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.

Features

• 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

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

HuggingFace Integration

Heihachi now includes powerful integration with HuggingFace's Transformers library, enabling advanced neural processing of audio using state-of-the-art models.

Available Models

The following specialized audio analysis models are available:

Model Type	Description	Default Model
Drum Sound Analysis	Advanced drum sound classification using Wav2Vec2	JackArt/wav2vec2-for-drum-classification
Drum Analysis	General drum kit sound analysis	DunnBC22/wav2vec2-base-Drum_Kit_Sounds
Audio-Text Similarity	Compare audio to text descriptions using CLAP	laion/clap-htsat-fused
Zero-Shot Tagging	Open-vocabulary audio tagging	UniMus/OpenJMLA
Audio Captioning	Generate textual descriptions of audio	slseanwu/beats-conformer-bart-audio-captioner
Real-Time Beat Tracking	Low-latency beat detection for live performance	beast-team/beast-dione
Feature Extraction	Extract high-level audio features	microsoft/BEATs-base
Stem Separation	Separate audio into vocals/drums/bass/other	htdemucs
Beat Detection	Detect beats and estimate tempo	amaai/music-tempo-beats

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: "amaai/music-tempo-beats"

# Additional models (disabled by default to save resources)
drum_sound_analysis:
  enabled: false
  model: "JackArt/wav2vec2-for-drum-classification"

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

# See configs/huggingface.yaml for all available options

Command-Line Usage

Analyze audio files using HuggingFace models:

# Process a file with the default pipeline (includes enabled HuggingFace models)
python -m src.main file.wav --config configs/huggingface.yaml

# Run specific HuggingFace analysis commands
python -m src.main huggingface extract file.wav --model "microsoft/BEATs-base"
python -m src.main huggingface drum-patterns file.wav --mode pattern --export-format midi
python -m src.main huggingface tag file.wav --tags "electronic,ambient,techno,bass"
python -m src.main huggingface caption file.wav --sentiment
python -m src.main huggingface similarity file.wav --mode text --queries "techno" "jungle" "ambient"
python -m src.main huggingface realtime-beats --file --input file.wav --export-beats --export-format json

Real-Time Processing

Some models support real-time processing:

# Run real-time beat tracking demo from microphone input
python -m src.main huggingface realtime-beats --duration 30.0

Available Commands

Command	Description	Example
extract	Extract features using neural models	huggingface extract audio.wav
analyze-drums	Analyze drum sounds	huggingface analyze-drums audio.wav --visualize
drum-patterns	Detect and analyze drum patterns	huggingface drum-patterns audio.wav --mode pattern
tag	Perform zero-shot audio tagging	huggingface tag audio.wav --categories "genre:techno,house,ambient"
caption	Generate audio descriptions	huggingface caption audio.wav --mix-notes
similarity	Audio-text similarity analysis	huggingface similarity audio.wav --mode timestamps --query "bass drop"
realtime-beats	Real-time beat tracking	huggingface realtime-beats --file --input audio.wav

Python API Usage

from src.huggingface import DrumSoundAnalyzer, AudioCaptioner, RealTimeBeatTracker

# Analyze drum patterns
analyzer = DrumSoundAnalyzer()
hits = analyzer.detect_drum_hits("audio.wav")
pattern = analyzer.create_drum_pattern(hits, quantize=True, tempo=170.0)
analyzer.export_pattern(pattern, output_format="midi", file_path="pattern.mid")

# Generate audio caption with sentiment analysis
captioner = AudioCaptioner()
results = captioner.caption_with_sentiment("audio.wav")
print(f"Caption: {results['caption']}")
print(f"Sentiment: {results['sentiment']['dominant']}")

# Track beats in real-time
tracker = RealTimeBeatTracker()
tracker.run_real_time_demo(duration=30.0, visualize=True)

Performance Tuning

Heihachi can be optimized for different hardware configurations:

# Use GPU acceleration
heihachi process audio.wav --gpu

# Specify number of processing workers
heihachi process audio_dir/ --workers 4

# Adjust memory usage
heihachi process audio_dir/ --memory-limit 2048

A full list of performance settings can be found in configs/performance.yaml.

