Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. The automatic detection of mechanical failures can bring numerous benefits for industry 4.0 and for factory automation using artificial intelligence[1].
The challenge (dcase 2020 task 2) is to detect unknown anomalies sounds under the condition that only normal sound samples have were provided as training data. For this task, a baseline system implementation was provided for comparison purposes [1].
About the machine learning model to detect anomalies, two methods involving autoencoder were used: Deep Autoencoder and Convolutional Autoencoder. About the audio features,the log energies derived directly from the filter bank energies (called MFECs) were used.
- Report - Technical report submitted to Dcase 2020 challenge describing the proposed system
- Keras
- matplotlib
- numpy
- PyYAML
- scikit-learn
- librosa
- tensorflow
- tqdm
Clone this repository from Github.
- Development dataset
- Download
dev_data_<Machine_Type>.zipfrom https://zenodo.org/record/3678171.
- Download
- "Additional training dataset", i.e. the evaluation dataset for training
- Download
eval_data_train_<Machine_Type>.zipfrom https://zenodo.org/record/3727685 .
- Download
- "Evaluation dataset", i.e. the evaluation for test
- Download
eval_data_test_<Machine_Type>.zipfrom https://zenodo.org/record/3841772 .
- Download
A simple Dense AE was proposed. The network architecture is shown in the following figure.
- Dense AE - Folder containing the implementation.
The Dense AE system consists of two main scripts:
Dcase.py- This script trains models for each Machine Type in the development dataset and tests the models creating csv files for each Machine ID including the anomaly scores for each test wav file, it also makes the csv files including the AUC and pAUC for each Machine ID
Dcase_Eval.py.py- This script loads models for each Machine Type and tests the models in the evaluation dataset creating csv files for each Machine ID including the anomaly scores for each test wav file.
Make the directory structure as follows:
- ./dcase2020
- /Dcase.py
- /Dcase_Eval.py
- /dev_data
- /ToyCar
- /train (Only normal data for all Machine IDs are included.)
- /normal_id_01_00000000.wav
- ...
- /normal_id_04_00000999.wav
- /test (Normal and anomaly data for all Machine IDs are included.)
- /normal_id_01_00000000.wav
- ...
- /anomaly_id_04_00000264.wav
- /train (Only normal data for all Machine IDs are included.)
- /ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)
- /fan
- /pump
- /slider
- /valve
- /ToyCar
- /eval_data
- /ToyCar
- /train ( "additional training dataset". Only normal data for all Machine IDs are included.)
- /normal_id_05_00000000.wav
- ...
- /normal_id_07_00000999.wav
- /test ("evaluation dataset". Normal and anomaly data for all Machine IDs are included, but there is no label about normal or anomaly.)
- /id_05_00000000.wav
- ...
- /id_07_00000514.wav
- /train ( "additional training dataset". Only normal data for all Machine IDs are included.)
- /ToyConveyor (The other machine types have the same directory structure as ToyCar.)
- /fan
- /pump
- /slider
- /valve
- /ToyCar
Run the training script Dcase.py.
$ python Dcase.py
Dcase.py trains the models and saves the trained models.
Run the test script Dcase_Eval.py.
$ python Dcase_Eval.py
Dcase_Eval.py calculates the anomaly scores for each wav file in the directory dev_data/<Machine_Type>/test/.
The csv files for each Machine ID including the anomaly scores will be stored.
A simple Convolutional AE was proposed. The network architecture is shown in the following figure.
- Convolutional AE - Folder containing the implementation.
The Convolutional AE system consists of three main scripts:
features.py- This script generates and saves the features for each Machine Type by using the directory dev_data/<Machine_Type>/train/ or eval_data/<Machine_Type>/train/.
00_train.py- This script trains models for each Machine Type by using the features extracted with the previous script.
01_test.py- This script makes csv files for each Machine ID including the anomaly scores for each wav file in the directory dev_data/<Machine_Type>/test/ or eval_data/<Machine_Type>/test/.
- The csv files will be stored in the directory result/.
- If the mode is "development", it also makes the csv files including the AUC and pAUC for each Machine ID.
Make the directory structure as follows:
- ./dcase2020
- /ConvAE
- /00_train.py
- /01_test.py
- /common.py
- /keras_model.py
- /config.yaml
- /dev_data
- /ToyCar
- /train (Only normal data for all Machine IDs are included.)
- /normal_id_01_00000000.wav
- ...
- /normal_id_04_00000999.wav
- /test (Normal and anomaly data for all Machine IDs are included.)
- /normal_id_01_00000000.wav
- ...
- /anomaly_id_04_00000264.wav
- /train (Only normal data for all Machine IDs are included.)
- /ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)
- /fan
- /pump
- /slider
- /valve
- /ToyCar
- /eval_data
- /ToyCar
- /train ( "additional training dataset". Only normal data for all Machine IDs are included.)
- /normal_id_05_00000000.wav
- ...
- /normal_id_07_00000999.wav
- /test ("evaluation dataset". Normal and anomaly data for all Machine IDs are included, but there is no label about normal or anomaly.)
- /id_05_00000000.wav
- ...
- /id_07_00000514.wav
- /train ( "additional training dataset". Only normal data for all Machine IDs are included.)
- /ToyConveyor (The other machine types have the same directory structure as ToyCar.)
- /fan
- /pump
- /slider
- /valve
- /ToyCar
- /ConvAE
You can change the parameters for feature extraction and model definition by editing config.yaml.
Run the training script features.py.
Use the option -d for the development dataset or -e for the evaluation dataset.
Use the option --target to select only one machine type (optional).
$ python features.py -d --target "ToyCar"
Run the training script 00_train.py.
Use the option -d for the development dataset or -e for the evaluation dataset.
Use the option --target to select only one machine type (optional).
$ python 00_train.py -d --target "ToyCar"
00_train.py trains the models and saves the trained models in the directory model/.
Run the test script 01_test.py.
Use the option -d for the development dataset or -e for the evaluation dataset.
Use the option --target to select only one machine type (optional).
$ python 01_test.py -d --target "ToyCar"
The options for 01_test.py are the same as those for 00_train.py.
01_test.py calculates the anomaly scores for each wav file in the directory dev_data/<Machine_Type>/test/.
The csv files for each Machine ID including the anomaly scores will be stored in the directory result/.
If the mode is "development", the script also makes the csv files including the AUCs and pAUCs for each Machine ID.
The table below shows the performance results of DCASE 2020 Task 2 for the development dataset in which the best (mean) results are in bold. Best mean for each machine type:
- ToyCar: Dense AE with 80.79% AUC and 71.17% pAUC
- ToyConveyor: Dense AE with 76.43% AUC and 63.79% pAUC
- fan: Dense AR with 72.03% AUC and 53.25% pAUC
- pump: Dense AE with 73.06% AUC and Conv AE with 60.96% pAUC
- slider: Conv AE with 91.77% AUC and 76.20% pAUC
- valve: Conv AE with 78.83% AUC and 53.10% pAUC
[1] Koizumi, Y., Kawaguchi, Y., Imoto, K., Nakamura, T., Nikaido, Y., Tanabe, R., ... & Harada, N. (2020). Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring. arXiv preprint arXiv:2006.05822.