What question did this study set out to answer?

To develop a model for accurately assessing interior sound quality in vehicles using advanced neural networks.

Interior sound quality evaluation model based on convolutional neural networks and bi-directional long short-term memory

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To develop a model for accurately assessing interior sound quality in vehicles using advanced neural networks.
Developed an evaluation model integrating convolutional neural networks (CNN) and bi-directional long short-term memory (Bi-LSTM).
Addressed limitations in multi-dimensional feature extraction and adaptability to dynamic data.
Utilized a training set to refine prediction accuracy.
Achieved a maximum prediction accuracy of 96% in the training set.
Demonstrated no significant overfitting, indicating robustness of the model.
Supported the model's generalization ability in sound quality assessment.

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Evaluating in-vehicle sound quality with a new model improves prediction accuracy, indicating better consumer insights.

Zhang et al. (2026) studied this question.

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