Abstract
Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.
Highlights
With the increasingly vigorous development of the modern Internet, music has new media and carriers, and more and more music products are derived
Most of the popular melodies are mixed to learn, and the advantages of various styles are integrated to provide a data reference for the accompaniment arrangement of the input single melody songs. e problem of accompaniment chord selection of single note theme and the optimization of chord sequence are solved by the accompaniment hidden Markov model [18,19,20]. e input melody is segmented, and the input melody mode is unified in different songs and modes, and the single melody song is transformed into the standard C major without changing the internal sound group structure of the melody itself. erefore, it greatly facilitates the arrangement of chords
As can be seen from the comparison results in the figure above, compared with the original traditional PCP features, the improved PCP features used in this article have improved the accuracy of chord arrangement to a certain extent. e experimental results obtained using the improved PCP features proposed in this article. e accuracy of chord arrangement In Vacation, Better Hurry Up, and Holiday Door Time increased by 6.65%, 6.58%, and 6.14%, respectively, while in Cool Hun Day and Better Door Us, the accuracy increased by 2.89% and 3.01%, respectively
Summary
With the increasingly vigorous development of the modern Internet, music has new media and carriers, and more and more music products are derived. Most musicians think music itself is extremely emotional, subjective, audio, a form of art, many segments of the rhythm of the music from the composer, fragmentary, and the creation inspiration of discontinuity; so for the inspiration of fragmentation and randomness, it is difficult to by a certain fixed computer algorithms to replicated and created again, So, it is more difficult to use computers to help us compose music, but as more and more computer algorithms are introduced into the field of music composition, through hidden Markov algorithm, stochastic process, genetic algorithm, artificial neural network, and so on, algorithmic composition is easier to apply to the current music form It can be done through the computer simulation in the world with all kinds of music styles and forms [2]. By introducing the maximum likelihood criterion decision tree algorithm, Xue et al calculated the likelihood coefficients between all single notes and calculated the occurrence times of adjacent intervals at different times. e chord sequence obtained from the combination of the single note with the most occurrence times, and the largest likelihood coefficient was taken as the final matching result [7]. erefore, solving the automatic arrangement of music chords has become a hot research direction of computer at the present stage
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