Electronic Piano Signal Spectrum Visualization and De-Noising Analysis Algorithm Optimization

The sound of a single note played on the piano is a harmonic structure that can be represented as the superposition of a series of sinusoidal signals. Hence, it is possible to analyze this information from the digital perspective. In this paper, the electronic piano signal spectrum visualization and de-noising analysis algorithm optimization is studied. To begin with, the analysis of electronic audio and piano spectrum characteristics is conducted, at present, digital audio signal matrix technology has synchronous mode and asynchronous mode using the TDM technology. TDM digital audio matrix usually adopts modular design, flexible construction, easy expansion and maintenance. This is selected as the basic structure of the model. Then, the optimization of the denoising analysis algorithm is illustrated. Sparse coding is used for the noise reduction, and the purpose of the dictionary learning algorithm is to adapt an initialized dictionary to a type of signal. In other words, the dictionary learned by this algorithm matches the characteristics of a certain type of signal. Furthermore, the principle of intelligent spectrum sensing is to detect and perceive the environment in multiple directions during the spectrum sensing process, this is used for designed visualization scheme. Through test with the index of SNR and RMSE, the performance of de-noising is evaluated.