mandipgoswami/AnomalyMachine-50K

Dataset Summary

AnomalyMachine-50K is a fully synthetic industrial machine sound anomaly detection dataset designed for research on acoustic monitoring, predictive maintenance, and sound event detection.
The dataset contains 50,000 monaural audio clips, each 10 seconds long at 22,050 Hz, covering six industrial machine types, multiple operating conditions, and diverse anomaly types under different signal-to-noise ratios.

The dataset is generated entirely via signal-processing based synthesis (no neural audio models), ensuring that it is lightweight to regenerate, deterministic under a fixed seed, and free from copyright or privacy issues.

Supported Tasks and Leaderboards

• audio-classification: classify clips as normal vs anomalous.
• sound-event-detection: detect and characterize anomaly subtypes at the clip level.

No official leaderboard is provided, but the dataset is intended as a strong synthetic counterpart to real-world benchmarks such as DCASE 2020 Task 2.

Dataset Structure

• Number of clips: 50,000
• Clip duration: 10 seconds
• Sample rate: 22,050 Hz
• Channels: mono

Machine types:

• fan
• pump
• compressor
• conveyor_belt
• electric_motor
• valve

Operating conditions:

• idle
• normal_load
• high_load

Labels:

• normal
• anomalous

Anomaly subtypes:

• bearing_fault (applies to fan, pump, compressor, electric_motor)
• imbalance (applies to fan, compressor, electric_motor)
• cavitation (applies to pump, valve)
• overheating (applies to compressor, electric_motor, pump)
• obstruction (applies to conveyor_belt, fan, valve)

SNR levels (background factory-floor ambience):

• clean (no added noise)
• low_noise (≈20 dB SNR)
• medium_noise (≈10 dB SNR)
• high_noise (≈5 dB SNR)

Splits (stratified by machine_type and label):

• train: 70%
• validation: 15%
• test: 15%

Data Fields

Each split is a datasets.Dataset with the following features:

• audio (Audio, 22050 Hz): waveform and metadata loaded from on-disk WAV files.
• file_path (string): relative path to the underlying WAV file.
• machine_type (string): one of the six machine types.
• operating_condition (string): idle, normal_load, or high_load.
• label (string): normal or anomalous.
• anomaly_subtype (string): one of the anomaly subtypes above; none for normal clips.
• snr_level (string): clean, low_noise, medium_noise, or high_noise.
• duration_seconds (float32): clip duration in seconds (nominally 10.0).
• sample_rate (int32): sample rate in Hz (22,050).
• split (string): train, val, or test.

Example Row

{
    "audio": {
        "array": <np.ndarray shape=(220500,)>,
        "sampling_rate": 22050
    },
    "file_path": "audio/fan_normal_load_anomalous_bearing_fault_1234.wav",
    "machine_type": "fan",
    "operating_condition": "normal_load",
    "label": "anomalous",
    "anomaly_subtype": "bearing_fault",
    "snr_level": "medium_noise",
    "duration_seconds": 10.0,
    "sample_rate": 22050,
    "split": "train",
}

Generation Methodology

The entire dataset is generated using deterministic signal processing techniques implemented in Python (NumPy, SciPy, and related libraries). No neural audio models are used.

1. Base machine sound synthesis

For each machine type, a dedicated synthesis model is used:

• Fan: broadband noise plus rotating blade harmonics with fundamental between 50–200 Hz.
• Pump: low-frequency rumble (20–80 Hz) with rhythmic pressure pulses and fluid noise.
• Compressor: cyclic compression envelope on top of a 60 Hz motor hum and its harmonics.
• Conveyor belt: rhythmic tapping events combined with frictional broadband noise.
• Electric motor: tonal fundamental derived from 1200–3600 RPM with harmonics and brush noise.
• Valve: turbulent broadband flow noise with intermittent actuation clicks.

Each clip is synthesized at 22,050 Hz for 10 seconds and normalized to a target RMS while preventing clipping. All machines are modeled in mono for simplicity and reproducibility.

2. Operating condition modifiers

Operating conditions modulate the base synthesis:

• idle: reduced amplitude, fewer or weaker harmonics.
• normal_load: baseline signal model.
• high_load: increased amplitude, additional harmonic distortion, and slight pitch or envelope changes.

These modifiers are applied deterministically on top of the base machine model.

