Auto-deploy 2026-04-29 10:19 UTC

README.md

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+---
+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: 2026-04-29 10:19 UTC
+
+## Classes
+
+| Label | Description |
+|---|---|
+| `active_colony` | — |
+| `external_noise` | — |
+| `missing_queen` | — |
+| `queenbee_present` | — |
+| `swarming` | — |
+
+## Model performance
+
+| File | Description | Accuracy | F1 (weighted) |
+|---|---|---|---|
+| `random_forest_model.pkl` | Random Forest | 0.9966 | 0.9966 |
+| `svm_rbf_model.pkl` | SVM (RBF) | 0.9950 | 0.9950 |
+| `xgboost_model.pkl` | XGBoost | 0.9977 | 0.9977 |
+| `gradient_boosting_model.pkl` | Gradient Boosting **best** | 0.9983 | 0.9983 |
+| `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 (171 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("gradient_boosting_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)
+```

bee_cnn_classifier.h5

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best_cnn.h5

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+size 10318104

cnn_label_encoder.pkl

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+size 647

config.json

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+{
+  "sample_rate": 22050,
+  "segment_length_sec": 5.0,
+  "n_mfcc": 40,
+  "hop_length": 512,
+  "n_fft": 2048,
+  "model_type": "bee_audio_classifier",
+  "classes": [
+    "active_colony",
+    "external_noise",
+    "missing_queen",
+    "queenbee_present",
+    "swarming"
+  ],
+  "num_classes": 5,
+  "n_features": 171,
+  "best_model": "gradient_boosting_model.pkl",
+  "uploaded_files": [
+    "random_forest_model.pkl",
+    "svm_rbf_model.pkl",
+    "xgboost_model.pkl",
+    "gradient_boosting_model.pkl",
+    "bee_cnn_classifier.h5",
+    "best_cnn.h5",
+    "label_encoder.pkl",
+    "cnn_label_encoder.pkl"
+  ],
+  "classical_metrics": {
+    "Random Forest": {
+      "accuracy": 0.9966,
+      "f1_weighted": 0.9966,
+      "f1_macro": 0.9957
+    },
+    "XGBoost": {
+      "accuracy": 0.9977,
+      "f1_weighted": 0.9977,
+      "f1_macro": 0.9973
+    },
+    "SVM (RBF)": {
+      "accuracy": 0.995,
+      "f1_weighted": 0.995,
+      "f1_macro": 0.9938
+    },
+    "Gradient Boosting": {
+      "accuracy": 0.9983,
+      "f1_weighted": 0.9983,
+      "f1_macro": 0.9979
+    }
+  },
+  "cnn_metrics": {}
+}

gradient_boosting_model.pkl

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label_encoder.pkl

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random_forest_model.pkl

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svm_rbf_model.pkl

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xgboost_model.pkl

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