Abstract
Digitization and analysis processing technology of music signals is the core of digital music technology. The paper studies the music signal feature recognition technology based on the mathematical equation inversion method, which is aimed at designing a method that can help music learners in music learning and music composition. The paper firstly studies the modeling of music signal and its analysis and processing algorithm, combining the four elements of music sound, analyzing and extracting the characteristic parameters of notes, and establishing the mathematical model of single note signal and music score signal. The single note recognition algorithm is studied to extract the Mel frequency cepstrum coefficient of the signal and improve the DTW algorithm to achieve single note recognition. Based on the implementation of the single note algorithm, we combine the note temporal segmentation method based on the energy-entropy ratio to segment the music score into single note sequences to realize the music score recognition. The paper then goes on to study the music synthesis algorithm and perform simulations. The benchmark model demonstrates the positive correlation of pitch features on recognition through comparative experiments and explores the number of harmonics that should be attended to when recognizing different instruments. The attention network-based classification model draws on the properties of human auditory attention to improve the recognition scores of the main playing instruments and the overall recognition accuracy of all instruments. The two-stage classification model is divided into a first-stage classification model and a second-stage classification model, and the second-stage classification model consists of three residual networks, which are trained separately to specifically identify strings, winds, and percussions. This method has the highest recognition score and overall accuracy.
Highlights
The emergence of computer technology and the Internet has facilitated the birth and development of a series of interdisciplinary disciplines that combine science and art
If computer technology is applied to music teaching, on the one hand, it can assist musicians in music teaching to reduce labor intensity, and on the other hand, music learners can carry out music learning independently from teachers to a certain extent and reduce learning costs
By calculating the correlation dimension, we find that the chaotic character of the music signal exists, and it remains stable at a single value despite the multiple differencing, which shows the stability of the chaos in the music signal
Summary
The emergence of computer technology and the Internet has facilitated the birth and development of a series of interdisciplinary disciplines that combine science and art. With the development of artificial intelligence and computing power, we can extract the corresponding features of musical instruments in audio files and train efficient deep convolutional networks to achieve automatic recognition of musical instruments. Many mainstream music search engines are still based on simple text retrieval, manually labeled song titles, artists, or years It would be significant for retrieval efficiency and user experience if retrieval could be based on the content information of the music signal itself, and these features could be automatically identified. Chapter 3: Research on Music Signal Recognition Based on Mathematical Equation Inversion Methods. Mathematical modeling of the music signal is studied, and additive synthesis techniques are used to achieve piano tone reproduction based on the music score as well as note time value information. It mainly summarizes the final research results of the paper, analyzes the shortcomings of the paper in the research process, and provides an outlook for future work because of these shortcomings
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