This repo is actually taken from "https://github.com/parth2170/DCASE2020-Task2" and the issues related to the library versions are resolved.
This repository can be used to reproduce our submissions for DCASE Challenge 2020 Task 2 - Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring
This report summarizes our submission for Task-2 of the DCASE 2020 Challenge. We propose two different anomalous sound detection systems, one based on features extracted from a modula- tion spectral signal representation and the other based on i-vectors extracted from mel-band features. The first system uses a nearest neighbour graph to construct clusters which capture local variations in the training data. Anomalies are then identified based on their distance from the cluster centroids. The second system uses i-vectors extracted from mel-band spectra for training a Gaussian Mixture Model. Anomalies are then identified using their negative log likelihood. Both these methods show significant improvement over the DCASE Challenge baseline AUC scores, with an average improvement of 6% across all machines. An ensemble of the two systems is shown to further improve the average performance by 11% over the baseline.
Requirements
conda create -n <env-name> python==3.8
conda activate <env-name>
git clone https://github.com/jfsantos/SRMRpy.git
cd SRMRpy/
python setup.py install
pip install -r requirements.txt
- Datasets are available here
- Datasets for all machines can be downloaded and unzipped by running
sh download_dev_data.sh
for development datash download_eval_data.sh
for evaluation data
cd bin/modspec_graph/
python graph_anom_detection.py d
- for running on development data- Modulation Spectrums for each machine-id will be stored in
npy
files insaved/
in the same directory - The results for development data are stored in
modspec_graph_dev_data_results.csv
in the same directory
- Modulation Spectrums for each machine-id will be stored in
python graph_anom_detection.py e
- for running on evaluation data- The results for evaluation data are stored in the submission format in the directory
task2
- The results for evaluation data are stored in the submission format in the directory
- i-Vectors for both development and evaluation have been provided in the zip file -
saved_iVectors/ivector_mfcc_100.zip
- Unzip
ivector_mfcc_100.zip
in the same directory- Code for extracting i-Vectors will be added soon
cd bin/iVectors_gmm/
python gmm.py d
- for running on development data- The results for development data are stored in
iVectors_gmm_dev_data_results.csv
in the same directory
- The results for development data are stored in
python gmm.py e
- for running on evaluation data- The results for evaluation data are stored in the submission format in the directory
task2
- The results for evaluation data are stored in the submission format in the directory