neckenml-analyzer 0.1.1.post1

Swedish folk music audio analysis and dance style classification

NeckenML Analyzer

Swedish folk music audio analysis and dance style classification using machine learning.

Overview

NeckenML Analyzer is a Python package that provides advanced audio analysis and automatic dance style classification for Swedish folk music. It uses a combination of signal processing, machine learning, and domain-specific heuristics to:

• Analyze audio features: BPM, meter (ternary/binary), swing ratio, vocal presence, articulation, bounciness, and more
• Classify dance styles: Polska, Hambo, Vals, Polka, Schottis, Snoa, and other Swedish folk dance types
• Assess authenticity: Distinguish traditional folk recordings from modern/electronic interpretations

Features

• Comprehensive Audio Analysis

  • Tempo and beat detection using Madmom RNN (optimized for rubato in folk music)
  • Meter classification (3/4 ternary vs 2/4/4/4 binary)
  • MusiCNN embeddings for audio texture fingerprinting
  • Vocal vs instrumental detection
  • Swing ratio calculation
  • Articulation analysis (smooth/staccato/punchy)
  • Folk-specific features (Polska vs Hambo signatures)

• Machine Learning Classification

  • Pre-trained RandomForest classifier included
  • Hierarchical decision-making (metadata → ML → heuristics)
  • Confidence scores for each prediction
  • Support for model retraining with custom data

• Extensible Architecture

  • Abstract AudioSource interface for flexible audio acquisition
  • Built-in file-based source
  • Easy to implement custom sources (S3, HTTP, streaming, etc.)

Installation

1. Install the package

pip install neckenml-analyzer

2. Set up PostgreSQL

neckenml Analyzer uses PostgreSQL with the pgvector extension for storing embeddings:

# Create database
createdb neckenml

# Enable pgvector extension
psql neckenml -c "CREATE EXTENSION vector;"

3. Download pre-trained models

The analyzer requires Essentia's MusiCNN models (not included due to licensing):

# Create models directory
mkdir -p ~/.neckenml/models

# Download MusiCNN embedding model
wget https://essentia.upf.edu/models/feature-extractors/musicnn/msd-musicnn-1.pb \
  -O ~/.neckenml/models/msd-musicnn-1.pb

# Download voice/instrumental classifier
wget https://essentia.upf.edu/models/audio-event-recognition/voice_instrumental/voice_instrumental-musicnn-msd-1.pb \
  -O ~/.neckenml/models/voice_instrumental-musicnn-msd-1.pb

Quick Start

from neckenml import AudioAnalyzer, StyleClassifier
from neckenml.sources import FileAudioSource

# Set up audio source (file-based)
source = FileAudioSource(audio_dir="/path/to/your/audio/files")

# Initialize analyzer with audio source
analyzer = AudioAnalyzer(
    audio_source=source,
    model_dir="~/.neckenml/models"  # Optional, uses default if not specified
)

# Analyze an audio file (track_id should match filename without extension)
features = analyzer.analyze(track_id="my_track")

# The features dict contains:
# - bpm: Tempo in beats per minute
# - meter: 'ternary' or 'binary'
# - swing_ratio: 0.0-1.0 (0.5 = straight, 0.67 = triplet feel)
# - vocal_probability: 0.0-1.0 (vocal vs instrumental)
# - embedding: 217-dimensional feature vector
# - and many more...

# Classify dance style
classifier = StyleClassifier()
result = classifier.classify(features)

print(f"Detected style: {result['primary_style']}")
print(f"Confidence: {result['confidence']:.1%}")
print(f"Secondary styles: {result['secondary_styles']}")

# Example output:
# Detected style: Polska
# Confidence: 85.0%
# Secondary styles: [('Slängpolska', 0.65)]

Advanced Usage

Artifact Persistence for Fast Re-analysis

Store expensive-to-compute artifacts once, then re-analyze instantly without touching audio files:

from neckenml import AudioAnalyzer, compute_derived_features

# Initial analysis with artifact storage
result = analyzer.analyze_file(
    file_path="/path/to/track.mp3",
    return_artifacts=True
)

features = result["features"]          # Derived features
artifacts = result["raw_artifacts"]     # Raw data to store

