The rotating machine is one of the common components which is examined under predictive maintenance across different industry sectors. Failure of bearings and gear boxes in such rotating machines are typical problems and their inspection is done using multiple sensors such as vibration, ultrasound, torque and temperature. The existing machine learning (ML) methods for bearing fault detection include traditional ML approaches and advanced deep learning algorithms. Nevertheless, these approaches often fail to account for the domain context and relationships between features, which are essential for building generalized, knowledge oriented, and trustworthy models. This work attempts to incorporate these aspects using a domain contextual and relational graph model (DCRG). It involves a graph convolutional network which is constructed with domain inspired features and their relations based on domain knowledge. The proposed method has been inspected using open-source bearing datasets such as Franche-Comté Électronique Mécanique Thermique et Optique (FEMTO), and Xi'an Jiaotong University (XJTU) bearing dataset. DCRG achieves stronger F1 scores (improvement by 5–10%) than the baseline machine learning approaches.

Time masking has become a de facto augmentation technique for speech and audio tasks, including automatic speech recognition (ASR) and audio classification, most notably as a part of SpecAugment.In this work, we propose SPLICEOUT, a simple modification to time masking which makes it computationally more efficient.SPLICEOUT performs comparably to (and sometimes outperforms) SpecAugment on a wide variety of speech and audio tasks, including ASR for seven different languages using varying amounts of training data, as well as on speech translation, sound and music classification, thus establishing itself as a broadly applicable audio augmentation method.SPLICEOUT also provides additional gains when used in conjunction with other augmentation techniques.Apart from the fully-supervised setting, we also demonstrate that SPLICEOUT can complement unsupervised representation learning with performance gains in the semi-supervised and self-supervised settings.

Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Student Training is a recent SSL paradigm proposed for image classification that is an extension of self-training and teacher-student learning. In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image. We show that, even in the absence of raw image augmentation, the use of simple model and feature perturbations to the input images for the student model are beneficial to SSL training. We also show how a paraphrase generator could be effectively used for label augmentation to improve the quality of pseudo labels and significantly improve performance. Our final results in the limited labeled data setting (1% of the MS-COCO labeled data) outperform previous state-of-the-art approaches by 2.5 on BLEU4 and 11.5 on CIDEr scores.

In this paper, we propose an approach to automatically classify COVID-19 and non-COVID-19 cough samples based on the combination of both feature engineering and deep learning models. In the feature engineering approach, we develop a support vector machine classifier over high dimensional (6373D) space of acoustic features. In the deep learning-based approach, on the other hand, we apply a convolutional neural network trained on the log-mel spectrograms. These two methodologically diverse models are then combined by fusing the probability scores of the models. The proposed system, which ranked 9th on the 2021 Diagnosing COVID-19 using Acoustics (Di- COVA) challenge leaderboard, obtained an area under the receiver operating characteristic curve (AUC) of 0:81 on the blind test data set, which is a 10:9 absolute improvement compared to the baseline. Moreover, we analyze the explainability of the deep learning-based model when detecting COVID-19 from cough signals. Copyright © 2021 ISCA.

Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, l1-norm, average percentage of zeros, etc) and retain only the top ranked filters. Once the low scoring filters are pruned away the remainder of the network is fine tuned and is shown to give performance comparable to the original unpruned network. In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned. Specifically, we show counter-intuitive results wherein by randomly pruning 25-50% filters from deep CNNs we are able to obtain the same performance as obtained by using state of the art pruning methods. We empirically validate our claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. Further, we also evaluate a real world scenario where a CNN trained on all 1000 ImageNet classes needs to be tested on only a small set of classes at test time (say, only animals). We create a new benchmark dataset from ImageNet to evaluate such class specific pruning and show that even here a random pruning strategy gives close to state of the art performance. Lastly, unlike existing approaches which mainly focus on the task of image classification, in this work we also report results on object detection. We show that using a simple random pruning strategy we can achieve significant speed up in object detection (74% improvement in fps) while retaining the same accuracy as that of the original Faster RCNN model.