| --- |
| 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: 2026-05-16 21:27 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) |
| ``` |
|
|
