Adaptive Data Augmentation to Achieve Noise Robustness and Overcome Data Deficiency for Deep Learning

Kim, Eunkyeong; Kim, Jinyong; Lee, Hansoo; Kim, Sungshin

doi:10.3390/app11125586

Cited by 12 publications

(4 citation statements)

References 21 publications

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“…In addition, all data augmentation strategies negatively impacted the detection of natural barn sounds except for the Gaussian noise technique which displayed better results. It reflected that Gaussian noise effectively promoted the robustness of the network to the environmental noise, which can be attributed to the fact that it is similar to the actual noise [53]. Beyond the data augmentation strategies that we used in our model, more complicated data augmentation methods were applied directly to audio recordings, such as time-stretching, pitch shifting and mixing multiple audios [27,45,54].…”

Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

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The annual global production of chickens exceeds 25 billion birds, which are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an ‘iceberg indicator’ of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotation, which is very labour-intensive and time-consuming. Thus, a novel convolutional neural network-based model, light-VGG11, was developed to automatically identify chicken distress calls using recordings (3363 distress calls and 1973 natural barn sounds) collected on an intensive farm. The light-VGG11 was modified from VGG11 with significantly fewer parameters (9.3 million versus 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e. precision (94.58%), recall (94.89%), F1-score (94.73%) and accuracy (95.07%), therefore more useful for model deployment in practice. To additionally improve light-VGG11's performance, we investigated the impacts of different data augmentation techniques (i.e. time masking, frequency masking, mixed spectrograms of the same class and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. Our distress call detection demonstration on continuous audio recordings, shows the potential for developing technologies to monitor the output of this call type in large, commercial chicken flocks.

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Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalizations using deep learning models

Mao

Giraudet

Liu

et al. 2022

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…In addition, all data augmentation strategies negatively impacted the detection of natural barn sounds except for the Gaussian noise technique which displayed better results. It reflected that Gaussian noise effectively promoted the robustness of the network to the environmental noise, which can be attributed to the fact that it is similar to the actual noise [52]. Beyond the data augmentation strategies that we used in our model, more complicated data augmentation methods are applied directly to audio recordings, such as time-stretching, pitch shifting, and mixing multiple audios [27,44,53].…”

Section: Discussionmentioning

confidence: 99%

Automated identification of chicken distress vocalisations using deep learning models

Mao

Giraudet

Liu

et al. 2021

Preprint

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The annual global production of chickens exceeds 25 billion birds, and they are often housed in very large groups, numbering thousands. Distress calling triggered by various sources of stress has been suggested as an “iceberg indicator” of chicken welfare. However, to date, the identification of distress calls largely relies on manual annotations, which is very labour-intensive and time-consuming. Thus, a novel light-VGG11 was developed to automatically identify chicken distress calls using recordings (3,363 distress calls and 1,973 natural barn sounds) collected on intensive chicken farms. The light-VGG11 was modified from VGG11 with a significantly smaller size in parameters (9.3 million vs 128 million) and 55.88% faster detection speed while displaying comparable performance, i.e., precision (94.58%), recall (94.89%), F1-score (94.73%), and accuracy (95.07%), therefore more useful for model deployment in practice. To further improve the light-VGG11’s performance, we investigated the impacts of different data augmentation techniques (i.e., time masking, frequency masking, mixed spectrograms of the same class, and Gaussian noise) and found that they could improve distress calls detection by up to 1.52%. In terms of precision livestock farming, our research opens new opportunities for developing technologies used to monitor the output of distress calls in large, commercial chicken flocks.

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“…Generally, data augmentation is the solution usually used to resolve this problem. For this purpose, data augmentation is used to increase the amount of training data and ensure robustness against noise attacks (Kim et al 2021 ). In this dataset, five hundred images have been labeled as C-shaped, and five hundred images have been labeled as S-shaped.…”

Section: Methodsmentioning

confidence: 99%

Classification of spinal curvature types using radiography images: deep learning versus classical methods

et al. 2023

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Scoliosis is a spinal abnormality that has two types of curves (C-shaped or S-shaped). The vertebrae of the spine reach an equilibrium at different times, which makes it challenging to detect the type of curves. In addition, it may be challenging to detect curvatures due to observer bias and image quality. This paper aims to evaluate spinal deformity by automatically classifying the type of spine curvature. Automatic spinal curvature classification is performed using SVM and KNN algorithms, and pre-trained Xception and MobileNetV2 networks with SVM as the final activation function to avoid vanishing gradient. Different feature extraction methods should be used to investigate the SVM and KNN machine learning methods in detecting the curvature type. Features are extracted through the representation of radiographic images. These representations are of two groups: (i) Low-level image representation techniques such as texture features and (ii) local patch-based representations such as Bag of Words (BoW). Such features are utilized by various algorithms for classification by SVM and KNN. The feature extraction process is automated in pre-trained deep networks. In this study, 1000 anterior–posterior (AP) radiographic images of the spine were collected as a private dataset from Shafa Hospital, Tehran, Iran. The transfer learning was used due to the relatively small private dataset of anterior–posterior radiology images of the spine. Based on the results of these experiments, pre-trained deep networks were found to be approximately 10% more accurate than classical methods in classifying whether the spinal curvature is C-shaped or S-shaped. As a result of automatic feature extraction, it has been found that the pre-trained Xception and mobilenetV2 networks with SVM as the final activation function for controlling the vanishing gradient perform better than the classical machine learning methods of classification of spinal curvature types.

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Adaptive Data Augmentation to Achieve Noise Robustness and Overcome Data Deficiency for Deep Learning

Cited by 12 publications

References 21 publications

Automated identification of chicken distress vocalizations using deep learning models

Automated identification of chicken distress vocalizations using deep learning models

Automated identification of chicken distress vocalisations using deep learning models

Classification of spinal curvature types using radiography images: deep learning versus classical methods

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