File size: 3,726 Bytes
c21f2aa 23055a7 c21f2aa 24ef7da c21f2aa 24ef7da c21f2aa 24ef7da c21f2aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | ---
language: en
tags:
- audio
- audio-classification
- bee
- hive-monitoring
- beekeeping
library_name: sklearn
license: mit
metrics:
- accuracy
- f1
---
# Bee Audio Classifier
5-class audio classifier for bee colony health monitoring.
Trained on segmented hive recordings using MFCC-based feature extraction.
> Last updated: 2026-05-19 15:54 UTC
## Classes
| Label | Description |
|---|---|
| (classes not found) | — |
## Model performance
| File | Description | Accuracy | F1 (weighted) |
|---|---|---|---|
| `bee_cnn_classifier.h5` | CNN (Mel Spectrogram) | — | — |
| `best_cnn.h5` | CNN checkpoint | — | — |
`label_encoder.pkl` is required by all classical ML models.
`cnn_label_encoder.pkl` is required by the CNN models.
## Feature extraction (0 features per 5-second segment)
- 40 MFCCs × (mean + std) = 80
- 40 delta-MFCCs × mean = 40
- 12 Chroma × (mean + std) = 24
- Mel spectrogram stats (mean, std, max, min) = 4
- Spectral centroid (mean + std) = 2
- Spectral bandwidth (mean + std) = 2
- Spectral rolloff (mean + std) = 2
- Spectral contrast × 7 × mean = 7
- Zero crossing rate (mean + std) = 2
- RMS energy (mean + std) = 2
- Tonnetz × 6 × mean = 6
## Quick Python usage
```python
import joblib
import librosa
import numpy as np
model = joblib.load("random_forest_model.pkl")
le = joblib.load("label_encoder.pkl")
def extract_features(y, sr, n_mfcc=40,
hop_length=512, n_fft=2048):
feats = {}
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length)
for i in range(n_mfcc):
feats[f"mfcc_{i}_mean"] = np.mean(mfcc[i])
feats[f"mfcc_{i}_std"] = np.std(mfcc[i])
delta = librosa.feature.delta(mfcc)
for i in range(n_mfcc):
feats[f"mfcc_delta_{i}_mean"] = np.mean(delta[i])
chroma = librosa.feature.chroma_stft(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length)
for i in range(12):
feats[f"chroma_{i}_mean"] = np.mean(chroma[i])
feats[f"chroma_{i}_std"] = np.std(chroma[i])
mel = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length)
mel_db = librosa.power_to_db(mel, ref=np.max)
feats["mel_mean"] = np.mean(mel_db); feats["mel_std"] = np.std(mel_db)
feats["mel_max"] = np.max(mel_db); feats["mel_min"] = np.min(mel_db)
sc = librosa.feature.spectral_centroid(y=y, sr=sr, hop_length=hop_length)
feats["spectral_centroid_mean"] = np.mean(sc); feats["spectral_centroid_std"] = np.std(sc)
sb = librosa.feature.spectral_bandwidth(y=y, sr=sr, hop_length=hop_length)
feats["spectral_bandwidth_mean"] = np.mean(sb); feats["spectral_bandwidth_std"] = np.std(sb)
sr_f = librosa.feature.spectral_rolloff(y=y, sr=sr, hop_length=hop_length)
feats["spectral_rolloff_mean"] = np.mean(sr_f); feats["spectral_rolloff_std"] = np.std(sr_f)
contrast = librosa.feature.spectral_contrast(y=y, sr=sr, hop_length=hop_length)
for i in range(contrast.shape[0]):
feats[f"spectral_contrast_{i}_mean"] = np.mean(contrast[i])
zcr = librosa.feature.zero_crossing_rate(y, hop_length=hop_length)
feats["zcr_mean"] = np.mean(zcr); feats["zcr_std"] = np.std(zcr)
rms = librosa.feature.rms(y=y, hop_length=hop_length)
feats["rms_mean"] = np.mean(rms); feats["rms_std"] = np.std(rms)
harmonic = librosa.effects.harmonic(y)
tonnetz = librosa.feature.tonnetz(y=harmonic, sr=sr)
for i in range(6):
feats[f"tonnetz_{i}_mean"] = np.mean(tonnetz[i])
return np.array(list(feats.values())).reshape(1, -1)
y, sr = librosa.load("hive_recording.wav", sr=22050)
seg = y[:int(5.0 * sr)]
feat = extract_features(seg, sr)
pred = le.classes_[model.predict(feat)[0]]
print(pred)
```
|