The rapid development of artificial intelligence has made various fields to have corresponding connections with it, and music plays an important role in daily life. One of the applications of artificial intelligence in the field of art is to use music generation algorithms to endow machines with the function of generating melody. This ability can provide certain assistance to musicians when composing music, so that music creators can inspire inspiration in the creative process. Researchers have done a lot of work in the automatic generation of music. The piano is widely used in the field of automatic accompaniment and has strong versatility. The main purpose of this paper is to design a piano-based automatic accompaniment system, to think of melody and harmony as a machine learning-like task. By training on a selected series of samples, a database of phonomorphic metastructures is constructed, to systematically collect the original piano accompaniment patterns by building a sound pattern database, and convert the collected original sound patterns into the original sound pattern structure and store in the database. Then, by establishing two Hidden Markov Model (HMM) systems to simulate the thinking mode of the composer’s piano accompaniment process, a melody style related to a certain collection of samples is formed. Finally, the Viterbi algorithm is used to select the appropriate piano accompaniment metastructure in the database to generate the piano accompaniment of the melody section. The experimental results of this paper show that, as far as the accompaniment generation effect is concerned, although the generation effect of this paper is slightly different, the overall difference does not change much. It shows that the effect generated by the method in this paper is relatively stable.

Among the basic elements of music, timbre is one of the most important elements of sound, and it is also the main basis for distinguishing one pronunciation from another. People usually have the ability to “listen and argue” because everyone’s pronunciation is different. However, the existing audio extraction technology has low efficiency and low accuracy. Therefore, this paper aims to discuss the algorithm that can make music timbre feature extraction more accurate and efficient. For audio signal feature extraction, this paper proposed an audio feature based on harmonic components to describe the harmonic structure information in the audio signal spectrum. The algorithm in this paper extracts timbre features from the sound data of Western musical instruments and national musical instruments and analyzes the recognition accuracy. The experimental results showed that the classification accuracy of the four feature extractors is above 92%, among which B has the worst effect, with an accuracy of 92.42%, and D has the best classification effect, with an accuracy of 99.15%, which shows that the feature extraction algorithm designed in this paper combined with the traditional feature extraction algorithm can achieve better results.