Identification Model of Electronic Music Signal based on Optimization Group Algorithm

As the basic component of music recognition and music information recording and retrieval, the recognition and estimation of music signals and the recording and retrieval of music signals have attracted more and more attention of relevant researchers. The diversity of music signals determines the complexity and difficulty of music signal recognition. The recognition model of electronic music signal is a method to extract music information from input audio signal. The purpose of this process is to identify the music content in the audio signal and extract the music content from the original sound. In this case, we can say that identifying music content means finding out the type of instrument played in the track or which music style it belongs to. This process is also called extraction because it extracts something from another thing (in our case, sound). In order to recognize electronic music signals, we need some preprocessing steps before starting the recognition algorithm. This process is based on an optimization algorithm, also known as an optimization group algorithm. The basic concept of this recognition method is as follows: input data consists of two signals, one of which contains all information about tone (frequency), and the other contains information about amplitude (volume). The output signal will contain only frequency components and no other components. To achieve this goal, we need to find out how much energy each component has in the input data by using optimization algorithms (such as newton).