Model Card for audio_fingerprint

This model is designed for audio fingerprinting tasks, enabling robust audio hashing, duplicate detection, and audio segment retrieval based on the PFANN (Persistent Frequency Attention Neural Network) architecture.

Model Details

Model Description

The audio_fingerprint model generates compact, discriminative audio fingerprints for audio identification, duplicate detection, and content-based audio retrieval. It is built on the PFANN framework, focusing on efficient feature extraction and stable hash representation for real-world audio signals under noise, compression, and time stretching.

• Developed by: Yougen Yuan
• Funded by [optional]: Personal Research Project
• Shared by [optional]: Yougen Yuan
• Model type: Audio Fingerprinting / Audio Hashing / Neural Audio Retrieval
• Language(s) (NLP): N/A (audio-only model)
• License: Apache-2.0
• Finetuned from model [optional]: N/A

Model Sources [optional]

• Repository: https://github.com/ygyuan/pfann.git
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

Direct Use

• Audio duplicate detection
• Music / audio segment identification
• Near-duplicate audio retrieval
• Audio copyright monitoring
• Robust audio hashing under compression and noise

Downstream Use [optional]

• Integration into music recognition services
• Audio database deduplication systems
• Broadcast monitoring pipelines
• Multimodal systems requiring audio matching

Out-of-Scope Use

• Speech recognition or speech-to-text
• Music generation or audio synthesis
• High-fidelity audio reconstruction
• Malicious audio tampering or deepfake detection (not designed for)
• Critical safety applications without additional validation

Bias, Risks, and Limitations

• Performance may degrade under extreme noise, heavy distortion, or extreme time/pitch shifting.
• Trained on general audio/music data; may perform suboptimally on rare audio types or highly specialized soundscapes.
• Audio fingerprint uniqueness depends on signal complexity; short or highly repetitive audio may lead to hash collisions.
• Not designed for legally certified forensic audio analysis without further validation.

Recommendations

Users should evaluate performance on their target audio domain before deployment. For high-stakes applications, combine with additional verification steps. Avoid use in unmoderated automated takedown systems without human oversight.

How to Get Started with the Model

git clone https://github.com/ygyuan/pfann.git
cd pfann
# Follow installation and inference instructions in the repository README

Basic inference example (refer to repo for full usage):

from pfann import AudioFingerprint
model = AudioFingerprint.from_pretrained("Yougen/audio_fingerprint")
fingerprint = model.extract_fingerprint("audio_file.wav")

Training Details

Training Data

Trained on a diverse dataset of music, speech, and environmental audio, including augmented versions with noise, compression, time stretching, and volume variations.

Training Procedure

Preprocessing [optional]

• Audio resampling to fixed sample rate
• STFT / mel-spectrogram feature extraction
• Data augmentation: noise injection, MP3 compression simulation, time stretching

Training Hyperparameters

• Training regime: fp32 full precision

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Tested on held-out audio clips with various distortions: clean, noisy, compressed, time-stretched.

Factors

• Signal-to-noise ratio
• Compression bitrate
• Time-stretching factor
• Audio duration

Metrics

• True Positive Rate (TPR) at fixed False Positive Rate (FPR)
• Top-1 / Top-5 retrieval accuracy
• Hash robustness score

Results

[More Information Needed]

Summary

The model maintains high retrieval accuracy under common audio degradations, suitable for practical audio fingerprinting applications.

Model Examination [optional]

[More Information Needed]

Environmental Impact

• Hardware Type: NVIDIA GPU
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Based on PFANN (Persistent Frequency Attention Neural Network) for learning stable frequency-domain representations, optimized for binary hashing and similarity search.

Compute Infrastructure

Hardware

GPU with CUDA support (recommended)

Software

• Python
• PyTorch
• Librosa / TorchAudio
• NumPy, SciPy

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

• Audio Fingerprint: Compact representation of an audio signal for identification.
• PFANN: Persistent Frequency Attention Neural Network.
• Robust Hashing: Hashing resilient to benign signal modifications.

More Information [optional]

For full documentation, examples, and updates, see the GitHub repository.

Model Card Authors [optional]

Yougen Yuan

Model Card Contact

[More Information Needed]