Purpose Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers improve their models using the proposed by authors. Design/methodology/approach The authors structured our into methods augment raw spectrograms. In paper, describe structure of give brief explanation how every function works. then perform exp...

Data augmentation is widely used for machine learning; however, an effective method to apply data has not been established even though it includes several factors that should be tuned carefully. One such factor sample suitability, which involves selecting samples are suitable augmentation. A typical applies all training disregards may reduce classifier performance. To address this problem, we p...

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage various crops. If change continues, it will likely be very difficult maintain current crop production, and the problem of a shortage expert manpower is also deepening. Fortunately, research on early diagnosis systems based deep learning actively underway so...

Short Answer Grading (SAG) is the task of scoring students’ answers for applications such as examinations or e-learning. Most existing SAG systems predict scores based only on answers, and critical evaluation criteria rubrics are ignored, which plays a crucial role in evaluating real-world situations. In this paper, we propose semi-supervised method to train neural model. We extract keyphrases ...

Data augmentation is a well-known technique used for improving the generalization performance of modern neural networks. After success several traditional random data images (including flipping, translation, or rotation), recent surge interest in implicit techniques occurs to complement techniques. Implicit augments training samples feature space, rather than pixel resulting generation semantic...

Data augmentation (DA) has been extensively studied to facilitate model optimization in many tasks. Prior DA works focus on designing operations themselves, while leaving selecting suitable samples for out of consideration. This might incur visual ambiguities and further induce training biases. In this paper, we propose an effective approach, dubbed SelectAugment, select a deterministic online ...