3. Anomaly injection

After generating the base clip, anomaly-specific transformations are applied only to clips labeled anomalous:

• Bearing fault: periodic impulsive spikes at a low fault frequency, created as a smoothed impulse train added to the base signal.
• Imbalance: low-frequency sinusoidal amplitude modulation of the waveform.
• Cavitation: short high-energy noise bursts (50–200 ms) at random times within the clip.
• Overheating: gradually increasing high-frequency noise floor, implemented via a ramped high-pass filtered noise process.
• Obstruction: intermittent amplitude drops combined with slight resonance or frequency warping events.

Anomaly applicability follows the mapping described above; invalid combinations are never generated.

4. Background noise and SNR control

Factory-floor ambience is synthesized as:

• Approximate pink noise (1/f spectrum), generated from white noise in the frequency domain.
• A 60 Hz hum plus a 120 Hz harmonic.

For each clip, the noise is mixed at a specified SNR level using the power-based definition: [ \mathrm{SNR_{dB}} = 10 \log_{10} \left( \frac{P_\text{signal}}{P_\text{noise}} \right) ]

Signal and noise RMS levels are computed, and the noise amplitude is scaled accordingly to achieve the target SNR. The clean SNR level skips noise addition entirely.

5. Splitting and metadata

Metadata for each clip is stored in a CSV file and includes:

• file_path, machine_type, operating_condition, label,
• anomaly_subtype, snr_level, duration_seconds, sample_rate, and split.

Train/validation/test splits are assigned in a stratified way over (machine_type, label) combinations with configurable ratios (0.7/0.15/0.15 by default).

Usage

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/AnomalyMachine-50K")

train_ds = dataset["train"]
example = train_ds[0]

audio = example["audio"]["array"]           # numpy array
sr = example["audio"]["sampling_rate"]      # 22050
label = example["label"]                    # "normal" or "anomalous"
machine = example["machine_type"]
anomaly = example["anomaly_subtype"]

print(machine, label, anomaly, sr, audio.shape)

You can easily plug this dataset into PyTorch or other frameworks:

import torch
from torch.utils.data import DataLoader

ds = dataset["train"]

def collate_fn(batch):
    waveforms = [torch.tensor(x["audio"]["array"]) for x in batch]
    labels = [1 if x["label"] == "anomalous" else 0 for x in batch]
    waveforms = torch.stack(waveforms)
    labels = torch.tensor(labels, dtype=torch.long)
    return waveforms, labels

loader = DataLoader(ds, batch_size=32, shuffle=True, collate_fn=collate_fn)

for waveforms, labels in loader:
    # Training loop here
    pass

Benchmark Comparison

Dataset	Type	#Clips	Machines	Anomaly Types	Real/Synthetic	License
DCASE 2020 Task 2	Acoustic	~7k	6	Several	Real	Various
AnomalyMachine-50K	Acoustic	50k	6	5	Synthetic	CC-BY-4.0

AnomalyMachine-50K is significantly larger and fully synthetic, making it well-suited for controlled experiments, ablation studies, and pretraining, while DCASE 2020 Task 2 provides real-world complexity and noise characteristics.

Citation

If you use this dataset in your research, please cite it as:

@dataset{anomalymachine50k_2026,
  title        = {AnomalyMachine-50K: Synthetic Industrial Machine Sound Anomaly Dataset},
  author       = {Anonymous},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/AnomalyMachine-50K}}
}

Known Limitations

• The dataset is fully synthetic; real industrial soundscapes can exhibit more complex reverberation, coupling between machines, and non-stationary background environments.
• Anomaly patterns are defined by explicit signal processing rules and may not capture all nuances of real hardware faults.
• Only single-machine audio is modeled per clip; multi-machine interference and spatial effects are not included.
• The mapping between physical parameters (e.g., RPM, load) and generated audio is simplified and not tied to any specific hardware make or model.

Ethical Considerations

This dataset does not contain speech or personally identifiable information. It is intended for research and development of anomaly detection and predictive maintenance systems. When deploying models trained on this dataset in real industrial environments, practitioners should validate performance carefully and account for domain shift.

How to Regenerate the Dataset

The original generation pipeline is implemented using:

• generate_sounds.py: base machine sound synthesis and metadata creation.
• add_anomalies.py: anomaly-specific signal transformations.
• add_noise.py: factory-floor noise generation and SNR mixing.
• validate_dataset.py: structural and statistical integrity checks.
• upload_to_hf.py: conversion to datasets format and push to the Hugging Face Hub.

All parameters (number of clips, machine types, anomaly types, SNR levels, and split ratios) are controlled via a single YAML configuration file so that the dataset can be regenerated at any scale.

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