--- license: apache-2.0 language: - en tags: - audio-classification - pytorch - se-resnet - machine-fault-detection - predictive-maintenance - mel-spectrogram pipeline_tag: audio-classification --- # Filter-Tank — Machine Fault Recognition A deep learning system that listens to factory machine audio recordings and classifies them into 6 categories across 3 machine types, each in either a normal or abnormal state. Built from scratch using SE-ResNet on log-mel spectrograms. --- ## Overview Filter-Tank is a complete machine learning pipeline for predictive maintenance. Given a raw `.wav` audio recording of a factory machine, the system automatically detects whether the machine is operating normally or has developed a fault — and identifies which machine type it belongs to. The model is a custom SE-ResNet (Squeeze-and-Excitation ResNet) trained entirely from scratch with no pretrained weights, designed specifically for 1-channel log-mel spectrogram input. --- ## Classes | Label | Description | |-------|----------------------| | 0 | Machine 1 — Normal | | 1 | Machine 1 — Abnormal | | 2 | Machine 2 — Normal | | 3 | Machine 2 — Abnormal | | 4 | Machine 3 — Normal | | 5 | Machine 3 — Abnormal | --- ## Preprocessing Pipeline Every audio file passes through a multi-stage preprocessing pipeline before reaching the model. All steps run on CPU and are excluded from the inference timer (only processing + prediction time is measured). ### 1. Resampling All audio is resampled to a fixed sample rate of 16,000 Hz to ensure consistency across recordings made with different microphones or recording equipment. ### 2. Noise Reduction Non-stationary background noise is removed using the `noisereduce` library with full noise reduction strength (prop_decrease=1.0). This handles real-world factory environments where background noise varies significantly between recordings. ### 3. Silence Trimming Leading and trailing silence is removed using librosa's trim function (top_db=20). This ensures the model focuses only on the actual machine sound rather than quiet gaps at the start or end of a recording. ### 4. Fixed-Length Normalization All recordings are normalized to exactly 11 seconds. Files longer than 11 seconds are truncated from the end. Files shorter than 11 seconds are zero-padded at the end. This gives the model a consistent input size regardless of the original recording length. ### 5. Log-Mel Spectrogram The waveform is converted into a 2D log-mel spectrogram using the following settings: - Mel bands: 128 - FFT window size: 1024 - Hop length: 512 - Power: 2.0 (power spectrogram) - Amplitude converted to dB scale (top_db=80) This transforms the raw audio signal into a visual time-frequency representation that the convolutional model can process effectively. ### 6. CMVN Normalization Cepstral Mean and Variance Normalization is applied per sample — each spectrogram is normalized to have zero mean and unit variance along the time axis. This handles volume variations and differences in microphone sensitivity across recordings. --- ## Model Architecture ### SE-ResNet (Squeeze-and-Excitation ResNet) The model follows a standard ResNet structure enhanced with Squeeze-and-Excitation (SE) attention blocks at every residual stage. **Stem:** A 7x7 convolution (stride 2) followed by batch normalization, ReLU, and max pooling reduces the input resolution before the residual stages. **4 Residual Stages:** - Stage 1: 3 SE-Residual blocks, 64 channels - Stage 2: 4 SE-Residual blocks, 128 channels (stride 2) - Stage 3: 6 SE-Residual blocks, 256 channels (stride 2) - Stage 4: 3 SE-Residual blocks, 512 channels (stride 2) **SE Attention Block:** Each residual block includes a Squeeze-and-Excitation module that performs global average pooling, passes the result through two fully-connected layers with a bottleneck (reduction=16), and produces per-channel attention weights via sigmoid. This lets the model focus on the most informative frequency channels for each input. **Head:** Global Average Pooling → Dropout (0.3) → Fully Connected layer → 6-class output. **Weight Initialization:** - Conv layers: Kaiming Normal (fan_out, relu) - BatchNorm: weight=1, bias=0 - Linear layers: Xavier Uniform **Total Parameters:** ~11 million --- ## Training Details ### Dataset Split The dataset is divided using stratified splitting to ensure balanced class representation across all splits: - Training set: 80% - Validation set: 10% - Test set: 10% Stratification is done by machine type and condition combined, so each split has proportional representation of all 6 classes. ### Class Imbalance Handling A WeightedRandomSampler is used during training to oversample underrepresented classes, ensuring the model sees a balanced distribution of all 6 classes per epoch regardless of the original dataset distribution. ### Data Augmentation Two augmentation strategies are applied during training: **SpecAugment (online, per batch):** Applied directly to the spectrogram tensors during training. Two frequency masks (freq_mask_param=20) and two time masks (time_mask_param=40) are applied randomly, forcing the model to be robust to missing frequency bands and time segments. **Mixup (online, per batch):** Pairs of training samples are blended together with a random interpolation weight drawn from a Beta distribution (alpha=0.4). Both the input spectrograms and their labels are mixed, which acts as a strong regularizer and improves generalization. ### Loss Function Cross-Entropy Loss with label smoothing (0.1). Label smoothing prevents overconfident predictions and improves calibration. ### Optimizer & Scheduler - Optimizer: AdamW (weight decay=1e-4) - Scheduler: OneCycleLR with cosine annealing - Max LR: 3e-3 - Warmup: 10% of total steps - Gradient clipping: max norm = 1.0 ### Mixed Precision Training All forward and backward passes use torch.amp autocast with float16 precision, reducing memory usage and speeding up training on GPU. ### Multi-GPU Support The model supports DataParallel training across multiple GPUs automatically. The best model state is always saved from the unwrapped module to ensure compatibility during single-GPU inference. ### Early Stopping Training stops automatically if validation accuracy does not improve for 12 consecutive epochs (patience=12). The best model checkpoint is saved based on validation accuracy. | Setting | Value | |-----------------|------------------------------| | Optimizer | AdamW | | Max LR | 3e-3 | | LR Schedule | OneCycleLR (cosine annealing)| | Weight Decay | 1e-4 | | Max Epochs | 60 | | Early Stopping | Patience = 12 | | Batch Size | 64 | | Label Smoothing | 0.1 | | Mixup Alpha | 0.4 | | Mixed Precision | float16 (AMP) | | Dropout | 0.3 | --- ## Inference During inference, audio files are processed strictly one-by-one in naturally sorted order (1.wav, 2.wav, ...). The preprocessing pipeline runs on each file individually, and only the processing + prediction time is measured (I/O reading is excluded from the timer). Two output files are produced: - `results.txt` — one predicted class label (0–5) per line - `time.txt` — processing time per file in seconds (rounded to 3 decimal places) --- ## Requirements - Python 3.8+ - PyTorch - torchaudio - librosa - noisereduce - numpy - soundfile - scikit-learn --- ## Limitations - Trained only on 3 specific machine types; may not generalize to unseen machine types out of the box - Performance may degrade with extremely noisy environments beyond the training distribution - Fixed 11-second input window; very short recordings are zero-padded which may affect accuracy --- ## Team Cairo University — Faculty of Engineering Computer Engineering Department Pattern Recognition and Neural Networks — Spring 2026