BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance

Qin, Haotong; Ma, Xudong; Ding, Yifu; Li, Xiaoyang; Zhang, Yang; Ma, Zhuo; Wang, Jiakai; Luo, Jie; Liu, Xianglong

doi:10.1109/tnnls.2023.3243259

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…KWS systems are less reliant on high-quality audio inputs. They are created to be cheap and flexible and to run accurately and reliably on low-resource gadgets such as embedded edge devices [ 50 , 51 , 52 , 53 ].…”

Section: Artificial Intelligence In Pain Detectionmentioning

confidence: 99%

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

et al. 2023

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Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

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Section: Artificial Intelligence In Pain Detectionmentioning

confidence: 99%

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

et al. 2023

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“…However, the integration of these networks results in larger model sizes and higher computational costs, limiting their practical application on resource-constrained devices [10]. To address these challenges, several compression and acceleration methods have been proposed, including pruning [11][12][13], quantization [14][15][16][17], kernel decomposition [18], and efficient inference backends [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Quantized Graph Neural Networks for Image Classification

Xu,

Ma,

Zeng

et al. 2023

Mathematics

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Researchers have resorted to model quantization to compress and accelerate graph neural networks (GNNs). Nevertheless, several challenges remain: (1) quantization functions overlook outliers in the distribution, leading to increased quantization errors; (2) the reliance on full-precision teacher models results in higher computational and memory overhead. To address these issues, this study introduces a novel framework called quantized graph neural networks for image classification (QGNN-IC), which incorporates a novel quantization function, Pauta quantization (PQ), and two innovative self-distillation methods, attention quantization distillation (AQD) and stochastic quantization distillation (SQD). Specifically, PQ utilizes the statistical characteristics of distribution to effectively eliminate outliers, thereby promoting fine-grained quantization and reducing quantization errors. AQD enhances the semantic information extraction capability by learning from beneficial channels via attention. SQD enhances the quantization robustness through stochastic quantization. AQD and SQD significantly improve the performance of the quantized model with minimal overhead. Extensive experiments show that QGNN-IC not only surpasses existing state-of-the-art quantization methods but also demonstrates robust generalizability.

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