MIDI哼唱检索算法研究+文献综述
Music retrieved is in other electronic music library library in the retrieved target music of technology. music retrieved has is strong of application background in mass data storage in the, if on each first music are through artificial do callout then through text retrieved necessarily brings huge of workload Dang data volume increasingly multi artificial of comments strength increased people of workload. but on audio of perception, as music of melody tone sound quality, difficult to with text comments expression and clearly these is based on content of audio retrieved needs research and solution of problem. For most people, because they do not have professional knowledge of music is hard to write melodic feeling down we can tune recorded by Melody extraction technology and can be used by electronic and acoustic methods to a variety of musical instruments and sound playing, So yihou has music gift of ordinary people also can is good of using convenient query music database in the storage of data. sound image graphics, music as human most directly of perception information of mode is people everyday life in the not missing of part in all area up with great of role due to years to Internet bandwidth and speed of great improve makes network music resources became very with commercial value and social value of resources but face huge and dispersed of music resources how at any time retrieved to like of music became a major problem from database in the using various can expression music of means retrieved to by needs of music is called music retrieved. Study on application of digital music, most high level semantic characteristics of music with MIDI music file libraries as source material, in which melody track as the most important music information, obtained from the MIDI file and theme information with great difficulty and error. Paper presents a method of using random forest automatically pulls the melody MIDI file tracks. First track feature are defined based on information entropy theory of entropy, and experimentally determine the entropy to differentiate the characteristics of audio MIDI melody and accompaniment tracks are valid. And then by the MIDI file tracks information entropy and a number of other important features of eigenvectors, building a random forest classifier track where the main melody extraction MIDI file. According to experiments, which have a high accuracy rate, valid for the extraction of main melody MIDI file tracks.
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