Musical timbre style transfer with diffusion model.

In this work, we focus on solving the problem of timbre transfer in audio samples. The goal is to transfer the source audio's timbre from one instrument to another while retaining as much of the other musical elements as possible, including loudness, pitch, and melody. While image-to-image style transfer has been used for timbre and style transfer in music recording, the current state of the findings is unsatisfactory. Current timbre transfer models frequently contain samples with unrelated waveforms that affect the quality of the generated audio. The diffusion model has excellent performance in the field of image generation and can generate high-quality images. Inspired by it, we propose a kind of timbre transfer technology based on the diffusion model. To be specific, we first convert the original audio waveform into the constant-Q transform (CQT) spectrogram and adopt image-to-image conversion technology to achieve timbre transfer. Lastly, we reconstruct the produced CQT spectrogram into an audio waveform using the DiffWave model. In both many-to-many and one-to-one timbre transfer tasks, we assessed our model. The experimental results show that compared with the baseline model, the proposed model has good performance in one-to-one and many-to-many timbre transfer tasks, which is an interesting technical progress.

Research and application of surface heat treatment for CO2 continuous laser ablation of polymeric methyl methacrylate materials.

Based on the influence of a filamentous laser Gaussian heat source and its movement speed on Polymeric Methyl Methacrylate materials (PMMA sheets), the physical model of heat transfer of PMMA materials by CO2 continuous laser ablation was established. Numerical simulation research on heat transfer in CO2 continuous laser processing of PMMA sheets was carried out by applying the heat transfer model, and experiments on continuous laser processing of PMMA sheets were conducted on the basis of the numerical simulation results. Theoretical and experimental research indicated that under relevant conditions, when the laser power was 20 W, the maximum surface temperature of PMMA sheet was approximately 520 K, which was higher than the melting temperature of the PMMA material, achieving the transformation of the PMMA material from solid to liquid phase in the laser ablation area. When the laser power was 40 W, the CO2 continuous laser could vaporize the PMMA material, cracking the polymer structure of polymethyl methacrylate. When the laser power was 80 W, the maximum surface temperature of the PMMA sheet was approximately 1300 K, and the processing efficiency of CO2 continuous laser ablation of the PMMA material was the highest. The above research provided theoretical guidance and process optimization for the research of CO2 continuous laser ablation of PMMA sheets. The consistency between the experimental results and the numerical simulation results demonstrated the correctness and feasibility of the theoretical model, which has certain universality and reference value for the optimization research of laser processing non-metallic materials and polymer materials.

Personalized movie recommendations based on deep representation learning.

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.