Estimation of the number of single channel source signals based on convolutional neural network

Estimating the number of source signals in a single channel is a highly challenging task in the field of signal processing. Accurately estimating the number of source signals is of great significance for numerous applications. In this study, an algorithm for estimating the number of single-channel source signals based on a convolutional neural network (CNN) is proposed. By constructing a shallow yet efficient CNN architecture, the algorithm fully exploits the advantage of CNN in feature extraction, enabling accurate determination of the number of source signals. The experimental results demonstrate that, compared with traditional methods, this algorithm significantly improves the estimation accuracy. The average recognition accuracy in the test set with mixed source signals is 97.22%. It is 2.07% to 17.99% higher than that of other methods. Moreover, the algorithm exhibits excellent stability in multiple repeated experiments. This research not only provides a novel solution for estimating the number of single-channel source signals but also broadens the application ideas of CNN in complex signal processing tasks. It is expected to drive the further development of signal processing technologies in fields such as communication and radar.