Interference Quality Assessment of Speech Communication Based on Deep Learning

Wang, Sen; Lin, Yuan; Hao, Ming; Xu, Huaitao; Tian, Qiao

doi:10.1109/tr.2022.3160587

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Referring to the visualization of the training process in Wang et al. (2023), the loss values of the training set and the accuracies of the validation set are stored to generate the curves, as shown in Figure 10. Graph (a) depicts the curve of training set loss values for the four models over epochs, whereas graph (b) illustrates the curve of validation set accuracy for the same four models.…”

Section: Resultsmentioning

confidence: 99%

Nondestructive testing of runny salted egg yolk based on improved ConvNeXt‐T

Chen,

Chang,

Chen

et al. 2024

Journal of Food Science

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Salted egg yolks from salted duck eggs are widely utilized in the domestic and international food industry as both raw materials and ingredients. When salted egg yolks are not fully cured and matured, they exist in a fluid state, with a mixture of solid and liquid internally. Due to this composition, they are susceptible to deterioration during storage and usage, necessitating their detection and classification. In this study, a dataset specifically for salted egg yolks was established, and the ConvNeXt‐T model, employed as the benchmark model, underwent two notable improvements. First, a lightweight location‐aware circular convolution (ParC) was introduced, utilizing a ParC‐block to replace a portion of the original ConvNeXt‐T block. This enhancement aimed to overcome the limitations of convolution in extracting global feature information while integrating the global sensing capability of vision transformer and the localization capability of convolution. Additionally, the activation function was modified through substitution. These improvements resulted in the final model. Experimental results indicate that the enhanced model exhibits faster convergence on the custom salted egg yolk dataset compared to the baseline model. Furthermore, a significant reduction of model parameters by a factor of 4 led to a 2.167 percentage point improvement in the accuracy of the test set. The ParC‐ConvNeXt‐SMU‐T model achieved an accuracy of 96.833% with 26.8 million parameters. Notably, the improved model demonstrates exceptional effectiveness in recognizing salted egg yolks.Practical ApplicationThis study can be widely applied in the process of salted egg yolk production and quality inspection, which can improve the actual sorting efficiency of salted egg yolks and reduce the labor cost at the same time. It can also be used for nondestructive testing of salted egg yolks by governmental enterprises and other regulatory authorities.

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Section: Resultsmentioning

confidence: 99%