Music Signal Processing and Optimization Algorithm Based on Wavelet Neural Network Model

Temporal extrapolation of TEC using WNN during 2007–2018 and comparison against GIM, IRI2016 and Kriging

A data-and-knowledge-driven WNN modeling approach for the absorption refrigeration system

In the process of colour space conversion from CMYK to CIELab, colour difference will be caused, which has a negative impact on the quality of digital printing products. In this article, an improved wavelet neural network (WNN) model optimised by cuckoo search (CS) algorithm is proposed to reduce the colour difference. Initially, the colour space conversion model based on WNN is established. The CS algorithm is used to optimise the initial weights and parameters of dilation and translation in the WNN model. Then, 1296 samples are made to train the CS‐WNN model. Finally, 100 test samples are input into the trained network to obtain the corresponding L, a and b values of CIELab. The experimental results show that the average conversion colour difference () of the proposed model is 3.469. The conversion accuracy and stability of the proposed model are better than the traditional neural network.

Wheel and rail wear seriously affects the safety and reliability of train operations. In this study single-carriage and double-carriage models considering the connecting unit of a high-speed train are developed to investigate the normal forces, lateral forces, and lateral displacements of wheelsets. Based on the results from these models, the Archard wear model is employed to predict the wheel wear. In addition, based on the daily measured data, a nonlinear autoregulatory (NAR) model and a wavelet neural network (WNN) model are developed to predict the wheel wear over a longer time period. The simulation results show that, compared with the single-carriage model, the normal forces, lateral forces, and lateral displacements of the wheelsets close to the connecting unit in the double-carriage model increase to a certain extent dependent on the speed. The wheel wear predictions show that the wheel wear on the wheelsets near the connecting unit is slightly larger than on the wheelsets far from the connecting unit. Based on the mean square error, the NAR model has somewhat better performance in the wheel wear prediction than the WNN model. The research results represent an important contribution to the maintenance and safe operation of high-speed trains.

A model of wavelet neural network (WNN) using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of Particle Swarm Optimization (PSO) and gradient descent algorithm (GD), and is thus called HGDPSO. The Particle Swarm Optimizer has previously been used to train neural networks and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. HGDPSO was proposed for neural network training to avoid premature and eliminate stagnation in PSO. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.

Deep Learning has emerged as a powerful set of computational methods achieving great results in a variety of different tasks. Music signal processing, a field with rich commercial applications, seems to benefit too from this data-driven approach. In this paper a review of the state of the art Deep Learning methods applied on music signal processing is provided. A special focus is given in music information retrieval and music generation. In addition, possible future research directions are discussed.

The security of ferry shows the characteristics of dynamics, randomness and uncertainty, and the system of its security is a typical non-linear system. Due to the limitations of traditional mathematical methods solving the non-linear system, this paper applies the wavelet neural network model for safety evaluation based on the assessment index system and weight algorithm. Results prove that the wavelet neural network model can solve the evaluation problems in the non-linear system, and it can do better than the BP artificial neural networks in fitting accuracy and convergence rate, so the application of wavelet neural network model has an important value.

To develop a high-accuracy method for predicting SCB based on the analysis of the shortcomings of the wavelet neural network (WNN) model, an improved WNN model to predict SCB is proposed herein. The activation function of the WNN is constructed by combining the advantages of Shannon and Gauss ‘window’ functions to improve the WNN. Finally, the improved WNN model is used to predict SCB. The results show that the proposed model has the highest prediction accuracy, stability, and robustness. Moreover, it effectively predicts long-time SCB data. Therefore, the proposed model can predict SCB with high accuracy.

The digitization, analysis, and processing technology of music signals are the core of digital music technology. There is generally a preprocessing process before the music signal processing. The preprocessing process usually includes antialiasing filtering, digitization, preemphasis, windowing, and framing. Songs in the popular wav format and MP3 format on the Internet are all songs that have been processed by digital technology and do not need to be digitalized. Preprocessing can affect the effectiveness and reliability of the feature parameter extraction of music signals. Since the music signal is a kind of voice signal, the processing of the voice is also applicable to the music signal. In the study of adaptive wave equation inversion, the traditional full-wave equation inversion uses the minimum mean square error between real data and simulated data as the objective function. The gradient direction is determined by the cross-correlation of the back propagation residual wave field and the forward simulation wave field with respect to the second derivative of time. When there is a big gap between the initial model and the formal model, the phenomenon of cycle jumping will inevitably appear. In this paper, adaptive wave equation inversion is used. This method adopts the idea of penalty function and introduces the Wiener filter to establish a dual objective function for the phase difference that appears in the inversion. This article discusses the calculation formulas of the accompanying source, gradient, and iteration step length and uses the conjugate gradient method to iteratively reduce the phase difference. In the test function group and the recorded music signal library, a large number of simulation experiments and comparative analysis of the music signal recognition experiment were performed on the extracted features, which verified the time-frequency analysis performance of the wave equation inversion and the improvement of the decomposition algorithm. The features extracted by the wave equation inversion have a higher recognition rate than the features extracted based on the standard decomposition algorithm, which verifies that the wave equation inversion has a better decomposition ability.

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

This study focuses on the development of China’s pet industry and its related sectors, providing an in-depth analysis and projections for its future growth. Using a gray prediction model, the paper forecasts that the market size of China’s pet industry will grow from RMB 3.29 trillion to RMB 4.10 trillion over the next three years. Furthermore, a wavelet neural network model is employed to analyze global demand for pet food. This model incorporates advanced techniques for determining hidden nodes, employs backpropagation for weight adjustment, and utilizes Adam’s algorithm to optimize the learning rate. The analysis predicts an increase in global pet food sales from $138 billion to approximately $149.7 billion within the same period. By integrating diverse factors and data sources, the comprehensive model offers valuable insights to industry professionals and researchers. This research aims to support strategic decision-making and promote the sustainable development of the pet industry in China and globally.

Development of a method for comprehensive water quality forecasting and its application in Miyun reservoir of Beijing, China

Stacked auto-encoders based visual features for speech/music classification

Wavelet transformation has the ability of representing a function and revealing the properties of the function in the joint local regions of the time frequency space. Based on wavelet and artificial neural network, a Wavelet Neural Network (WNN) model predicting the average post-dry out Nusselt number for upward flow in vertical narrow annuli with bilateral heating has been developed. The WNN mode combining the properties of the wavelet transform and the advantages of Artificial Neural Networks (ANN) has some advantages of solving non-linear problem.

A non‐parametric system identification‐based model is presented for damage detection of highrise building structures subjected to seismic excitations using the dynamic fuzzy wavelet neural network (WNN) model developed by the authors. The model does not require complete measurements of the dynamic responses of the whole structure. A large structure is divided into a series of sub‐structures around a few pre‐selected floors where sensors are placed and measurements are made. The new model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively, thus resulting in fast training convergence and high accuracy. A new damage evaluation method is proposed based on a power density spectrum method, called pseudospectrum. The multiple signal classification (MUSIC) method is employed to compute the pseudospectrum from the structural response time series. The methodology is validated using the data obtained for a 38‐storey concrete test model. The results demonstrate the effectiveness of the WNN model together with the pseudospectrum method for damage detection of highrise buildings based on a small amount of sensed data. Copyright © 2007 John Wiley & Sons, Ltd.