Datasets:
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audio audio | label class label |
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0bearing_fault | |
0bearing_fault | |
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0bearing_fault | |
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0bearing_fault | |
0bearing_fault | |
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0bearing_fault | |
0bearing_fault | |
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0bearing_fault | |
0bearing_fault | |
0bearing_fault | |
0bearing_fault | |
1belt_slip | |
1belt_slip | |
1belt_slip | |
1belt_slip | |
1belt_slip | |
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2normal |
Machine Sound Doctor Dataset
Labeled audio clips of running machinery, used to train the classifier behind Machine Sound Doctor — a phone-based predictive maintenance tool: call in, hold your phone near a running machine, get an SMS back diagnosing the sound.
Classes
| Class | Clips | Description |
|---|---|---|
normal |
33 | Machine running normally |
bearing_fault |
33 | Bearing fault sound signature |
belt_slip |
33 | Belt slip sound signature |
99 clips total. Most are 16-bit PCM mono WAV at 16 kHz; a small number are
synthetic augmentations (filenames prefixed synth_).
Layout
normal/
bearing_fault/
belt_slip/
Each class is a folder of .wav clips.
Use
pip install huggingface_hub
hf download hamezksm/machine_noise_dataset --repo-type dataset --local-dir ./dataset
or with git:
git clone https://huggingface.co/datasets/hamezksm/machine_noise_dataset dataset
See the Machine Sound Doctor repo
for the training pipeline (src/train.py) that consumes this data.
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