Interactive Explorer and Progress Utilities

Interactive Explorer

Heihachi provides two interactive interfaces for exploring analysis results:

Command-Line Explorer

The command-line explorer allows you to interactively explore audio analysis results:

# Start the CLI explorer
python -m src.cli.interactive_cli --results-dir /path/to/results

Available commands:

• list - List available result files
• open <index/filename> - Open a result file
• summary - Show summary of current result
• info <feature> - Show detailed feature information
• plot <type> [feature] - Generate visualizations
• compare <feature> [files] - Compare features across files
• export <format> [filename] - Export to different formats
• help - Show available commands

Web UI Explorer

A browser-based interface for visualizing and exploring analysis results:

# Start the web UI
python -m src.cli.interactive_cli --web --results-dir /path/to/results

Options:

• --host - Host to bind to (default: localhost)
• --port - Port to use (default: 5000)
• --no-browser - Don't automatically open browser

Progress Utilities

The framework includes several utilities for tracking progress in long-running operations:

Progress Context

from src.utils.progress import progress_context

with progress_context("Processing audio", total=100) as progress:
    for i in range(100):
        # Do some work
        progress.update(1)

Progress Manager

For tracking multiple operations simultaneously:

from src.utils.progress import ProgressManager

manager = ProgressManager()
with manager.task("Processing audio", total=100) as task1:
    with manager.task("Extracting features", total=50) as task2:
        # Operations with nested progress tracking

CLI Progress Bar

For simple command-line progress indication:

from src.utils.progress import cli_progress_bar

for file in cli_progress_bar(files, desc="Processing files"):
    # Process each file

Demonstration

A demonstration script is available to showcase the interactive explorer and progress utilities:

python scripts/interactive_demo.py

This script demonstrates:

1. Progress tracking during sample audio processing
2. Interactive exploration of analysis results
3. Various visualization capabilities

Options:

• --skip-processing - Skip processing and use existing results
• --progress-demo - Show only progress indicators demonstration

Advanced Usage

Custom Pipelines

# Example pipeline configuration (pipeline.yaml)
stages:
  - name: load
    processor: AudioLoader
    params:
      sample_rate: 44100
  
  - name: analyze
    processor: SpectrumAnalyzer
    params:
      n_fft: 2048
      hop_length: 512
  
  - name: extract
    processor: FeatureExtractor
    params:
      features: [mfcc, chroma, tonnetz]

Run with custom pipeline:

heihachi process audio.wav --pipeline pipeline.yaml

Development

Running Tests

# Run all tests
pytest

# Run specific test modules
pytest tests/test_audio_processing.py

Code Formatting

# Format code
black src/ tests/

# Check typing
mypy src/

License

MIT

Heihachi: Neural Processing of Electronic Music

Overview

Heihachi is a high-performance audio analysis framework designed for electronic music, with a particular focus on neurofunk and drum & bass genres. The system implements novel approaches to audio analysis by combining neurological models of rhythm processing with advanced signal processing techniques.

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:

1. Beat-based Timing Networks

   • Basal ganglia-thalamocortical circuits
   • Supplementary motor area (SMA)
   • Premotor cortex (PMC)

2. 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

Extended Mathematical Framework

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

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

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

2. Groove Pattern Analysis: For microtiming deviations:

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

3. Amen Break Detection: Pattern matching score:

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

4. Reese Bass Analysis: For analyzing modulation and phase relationships:

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

Where:

• $A_k(t)$ is the amplitude of the k-th component
• $\phi_k(t)$ is the phase of the k-th component

5. Transition Detection: For identifying mix points and transitions:

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

Where:

• $E(t)$ is energy change
• $S(t)$ is spectral change
• $H(t)$ is harmonic change
• $\alpha, \beta, \gamma$ are weighting factors

6. Similarity Computation: For comparing audio segments:

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

Where:

• $sim_i(x,y)$ is the similarity for feature i
• $w_i$ is the weight for feature i

7. Segment Clustering: Using DBSCAN with adaptive distance:

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

Where:

• $f_i(p)$ is feature i of point p
• $\lambda_i$ is the importance weight for feature i

Additional Mathematical Framework

8. Bass Design Analysis: For analyzing Reese bass modulation depth:

$$ M(t) = \frac{max(A(t)) - min(A(t))}{max(A(t)) + min(A(t))} $$

9. Effect Chain Detection: For compression ratio estimation:

$$ CR(x) = \frac{\Delta_{in}}{\Delta_{out}} = \frac{x_{in,2} - x_{in,1}}{x_{out,2} - x_{out,1}} $$