# Store artifacts in database (AnalysisSource.raw_data JSONB column)
db.store(artifacts)

# Later: Fast re-analysis from stored artifacts (300x faster!)
new_features = compute_derived_features(artifacts)

# Re-classify with updated model (no audio needed!)
new_features = compute_derived_features(artifacts, new_classifier=my_model)

Performance: Re-classify 1000 tracks in ~2 minutes instead of 8+ hours!

See Artifact Persistence Documentation for details.

Custom Audio Source

Implement the AudioSource interface for custom audio acquisition:

from neckenml.sources import AudioSource
import os

class CloudStorageAudioSource(AudioSource):
    """Fetch audio from cloud object storage"""

    def __init__(self, bucket_name, storage_client):
        self.client = storage_client
        self.bucket = bucket_name

    def fetch_audio(self, track_id: str) -> str:
        """Download audio file from cloud storage and return local path"""
        local_path = f"/tmp/{track_id}.mp3"
        self.client.download_file(
            bucket=self.bucket,
            key=f"audio/{track_id}.mp3",
            destination=local_path
        )
        return local_path

    def cleanup(self, file_path: str) -> None:
        """Clean up temporary file"""
        if os.path.exists(file_path):
            os.remove(file_path)

# Use custom source
source = CloudStorageAudioSource(bucket_name="my-music-bucket", storage_client=my_client)
analyzer = AudioAnalyzer(audio_source=source)

Retraining the Classifier

Train a custom model with your own labeled data:

from neckenml.training import TrainingService
import numpy as np

# Prepare training data
embeddings = np.array([...])  # Nx217 feature vectors from analyzer
labels = ["Polska", "Hambo", "Polska", ...]  # Dance style labels

# Train new model
trainer = TrainingService(model_path="./my_custom_model.pkl")
trainer.train_from_data(embeddings, labels)

# The classifier will automatically use the new model

Supported Dance Styles

Ternary (3/4 meter):

• Polska
• Slängpolska
• Hambo
• Vals (Waltz)
• Springlek
• Mazurka

Binary (2/4, 4/4 meter):

• Polka
• Schottis
• Snoa
• Gånglåt
• Engelska
• Marsch

Documentation

• Installation Guide - Detailed setup instructions
• Quick Start - Getting started examples
• Extending - Custom AudioSource implementations and model training

Architecture

NeckenML Analyzer uses a multi-stage pipeline:

1. Audio Acquisition: Flexible AudioSource interface
2. Feature Extraction: Madmom RNN for beat/rhythm analysis + Librosa for onsets
3. Embedding Generation: MusiCNN for 217-dim audio fingerprints
4. Folk Features: Domain-specific rhythm and meter analysis
5. Classification: Hierarchical decision tree (metadata → ML → heuristics)
6. Artifact Persistence: Store raw analysis outputs for instant re-classification

Requirements

• Python 3.9+
• PostgreSQL with pgvector extension
• Essentia pre-trained models (see installation instructions)

Contributing

We welcome contributions! Please see our Contributing Guide for details on:

• How to report bugs and suggest enhancements
• Development setup and coding standards
• Testing requirements and guidelines
• Pull request process

Whether you're fixing a bug, adding a feature, or improving documentation, your contributions help make Swedish folk music more accessible through technology.

License

MIT License - see LICENSE file for details.

Citation

If you use NeckenML Analyzer in your research, please cite:

@software{neckenml_analyzer,
  title = {NeckenML Analyzer: Swedish Folk Music Analysis and Classification},
  author = {NeckenML Contributors},
  year = {2025},
  url = {https://github.com/svnoak/neckenml-analyzer}
}

Acknowledgments

• Built with Essentia audio analysis library
• MusiCNN models by Jordi Pons et al.
• Powered by the Swedish folk music community