635 hits (18.2%)
Kick: 16,002 hits (17.6%)
Cymbal: 15,313 hits (16.8%)

These classifications were derived using a specialized audio recognition model that separates and identifies percussion components based on their spectral and temporal characteristics.

Drum Hit Density Timeline

The density plot reveals the distribution of drum hits over time, providing insight into the rhythmic structure and intensity variations throughout the mix. Notable observations include:

Clear sections of varying percussion density, indicating track transitions and arrangement changes
Consistent underlying beat patterns maintained throughout the mix
Periodic intensity peaks corresponding to build-ups and drops in the arrangement

Pattern Visualization

The heatmap visualization represents normalized hit density across time for each drum type, revealing:

Structured patterns in kick and snare placement, typical of electronic dance music
Variations in hi-hat and cymbal usage that correspond to energy shifts
Clearly defined segments with distinct drum programming approaches

Detailed Timeline Analysis

The timeline visualization provides a comprehensive view of all drum events plotted against time, allowing for detailed analysis of the rhythmic structure. Key observations from this temporal analysis include:

Microtiming Variations: Subtle deviations from the quantized grid, particularly evident in hi-hats and snares, contribute to the human feel of the percussion
Structural Markers: Clear delineation of musical sections visible through changes in drum event density and type distribution
Layering Techniques: Overlapping drum hits at key points (e.g., stacked kick and cymbal events) to create impact moments
Rhythmic Motifs: Recurring patterns of specific drum combinations that serve as stylistic identifiers throughout the mix

The temporal analysis employed statistical methods to identify:

Event Clustering: Hierarchical clustering based on temporal proximity, velocity, and drum type
Pattern Detection: N-gram analysis of drum sequences to identify common motifs
Grid Alignment: Adaptive grid inference to determine underlying tempo and quantization
Transition Detection: Change-point analysis to identify structural boundaries

These analytical methods reveal the sophisticated rhythmic programming underlying the seemingly straightforward electronic beat patterns, with calculated variation applied to create both consistency and interest.

Hit Classification Confidence

The confidence metrics for the drum classification model demonstrate varying levels of certainty depending on the drum type:

Drum Type	Avg. Confidence	Avg. Velocity
Tom	0.385	1.816
Snare	0.381	1.337
Kick	0.370	0.589
Cymbal	0.284	1.962
Hi-hat	0.223	1.646

The confidence scores reflect the model's certainty in classification, with higher values for toms and snares suggesting these sounds have more distinctive spectral signatures. Meanwhile, velocity measurements indicate the relative energy of each hit, with cymbals and toms showing the highest average values.

Classification Performance Analysis

The scatter plot visualization reveals the relationship between classification confidence and hit velocity across all percussion events. This analysis provides critical insights into the performance of the neural classification model:

Velocity-Confidence Correlation: The plot demonstrates a positive correlation between hit velocity and classification confidence for most drum types, particularly evident in the upper-right quadrant where high-velocity hits receive more confident classifications.
Type-Specific Clusters: Each percussion type forms distinct clusters in the confidence-velocity space, with:
- Kicks (blue): Concentrated in the low-velocity, medium-confidence region
- Snares (orange): Forming a broad distribution across medium velocities with varying confidence
- Toms (green): Creating a distinctive cluster in the high-velocity, high-confidence region
- Hi-hats (red): Showing the widest distribution, indicating greater variability in classification performance
- Cymbals (purple): Forming a more diffuse pattern at higher velocities with moderate confidence
Classification Challenges: The lower confidence regions (bottom half of the plot) indicate areas where the model experiences greater uncertainty, particularly:
- Low-velocity hits across all percussion types
- Overlapping spectral characteristics between similar percussion sounds (e.g., certain hi-hats and cymbals)
- Boundary cases where multiple drum types may be present simultaneously
Performance Insights: The density of points in different regions provides a robust evaluation metric for the classification model, revealing both strengths in distinctive percussion identification and challenges in boundary cases.

This visualization serves as a valuable tool for evaluating classification performance and identifying specific areas for model improvement in future iterations of the framework.

Interactive Timeline

The drum hit analysis also generated an interactive HTML timeline that allows for detailed exploration of the percussion events. This visualization maps each drum hit across time with interactive tooltips displaying precise timing, confidence scores, and velocity information.

The interactive timeline is available at:

visualizations/drum_feature_analysis/interactive_timeline.html

To view the interactive timeline alongside the music:

Open the interactive timeline HTML file in a browser
In a separate browser tab, play the corresponding audio mix
Synchronize playback position to explore the relationship between audio and detected drum events

Technical Implementation Notes

The drum hit analysis pipeline employs several advanced techniques:

Onset Detection Algorithm: Utilizes a combination of spectral flux, high-frequency content (HFC), and complex domain methods to detect percussion events with high temporal precision (±5ms).
Neural Classification: Implements a specialized convolutional neural network trained on isolated drum samples to classify detected onsets into specific percussion categories.
Confidence Estimation: Employs softmax probability outputs from the classification model to assess classification reliability, with additional weighting based on signal-to-noise ratio and onset clarity.
Pattern Recognition: Applies a sliding-window approach with dynamic time warping (DTW) to identify recurring rhythmic patterns and variations.
Memory-Optimized Processing: Implements chunked processing with a sliding window approach to handle large audio files while maintaining consistent analysis quality.

