论文标题
在机器状态下无监督异常检测的深度密集和卷积自动编码器
Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds
论文作者
论文摘要
该技术报告描述了为Dcase 2020挑战的任务2开发的两种方法。挑战涉及一个无监督的学习来检测异常声音，因此在训练过程中只有普通的机器工作状态样本。这两种方法涉及深度自动编码器，基于使用Melspectrogram处理的声音功能的密集和卷积体系结构。使用挑战的六个机器类型数据集进行了实验。总体而言，拟议的密集和卷积AE实现了竞争结果，表现优于基线挑战方法。
This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features. Experiments were held, using the six machine type datasets of the challenge. Overall, competitive results were achieved by the proposed dense and convolutional AE, outperforming the baseline challenge method.
