filharmonia-ast

Filharmonia AST - Concert Audio Classifier

Pre-trained Audio Spectrogram Transformer for classifying concert recordings.

Model: Audio Spectrogram Transformer (MIT/PSLA) Trained on: Classical philharmonic concert recordings Test accuracy: 97.75%

Classes

• MUSIC - orchestral music performance
• APPLAUSE - audience applause (marks piece boundaries)
• SPEECH - announcements, introductions
• PUBLIC - crowd noise, intermissions
• TUNING - orchestra tuning

Usage

Option 1: With Hugging Face Transformers

import torch
from transformers import ASTFeatureExtractor, ASTForAudioClassification

# Download model
model = ASTForAudioClassification.from_pretrained("szymontex/filharmonia-ast")
feature_extractor = ASTFeatureExtractor.from_pretrained("MIT/ast-finetuned-audioset-10-10-0.4593")

# Your audio processing code here

Option 2: With Filharmonia AI

Use with Filharmonia AI:

1. Download ast_20251009_222204.pth
2. Place in RECOGNITION_MODELS/ast_active.pth
3. Run analysis through web UI

Model Details

Architecture:

• Base: MIT/PSLA Audio Spectrogram Transformer
• Fine-tuned on concert recordings
• Input: 16kHz audio, 2.97s segments
• Features: Fbank (filter bank), 1024 time frames × 128 freq bins

Training:

• Dataset: ~1200 min of classical concert recordings
• Split: 80% train / 10% val / 10% test
• Batch size: 32
• Epochs: 10
• Balance strength: 0.75

Performance:

Limitations

• Trained on classical orchestral concerts only
• May not work well on rock, jazz, pop, or other genres
• Requires retraining for different audio types
• Best results with similar recording conditions (concert hall acoustics)

Training Your Own

For non-classical music or different use cases:

1. Clone filharmonia-ai
2. Collect your audio samples
3. Export segments to training data
4. Retrain via web UI

See Development Guide for details.

License

MIT - same as code repository

Citation

@software{filharmonia_ast,
  author = {Szymon Gwóźdź},
  title = {Filharmonia AST: Concert Audio Classifier},
  year = {2025},
  url = {https://github.com/szymontex/filharmonia-ai}
}

Links

• Code: https://github.com/szymontex/filharmonia-ai
• Documentation: https://github.com/szymontex/filharmonia-ai/blob/master/docs/DEVELOPMENT.md
• API Reference: https://github.com/szymontex/filharmonia-ai/blob/master/docs/API.md