We lifted 2 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| YumaKoizumi/ToyADMOS-dataset | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| wavread | Ran | YumaKoizumi/ToyADMOS-dataset/C01_create_small_INT_dataset/make_dataset_for_car_and_conveyor.py pointer only (licence: NOASSERTION) · get_code("a63dce463fde9c6c") |
| load_and_cut_noise | Not yet run | YumaKoizumi/ToyADMOS-dataset/C01_create_small_INT_dataset/make_dataset_for_car_and_conveyor.py pointer only (licence: NOASSERTION) · get_code("a15cd0da79ddb2e6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anomalous sound data are difficult to collect. To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them. The released dataset consists of three sub-datasets for machine-condition inspection, fault diagnosis of machines with geometrically fixed tasks, and fault diagnosis of machines with moving tasks. Each sub-dataset includes over 180 hours of normal machine-operating sounds and over 4,000 samples of anomalous sounds collected with four microphones at a 48-kHz sampling rate. The dataset is freely available for download at https://github.com/YumaKoizumi/ToyADMOS-dataset
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.03299")
get_code_for_paper("1908.03299")
have("1908.03299")
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