The complete analysis was performed using the following command:

python -m src.main hf analyze-drums /path/to/mix.mp3 --visualize

Limitations and Future Improvements

Current limitations of the drum analysis include:

Occasional misclassification between similar drum types (e.g., toms vs. snares)
Limited ability to detect layered drum hits occurring simultaneously
Reduced accuracy during segments with heavy processing effects

Future improvements will focus on:

Enhanced separation of overlapping drum sounds
Tempo-aware pattern recognition
Integration with musical structure analysis
Improved classification of electronic drum sounds and synthesized percussion

Performance Optimizations

Memory Management

Streaming processing for large files
Efficient cache utilization
GPU memory optimization
Automatic garbage collection optimization
Chunked loading for very large files
Audio validation at each processing stage

Parallel Processing

Multi-threaded feature extraction
Batch processing capabilities
Distributed analysis support
Adaptive resource allocation
Scalable parallel execution

Storage Efficiency

Compressed result storage
Metadata indexing
Version control for analysis results
Simple, consistent path handling

Applications

1. DJ Mix Analysis

Track boundary detection
Transition type classification
Mix structure analysis
Energy flow visualization

2. Production Analysis

Sound design deconstruction
Arrangement analysis
Effect chain detection
Reference track comparison

3. Music Information Retrieval

Similar track identification
Style classification
Groove pattern matching
VIP/Dubplate detection

Future Directions

Enhanced Neural Processing
- Integration of deep learning models
- Real-time processing capabilities
- Adaptive threshold optimization
Extended Analysis Capabilities
- Additional genre support
- Extended effect detection
- Advanced pattern recognition
- Further error resilience improvements
Improved Visualization
- Interactive dashboards
- 3D visualization options
- Real-time visualization
- Error diagnostics visualization

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this framework in your research, please cite:

@software{heihachi2024,
  title = {Heihachi: Neural Processing of Electronic Music},
  author = {Kundai Farai Sachikonye},
  year = {2024},
  url = {https://github.com/fullscreen-triangle/heihachi}
}

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
configs		configs
core/engine		core/engine
docs		docs
examples		examples
scripts		scripts
src		src
visualizations		visualizations
.gitignore		.gitignore
Cargo.toml		Cargo.toml
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
api_server.py		api_server.py
heihachi.png		heihachi.png
pyproject.toml		pyproject.toml
requirements-autobahn.txt		requirements-autobahn.txt
requirements-fire-interface.txt		requirements-fire-interface.txt
requirements-huggingface.txt		requirements-huggingface.txt
requirements.txt		requirements.txt
setup.py		setup.py

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fullscreen-triangle/heihachi

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Heihachi

Heihachi Audio Analysis Framework

🔥 Revolutionary Fire-Based Emotion Interface

The Science Behind Fire-Emotion Mapping

🧠 Autobahn Integration: Delegated Probabilistic Reasoning

Table of Contents

🔥 Fire-Based Emotion Interface

Revolutionary Emotional Querying System

How It Works

Technical Innovation

🧠 Autobahn Integration: Delegated Probabilistic Reasoning

Revolutionary Architecture Delegation

Autobahn System Overview

Integration Benefits

Delegation Architecture

🦀 Rust-Powered Architecture

High-Performance Core Engine

Hybrid Language Benefits

Overview

Features

Installation

Quick Install

Options

Manual Installation

Usage

Basic Usage

Interactive Mode

Export Options

Command-Line Interface (CLI)

Command-Line Options

Examples

Processing Results

Fire Interface Usage

Starting the Fire Interface

Fire Interface Workflow

API Integration

Advanced Fire Controls

Autobahn Integration Usage

Starting the Integrated System

Delegated Processing Workflow

Advanced Integration Features

Performance Monitoring

Configuration Management

Theoretical Foundation

Neural Basis of Rhythm Processing

Motor-Auditory Coupling

Mathematical Framework

Core Components

1. Feature Extraction Pipeline

Rhythmic Analysis

Spectral Analysis

Component Analysis

2. Alignment Modules

Amen Break Analysis

Prior Subspace Analysis

Composite Similarity

3. Annotation System

Peak Detection

Segment Clustering

Transition Detection

4. Robust Processing Framework

Error Handling and Validation

Memory Management

Signal Processing Enhancements

REST API

Quick Start

API Endpoints

Usage Examples

1. Analyze Single Audio File

2. Extract Features

3. Detect Beats

4. Analyze Drums

5. Separate Stems

6. Batch Processing

Packages