10. Pattern Recognition: For rhythmic similarity using dynamic time warping:

$$ DTW(X,Y) = min\left(\sum_{k=1}^K w_k \cdot d(x_{i_k}, y_{j_k})\right) $$

11. Transition Analysis: For blend detection using cross-correlation:

$$ R_{xy}(\tau) = \sum_{n=-\infty}^{\infty} x(n)y(n+\tau) $$

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

Extended Pipeline Architecture

graph LR
    A[Audio Stream] --> B[Preprocessing]
    B --> C[Feature Extraction]
    C --> D[Component Analysis]
    D --> E[Pattern Recognition]
    E --> F[Result Generation]
    
    subgraph "Feature Extraction"
    C1[Spectral] --> C2[Temporal]
    C2 --> C3[Rhythmic]
    end

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Command Line Interface (CLI)

Heihachi provides a powerful command-line interface to analyze audio files. The CLI allows you to process individual audio files or entire directories of audio files.

Installation

First, ensure the package is installed:

# Clone the repository
git clone https://github.com/your-username/heihachi.git
cd heihachi

# Install dependencies
pip install -r requirements.txt

# Install the package in development mode
pip install -e .

Basic Usage

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:

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

Amen Break Analysis

For specific Amen break analysis, ensure the reference Amen break sample is available in the ../public/amen_break.wav path. The analysis will detect Amen break variations, their timing, and provide confidence scores.

Example for specifically analyzing Amen breaks:

python -m src.main /path/to/jungle_track.wav

The output will indicate if Amen break patterns were detected, along with timestamps and variation information.

Neurofunk Component Analysis

graph TD
    A[Input Signal] --> B[Sub-bass Extraction]
    A --> C[Reese Detection]
    A --> D[Drum Pattern Analysis]
    B --> E[Bass Pattern]
    C --> E
    D --> F[Rhythm Grid]
    E --> G[Component Fusion]
    F --> G

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Feature Extraction Pipeline

graph TD
    A[Audio Input] --> B[Preprocessing]
    B --> C[Feature Extraction]
    
    subgraph "Feature Extraction"
        C1[Spectral Analysis] --> D1[Sub-bass]
        C1 --> D2[Mid-range]
        C1 --> D3[High-freq]
        
        C2[Temporal Analysis] --> E1[Envelope]
        C2 --> E2[Transients]
        
        C3[Rhythmic Analysis] --> F1[Beats]
        C3 --> F2[Patterns]
    end
    
    D1 --> G[Feature Fusion]
    D2 --> G
    D3 --> G
    E1 --> G
    E2 --> G
    F1 --> G
    F2 --> G

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Annotation System Flow

graph LR
    A[Audio Stream] --> B[Peak Detection]
    B --> C[Segment Creation]
    C --> D[Pattern Analysis]
    D --> E[Clustering]
    
    subgraph "Pattern Analysis"
        D1[Drum Patterns]
        D2[Bass Patterns]
        D3[Effect Patterns]
    end

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Audio Scene Analysis

graph TD
    A[Input Signal] --> B[Background Separation]
    A --> C[Foreground Analysis]
    
    subgraph "Background"
        B1[Ambient Detection]
        B2[Noise Floor]
        B3[Reverb Tail]
    end
    
    subgraph "Foreground"
        C1[Transient Detection]
        C2[Note Events]
        C3[Effect Events]
    end

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Result Fusion Process

graph LR
    A[Component Results] --> B[Confidence Scoring]
    B --> C[Weight Assignment]
    C --> D[Fusion]
    D --> E[Final Results]
    
    subgraph "Confidence Scoring"
        B1[Pattern Confidence]
        B2[Feature Confidence]
        B3[Temporal Confidence]
    end

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Neurofunk-Specific Analysis

Bass Sound Design Analysis

1. Reese Bass Components:
   - Fundamental frequency tracking
   - Phase relationship analysis
   - Modulation pattern detection
   - Harmonic content analysis

2. Sub Bass Characteristics:
   - Frequency range: 20-60 Hz
   - Envelope characteristics
   - Distortion analysis
   - Phase alignment

Effect Chain Detection

1. Signal Chain Analysis:
   - Compression detection
   - Distortion identification
   - Filter resonance analysis
   - Modulation effects

2. Processing Order:
   - Pre/post processing detection
   - Parallel processing identification
   - Send/return effect analysis

Pattern Transformation Analysis

1. Rhythmic Transformations:
   - Time-stretching detection
   - Beat shuffling analysis
   - Groove template matching
   - Syncopation patterns

