Samid Drone Detector

--- language: en tags: - audio - audio-classification - drone-detection - acoustic license: mit --- # Samid drone detector Audio Spectrogram Transformer fine-tuned for binary acoustic drone detection. ## Quick use ```python from transformers import AutoModelForAudioClassification, AutoFeatureExtractor import torch, soundfile as sf model = AutoModelForAudioClassification.from_pretrained("Rashidbm/samid-drone-detector") fe = AutoFeatureExtractor.from_pretrained("Rashidbm/samid-drone-detector") audio, sr = sf.read("clip.wav") inputs = fe(audio, sampling_rate=16000, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits print(f"p(drone) = {torch.softmax(logits, dim=-1)[0, 1].item():.4f}") ``` ## Training - Backbone: `MIT/ast-finetuned-audioset-10-10-0.4593` - Datasets: geronimobasso/drone-audio-detection-samples, ahlab-drone-project/DroneAudioSet (splits 1-20) - Augmentations applied symmetrically across both classes: codec round-trip, synthetic RIR, random EQ, FilterAugment, Patchout, SpecAugment, Mixup. Asymmetric: urban-noise overlay onto drone class. ## Performance | Test | Result | |---|---| | NUS DroneAudioSet held-out (48 clips) | 100% detection | | Geronimobasso sanity (50 random clips) | 24/25 drone, 25/25 no-drone | ## Recommended inference For long audio, slide a 1-second window with 0.5s hop, apply a median filter to per-window probabilities, and require N consecutive windows above threshold. See `scripts/standalone_inference.py` in the repo. Trained on 1.0s windows at 16 kHz mono. May fire on rotor-like sounds.

Source context: 679 downloads · 0 likes · Pipeline audio-classification · Repo Rashidbm/samid-drone-detector

Source context: 116 downloads · 0 likes · Pipeline audio-classification · Repo Rashidbm/samid-drone-detector