Simulation of Human Ear Recognition Sound Direction Based on Convolutional Neural Network

Abstract

In recent years, more and more people are applying Convolutional Neural Networks to the study of sound signals. The main reason is the translational invariance of convolution in time and space. Thereby the diversity of the sound signal can be overcome. However, in terms of sound direction recognition, there are also problems such as a microphone matrix being too large, and feature selection. This paper proposes a sound direction recognition using a simulated human head with microphones at both ears. Theoretically, the two microphones cannot distinguish the front and rear directions. However, we use the original data of the two channels as the input of the convolutional neural network, and the resolution effect can reach more than 0.9. For comparison, we also chose the delay feature (GCC) for sound direction recognition. Finally, we also conducted experiments that used probability distributions to identify more directions.

Mobile Virtual Reality Rail Traffic Congestion Prediction Algorithm Based on Convolutional Neural Network

“…Considering that the traffic flow has a weekly periodicity, the traffic flow at the current moment is similar to that at the same time last week, and the difference is used to subtract the traffic flow at the same time last week from the traffic flow at the current moment. (2) LSTM1 and LSTM2 represent single-layer LSTM and double-layer LSTM, respectively, and the number of hidden layer units is 64; (3) GRU1 and GRU2 indicate that single-layer GRU and double-layer GRU are used, respectively, and the number of hidden layer units is 64; (4) e expansion coefficient of each layer of DCFCN is [1,2,4,8,16,32], the number of convolutional kernels of each layer is 32, and the size of convolutional kernels is 4. As can be seen from Table 2, compared with other comparison models, the proposed DCFCN has the best prediction effect and the lowest in RMSE, MAE, and MAPE indicators.…”

“…e tra c ow prediction method predicts the tra c ow information for a period of time in the future through the established tra c ow prediction model, so as to provide reference information for tra c management and travel of tra c managers and travelers, so as to avoid tra c congestion [3]. BP neural network is a multilayer feed-forward neural network, which has become an important method for research on tra c ow prediction due to its good self-learning ability, generalization ability, and nonlinear mapping ability [4]. Due to the randomness of the initial weights and thresholds selected by BP neural network, it has poor global search ability and is easy to fall into the local optimal solution and slow convergence rate [5].…”