2. Spectral Transformations:
   - Frequency shifting
   - Harmonic manipulation
   - Formant preservation
   - Resynthesis detection

Implementation Details

Audio Processing Pipeline

1. Preprocessing

   - Sample rate normalization (44.1 kHz)
   - Stereo to mono conversion when needed
   - Segment-wise processing for large files
   - Empty audio detection and validation
   - Signal integrity verification

2. Feature Extraction

   - Multi-threaded processing
   - GPU acceleration where available
   - Efficient memory management
   - Caching system for intermediate results
   - Hann window application to prevent spectral leakage

3. Analysis Flow

   - Cascading analysis system
   - Component-wise processing
   - Result fusion and validation
   - Confidence scoring
   - Graceful error handling and recovery

Performance Optimizations

1. 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

2. Parallel Processing

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

3. Storage Efficiency

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

Error Handling and Recovery

1. Robust Processing

   - Validation of audio signals before processing
   - Fallback mechanisms for empty or corrupted audio
   - Graceful recovery from processing errors
   - Comprehensive logging of failure points

2. Data Integrity

   - Sample rate validation and automatic correction
   - Empty array detection and prevention
   - Default parameter settings when configuration is missing
   - Prevention of null pointer exceptions in signal processing chain

3. Processing Resilience

   - Windowing functions to eliminate warnings and improve spectral quality
   - Automatic memory management for very large files
   - Adaptive parameter selection based on file size
   - Simplified path handling with hardcoded relative paths

Performance Metrics

For evaluating analysis accuracy:

$$ Accuracy_{component} = \frac{TP + TN}{TP + TN + FP + FN} $$

Where:

• TP: True Positives (correctly identified patterns)
• TN: True Negatives (correctly rejected non-patterns)
• FP: False Positives (incorrectly identified patterns)
• FN: False Negatives (missed patterns)

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

Visualization and Reporting

The framework includes comprehensive visualization tools for:

• Spectral analysis results
• Component relationships
• Groove patterns
• Transition points
• Similarity matrices
• Analysis confidence scores

Future Directions

1. Enhanced Neural Processing

   • Integration of deep learning models
   • Real-time processing capabilities
   • Adaptive threshold optimization

2. Extended Analysis Capabilities

   • Additional genre support
   • Extended effect detection
   • Advanced pattern recognition
   • Further error resilience improvements

3. Improved Visualization

   • Interactive dashboards
   • 3D visualization options
   • Real-time visualization
   • Error diagnostics visualization

Extended References

1. Chen, J. L., Penhune, V. B., & Zatorre, R. J. (2008). Listening to musical rhythms recruits motor regions of the brain. Cerebral Cortex, 18(12), 2844-2854.

2. Cannon, J. J., & Patel, A. D. (2020). How beat perception co-opts motor neurophysiology. Trends in Cognitive Sciences, 24(1), 51-64.

3. Fukuie, T., et al. (2022). Neural entrainment reflects temporal predictions guiding speech comprehension. Current Biology, 32(5), 1051-1067.

4. Smith, J. O. (2011). Spectral Audio Signal Processing. W3K Publishing.

5. Bello, J. P., et al. (2005). A Tutorial on Onset Detection in Music Signals. IEEE Transactions on Speech and Audio Processing.

6. Gouyon, F., & Dixon, S. (2005). A Review of Automatic Rhythm Description Systems. Computer Music Journal.

7. Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51-83.

8. McFee, B., et al. (2015). librosa: Audio and music signal analysis in Python. Proceedings of the 14th Python in Science Conference.

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 = {[Author Names]},
  year = {2024},
  url = {https://github.com/[username]/heihachi}
}

Extended System Architecture

graph TD
    A[Audio Input] --> B[Feature Extraction]
    B --> C[Analysis Pipeline]
    C --> D[Results Generation]

    subgraph "Feature Extraction"
        B1[Spectral] --> B2[Temporal]
        B2 --> B3[Rhythmic]
        B3 --> B4[Component]
    end

    subgraph "Analysis Pipeline"
        C1[Pattern Recognition]
        C2[Similarity Analysis]
        C3[Structure Analysis]
        C4[Effect Analysis]
    end

    subgraph "Results Generation"
        D1[Visualization]
        D2[Storage]
        D3[Export]
    end

