DOA estimation using GRNN for acoustic sensor arrays

Yao, Qihai; Wang, Yong; Yang, Yixin; Yang, Long

doi:10.1007/s11045-023-00877-9

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Generally speaking, GRNN mainly has two biggest advantages, on the one hand, the structure is simple, including the input layer, mode layer, summation layer, and output layer. On the other hand, there is only one key parameter called the smoothing factor, a small smoothing factor will cause the network to overfit, while a large smoothing factor may cause the network to underfit [17][18][19].…”

Section: Modelingmentioning

confidence: 99%

Small Sample Sound Quality Prediction Method of Hy-Vo Chain Transmission System Based on Fuzzy Generation

Li,

An,

Cheng

et al. 2024

Applied Sciences

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To improve the noise comfort of the whole machine, it is necessary to establish the sound quality prediction model of the Hy-Vo chain transmission system. Compared with the silent chain transmission system, the Hy-Vo chain transmission system normally operates at a lower speed and cannot have too much load at the limit speed. It is difficult to obtain a sufficient quantity of high-quality noise samples because there are few different working conditions. For small sample sound quality prediction, we use a sample enhancement method called fuzzy generation based on fuzzy mathematics. Firstly, audio samples of the Hy-Vo chain transmission system are collected through noise tests. Secondly, the processed samples are evaluated objectively and subjectively. After a correlation test of the subjective evaluation results, correct subjective evaluation scores of each noise sample are obtained. With the help of fuzzy generation, we can obtain a sufficient number of new samples. By mixing the original samples with the generated samples, a new dataset is created. Through using a general regression neural network (GRNN), support vector regression (SVR) model, and ridge regression (RR) method, the sound quality of the Hy-Vo chain transmission system can be predicted. Different from prediction results under the original dataset, using the fuzzy generation method can not only significantly reduce the prediction error of the model but also improve stability.

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Section: Modelingmentioning

confidence: 99%