Detecting machine malfunctions at an early stage is crucial for reducing
interruptions in operational processes within industrial settings. Recently,
the deep learning approach has started to be preferred for the detection of
failures in machines. Deep learning provides an effective solution in fault
detection processes thanks to automatic feature extraction. In this study, a
deep learning-based system was designed to analyze the sound signals produced
by industrial machines. Acoustic sound signals were converted into Mel
spectrograms. For the purpose of classifying spectrogram images, the
DenseNet-169 model, a deep learning architecture recognized for its
effectiveness in image classification tasks, was used. The model was trained
using the transfer learning method on the MIMII dataset including sounds from
four types of industrial machines. The results showed that the proposed method
reached an accuracy rate varying between 97.17% and 99.87% at different Sound
Noise Rate levels.

设计了基于深度学习的系统来分析工业机器产生的声音信号，将声音信号转换为 Mel 频谱图，并使用 DenseNet-169 模型对谱图图像进行分类，结果表明该方法在不同的声音噪声比水平下实验得到了 97.17% 到 99.87% 的准确率。

工业机器故障诊断的深度神经网络声音信号分析

Acoustic Signal Analysis with Deep Neural Network for Detecting Fault  Diagnosis in Industrial Machines

Language Identification (LID) systems are used to classify the spoken
language from a given audio sample and are typically the first step for many
spoken language processing tasks, such as Automatic Speech Recognition (ASR)
systems. Without automatic language detection, speech utterances cannot be
parsed correctly and grammar rules cannot be applied, causing subsequent speech
recognition steps to fail. We propose a LID system that solves the problem in
the image domain, rather than the audio domain. We use a hybrid Convolutional
Recurrent Neural Network (CRNN) that operates on spectrogram images of the
provided audio snippets. In extensive experiments we show, that our model is
applicable to a range of noisy scenarios and can easily be extended to
previously unknown languages, while maintaining its classification accuracy. We
release our code and a large scale training set for LID systems to the
community.

该论文提出了基于图像领域解决 LID 问题的模型，使用了混合的卷积循环神经网络技术对提供的音频片段的频谱图像进行分析，并在广泛的实验中证明了模型的应用性，能够轻松地扩展到以前未知的语言，同时保持其分类准确性。

使用深度卷积循环神经网络进行语言识别

Language Identification Using Deep Convolutional Recurrent Neural  Networks

The exquisite sensitivity of the advanced LIGO detectors has enabled the
detection of multiple gravitational wave signals. The sophisticated design of
these detectors mitigates the effect of most types of noise. However, advanced
LIGO data streams are contaminated by numerous artifacts known as glitches:
non-Gaussian noise transients with complex morphologies. Given their high rate
of occurrence, glitches can lead to false coincident detections, obscure and
even mimic gravitational wave signals. Therefore, successfully characterizing
and removing glitches from advanced LIGO data is of utmost importance. Here, we
present the first application of Deep Transfer Learning for glitch
classification, showing that knowledge from deep learning algorithms trained
for real-world object recognition can be transferred for classifying glitches
in time-series based on their spectrogram images. Using the Gravity Spy
dataset, containing hand-labeled, multi-duration spectrograms obtained from
real LIGO data, we demonstrate that this method enables optimal use of very
deep convolutional neural networks for classification given small training
datasets, significantly reduces the time for training the networks, and
achieves state-of-the-art accuracy above 98.8%, with perfect precision-recall
on 8 out of 22 classes. Furthermore, new types of glitches can be classified
accurately given few labeled examples with this technique. Once trained via
transfer learning, we show that the convolutional neural networks can be
truncated and used as excellent feature extractors for unsupervised clustering
methods to identify new classes based on their morphology, without any labeled
examples. Therefore, this provides a new framework for dynamic glitch
classification for gravitational wave detectors, which are expected to
encounter new types of noise as they undergo gradual improvements to attain
design sensitivity.

该研究应用了深度迁移学习技术，利用卷积神经网络根据光谱图像准确地分类毛刺现象，对于长时间运作的先进激光干涉重力波探测器，此方法提供了一种动态分类毛刺现象的新框架。