3 research outputs found
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
In this paper, we propose a deep learning based model for Acoustic Anomaly
Detection of Machines, the task for detecting abnormal machines by analysing
the machine sound. By conducting extensive experiments, we indicate that
multiple techniques of pseudo audios, audio segment, data augmentation,
Mahalanobis distance, and narrow frequency bands, which mainly focus on feature
engineering, are effective to enhance the system performance. Among the
evaluating techniques, the narrow frequency bands presents a significant
impact. Indeed, our proposed model, which focuses on the narrow frequency
bands, outperforms the DCASE baseline on the benchmark dataset of DCASE 2022
Task 2 Development set. The important role of the narrow frequency bands
indicated in this paper inspires the research community on the task of Acoustic
Anomaly Detection of Machines to further investigate and propose novel network
architectures focusing on the frequency bands
ADBench: Anomaly Detection Benchmark
Given a long list of anomaly detection algorithms developed in the last few
decades, how do they perform with regard to (i) varying levels of supervision,
(ii) different types of anomalies, and (iii) noisy and corrupted data? In this
work, we answer these key questions by conducting (to our best knowledge) the
most comprehensive anomaly detection benchmark with 30 algorithms on 57
benchmark datasets, named ADBench. Our extensive experiments (98,436 in total)
identify meaningful insights into the role of supervision and anomaly types,
and unlock future directions for researchers in algorithm selection and design.
With ADBench, researchers can easily conduct comprehensive and fair evaluations
for newly proposed methods on the datasets (including our contributed ones from
natural language and computer vision domains) against the existing baselines.
To foster accessibility and reproducibility, we fully open-source ADBench and
the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed
alphabetically. Code available at https://github.com/Minqi824/ADBenc