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1,283 results on '"WU Bin"'

11. Class-Aware Self-Distillation for Remote Sensing Image Scene Classification.

Author

Wu, Bin, Hao, Siyuan, and Wang, Wei

Abstract

Currently, convolutional neural networks (CNNs) and vision transformers (ViTs) are widely adopted as the predominant neural network architectures for remote sensing image scene classification. Although CNNs have lower computational complexity, ViTs have a higher performance ceiling, making both suitable as backbone networks for remote sensing scene classification tasks. However, remote sensing imagery has high intraclass variation and interclass similarity, which poses a challenge for existing methods. To address this issue, we propose the class-aware self-distillation (CASD) framework. This framework uses an end-to-end distillation mechanism to mine class-aware knowledge, effectively reducing the impact of significant intraclass variation and interclass similarity in remote sensing imagery. Specifically, our approach involves constructing pairs of images: similar pairs consisting of images belonging to the same class, and dissimilar pairs consisting of images from different classes. We then apply a distillation loss that we designed, which distills the corresponding probability distributions to ensure that the distributions of similar pairs become more consistent, and those of dissimilar pairs become more distinct. In addition, the enforced learnable $\alpha$ added to the distillation loss further amplifies the network's ability to comprehend class-aware knowledge. The experiment section demonstrates that our method CASD outperforms other methods on four publicly available datasets. And the ablation experiments demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Tackling Perception Bias in Unsupervised Phoneme Discovery Using DPGMM-RNN Hybrid Model and Functional Load

Author

Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, Nakamura, Satoshi, Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, and Nakamura, Satoshi

Abstract

The human perception of phonemes is biased against speech sounds. The lack of correspondence between perceputal phonemes and acoustic signals forms a big challenge in designing unsupervised algorithms to distinguish phonemes from sound. We propose the DPGMM-RNN hybrid model that improves phoneme categorization by relieving the fragmentation problem. We also merge segments with low functional load, which is the work done by segment contrasts to differentiate between utterances, just like humans who convert unambiguous segments into phonemes as units for immediate perception. Our results show that the DPGMM-RNN hybrid model relieves the fragmentation problem and improves phoneme discriminability. The minimal functional load merge compresses a segment system, preserves information and keeps phoneme discriminability.

Published
2023

17. Modeling Unsupervised Empirical Adaptation by DPGMM and DPGMM-RNN Hybrid Model to Extract Perceptual Features for Low-Resource ASR

Author

Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, Nakamura, Satoshi, Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, and Nakamura, Satoshi

Abstract

Speech feature extraction is critical for ASR systems. Such successful features as MFCC and PLP use filterbank techniques to model log-scaled speech perception but fail to model the adaptation of human speech perception by hearing experiences. Infant perception that is adapted by hearing speech without text may cause permanent brain state modifications (engrams) that serve as a physical fundamental basis for lifetime speech perception formation. This realization motivates us to propose to model such an unsupervised adaptation process, where adaptation denotes perception that is affected or changed by the history of experiences, with the Dirichlet Process Gaussian Mixture Model (DPGMM) and the DPGMM-RNN hybrid model to extract perceptual features to improve ASR. Our proposed features extend MFCC features with posteriorgrams extracted from the DPGMM algorithm or the DPGMM-RNN hybrid model. Our analysis shows that the DPGMM and DPGMM-RNN model perplexities agree with infant auditory perplexity to support that the proposed features are perceptual. Our ASR results verify the effectiveness of the proposed unsupervised features in such tasks as LVCSR on WSJ and ASR on noisy low-resource telephone conversations, compared with the supervised bottleneck features from Kaldi in ASR performance.

Published
2023

33. Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN.

Author

Mao, Kaining, Zhang, Wei, Wang, Deborah Baofeng, Li, Ang, Jiao, Rongqi, Zhu, Yanhui, Wu, Bin, Zheng, Tiansheng, Qian, Lei, Lyu, Wei, Ye, Minjie, and Chen, Jie

Abstract

Depression is increasingly impacting individuals both physically and psychologically worldwide. It has become a global major public health problem and attracts attention from various research fields. Traditionally, the diagnosis of depression is formulated through semi-structured interviews and supplementary questionnaires, which makes the diagnosis heavily relying on physicians’ experience and is subject to bias. However, since the pathogenic mechanism of depression is still under investigation, it is difficult for physicians to diagnose and treat, especially in the early clinical stage. As smart devices and artificial intelligence advance rapidly, understanding how depression associates with daily behaviors can be beneficial for the early stage depression diagnosis, which reduces labor costs and the likelihood of clinical mistakes as well as physicians bias. Furthermore, mental health monitoring and cloud-based remote diagnosis can be implemented through an automated depression diagnosis system. In this article, we propose an attention-based multimodality speech and text representation for depression prediction. Our model is trained to estimate the depression severity of participants using the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset. For the audio modality, we use the collaborative voice analysis repository (COVAREP) features provided by the dataset and employ a Bidirectional Long Short-Term Memory Network (Bi-LSTM) followed by a Time-distributed Convolutional Neural Network (T-CNN). For the text modality, we use global vectors for word representation (GloVe) to perform word embeddings and the embeddings are fed into the Bi-LSTM network. Results show that both audio and text models perform well on the depression severity estimation task, with best sequence level $F_{1}$ F 1 score of 0.9870 and patient-level $F_{1}$ F 1 score of 0.9074 for the audio model over five classes (healthy, mild, moderate, moderately severe, and severe), as well as sequence level $F_{1}$ F 1 score of 0.9709 and patient-level $F_{1}$ F 1 score of 0.9245 for the text model over five classes. Results are similar for the multimodality fused model, with the highest $F_{1}$ F 1 score of 0.9580 on the patient-level depression detection task over five classes. Experiments show statistically significant improvements over previous works. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Modeling Unsupervised Empirical Adaptation by DPGMM and DPGMM-RNN Hybrid Model to Extract Perceptual Features for Low-Resource ASR

Author

Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, Nakamura, Satoshi, Wu, Bin, Sakti, Sakriani, Zhang, Jinsong, and Nakamura, Satoshi

Abstract

Speech feature extraction is critical for ASR systems. Such successful features as MFCC and PLP use filterbank techniques to model log-scaled speech perception but fail to model the adaptation of human speech perception by hearing experiences. Infant perception that is adapted by hearing speech without text may cause permanent brain state modifications (engrams) that serve as a physical fundamental basis for lifetime speech perception formation. This realization motivates us to propose to model such an unsupervised adaptation process, where adaptation denotes perception that is affected or changed by the history of experiences, with the Dirichlet Process Gaussian Mixture Model (DPGMM) and the DPGMM-RNN hybrid model to extract perceptual features to improve ASR. Our proposed features extend MFCC features with posteriorgrams extracted from the DPGMM algorithm or the DPGMM-RNN hybrid model. Our analysis shows that the DPGMM and DPGMM-RNN model perplexities agree with infant auditory perplexity to support that the proposed features are perceptual. Our ASR results verify the effectiveness of the proposed unsupervised features in such tasks as LVCSR on WSJ and ASR on noisy low-resource telephone conversations, compared with the supervised bottleneck features from Kaldi in ASR performance., identifier:2329-9304

Published
2022

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