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Peranut Nimitsurachat and Peter Washington
Emotion recognition models using audio input data can enable the development of interactive systems with applications in mental healthcare, marketing, gaming, and social media analysis. While the field of affective computing using audio data is rich, a m...
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Yusuf Brima, Ulf Krumnack, Simone Pika and Gunther Heidemann
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are ...
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Xiaodong Cui, Zhuofan He, Yangtao Xue, Keke Tang, Peican Zhu and Jing Han
Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot le...
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Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes and Tobias Meisen
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is ther...
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Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri...
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Zhuo Wang, Haojie Chen, Hongde Qin and Qin Chen
In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color dis...
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Xuefeng Zhang, Youngsung Kim, Young-Chul Chung, Sangcheol Yoon, Sang-Yong Rhee and Yong Soo Kim
Large-scale datasets, which have sufficient and identical quantities of data in each class, are the main factor in the success of deep-learning-based classification models for vision tasks. A shortage of sufficient data and interclass imbalanced data dis...
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Tanvir Islam and Peter Washington
Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interv...
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Yejin Lee, Suho Lee and Sangheum Hwang
Fine-grained image recognition aims to classify fine subcategories belonging to the same parent category, such as vehicle model or bird species classification. This is an inherently challenging task because a classifier must capture subtle interclass dif...
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Esmaeil Zahedi, Mohamad Saraee, Fatemeh Sadat Masoumi and Mohsen Yazdinejad
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative and C...
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