Choosing the optimum route is the most critical planning content in economic tourism planning. This paper takes the coordinates of tourism attractions in Enshi as the research background, considers the problem of travel route planning as a tourism salesman problem, and solves the shortest circuit path for traveling salesman as the research object. In our research, we chose the genetic algorithm, the Ant Colony Algorithm, and the combination of the two algorithms and compared and analyzed them with the best parameters. The experiment shows that combining the ant colony and genetic algorithms can more effectively find the shortest circuit path in developing tourism planning in the Enshi Scenic Area. At the same time, the effect is obviously better than the results obtained by any of the algorithms alone.
KEYWORDS: Denoising, Signal to noise ratio, Speech recognition, Performance modeling, Education and training, Distortion, Instrument modeling, Feature extraction, Signal attenuation
In order to enhance the noise reduction capability and recognition efficiency of speech recognition technology applied in medical devices, this paper adopts an improved subspace CNN based noise reduction model, adding a subspace projection module with two orthogonal self-attentive mechanisms to the original model, inputting the feature vectors extracted from the convolutional layer to the self-attentive layer, and the two self-attentive layers learn and project orthogonally to each other to obtain the noise embedding vector and speech embedding vectors to extract cleaner speech. In this paper, the improved subspace CNN noise reduction model is compared with the CNN noise reduction model by comparing the distortion degree, segmental signal-to-noise ratio and quality at different signal-to-noise ratios to demonstrate the reliability of the model. The results show that the overall distortion level of the model is reduced by about 7.27%, the overall segmental SNR performance is increased by about 15.27% and the quality is improved by about 8.7%, which is a certain improvement in performance compared with the traditional CNN noise reduction model.
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