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Robust Audio Processing Pipeline

graph TD
    A[Audio Input] --> B[Input Validation]
    B -->|Valid| C[Chunked Loading]
    B -->|Invalid| E[Error Handling]
    C --> D[Signal Processing]
    D --> F[Result Generation]
    E --> G[Recovery Mechanisms]
    G -->|Recoverable| C
    G -->|Not Recoverable| H[Error Reporting]
    
    subgraph "Input Validation"
        B1[File Exists Check]
        B2[Format Validation]
        B3[Sample Rate Check]
        B4[Empty Signal Check]
    end

    subgraph "Signal Processing"
        D1[Window Function Application]
        D2[Spectral Analysis with Hann Window]
        D3[Memory-Optimized Processing]
        D4[Null Value Handling]
    end

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Neurofunk Component Interaction

graph LR
    A[Audio Stream] --> B[Component Separation]
    B --> C[Feature Analysis]
    C --> D[Pattern Recognition]
    
    subgraph "Component Separation"
        B1[Sub Bass]
        B2[Reese Bass]
        B3[Drums]
        B4[Effects]
    end

    subgraph "Feature Analysis"
        C1[Spectral Features]
        C2[Temporal Features]
        C3[Modulation Features]
    end

    subgraph "Pattern Recognition"
        D1[Rhythmic Patterns]
        D2[Effect Patterns]
        D3[Bass Patterns]
    end

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Processing Pipeline Details

graph TD
    A[Input] --> B[Preprocessing]
    B --> C[Analysis]
    C --> D[Results]

    subgraph "Preprocessing"
        B1[Normalization]
        B2[Segmentation]
        B3[Enhancement]
    end

    subgraph "Analysis"
        C1[Feature Extraction]
        C2[Pattern Analysis]
        C3[Component Analysis]
    end

    subgraph "Results"
        D1[Metrics]
        D2[Visualizations]
        D3[Reports]
    end

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Technical Implementation Details

Bass Sound Design Analysis

class BassAnalyzer:
    """Advanced bass analysis system."""
    
    def analyze_reese(self, audio: np.ndarray) -> Dict:
        """
        Analyze Reese bass characteristics.
        
        Parameters:
            audio (np.ndarray): Input audio signal
            
        Returns:
            Dict containing:
            - modulation_depth: Float
            - phase_correlation: Float
            - harmonic_content: np.ndarray
            - stereo_width: Float
        """
        pass

    def analyze_sub(self, audio: np.ndarray) -> Dict:
        """
        Analyze sub bass characteristics.
        
        Parameters:
            audio (np.ndarray): Input audio signal
            
        Returns:
            Dict containing:
            - fundamental_freq: Float
            - energy: Float
            - phase_alignment: Float
            - distortion: Float
        """
        pass

Effect Chain Analysis

class EffectChainAnalyzer:
    """Advanced effect chain analysis."""
    
    def detect_chain(self, audio: np.ndarray) -> List[Dict]:
        """
        Detect processing chain order.
        
        Parameters:
            audio (np.ndarray): Input audio signal
            
        Returns:
            List[Dict] containing detected effects in order:
            - effect_type: str
            - parameters: Dict
            - confidence: Float
        """
        pass

    def analyze_parallel(self, audio: np.ndarray) -> Dict:
        """
        Analyze parallel processing.
        
        Parameters:
            audio (np.ndarray): Input audio signal
            
        Returns:
            Dict containing:
            - bands: List[Dict]
            - routing: Dict
            - blend_type: str
        """
        pass

Robust Audio Processing

class AudioProcessor:
    """Memory-efficient and fault-tolerant audio processing."""
    
    def load(self, file_path: str, start_time: float = 0.0, end_time: float = None) -> np.ndarray:
        """
        Load audio file with robust error handling.
        
        Parameters:
            file_path: Path to the audio file
            start_time: Start time in seconds
            end_time: End time in seconds (or None for full file)
            
        Returns:
            numpy.ndarray: Audio data
            
        Raises:
            ValueError: If file empty or corrupted
        """
        # Validates file existence
        # Checks for empty audio
        # Handles sample rate conversion
        # Returns normalized audio data
        pass
    
    def _chunked_load(self, file_path: str, start_time: float, end_time: float) -> np.ndarray:
        """
        Memory-efficient loading for large audio files.
        
        Parameters:
            file_path: Path to the audio file
            start_time: Start time in seconds
            end_time: End time in seconds
            
        Returns:
            numpy.ndarray: Audio data
        """
        # Processes file in manageable chunks
        # Handles memory efficiently
        # Validates output before returning
        pass