MODIFICATION OF MIDI INSTRUMENTS TRACKS
A system receives output from a machine learning algorithm. The machine learning algorithm was trained to learn a music characteristic of work of music. The system then receives a musical instrument digital interface (MIDI) track. The MIDI track includes a MIDI characteristic of the MIDI track. The system finally modifies the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music.
1 . A process comprising:
receiving into a computer processor output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music;
receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track; and
modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music.
2 . The process of claim 1 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track.
3 . The process of claim 1 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track.
4 . The process of claim 1 , wherein the work of music is generated by a sole musician.
5 . The process of claim 1 , wherein the work of music is generated by a plurality of musicians.
6 . The process of claim 1 , wherein the work of music comprises a raw data track of music of a sole musician, or the work of music comprises a composite music track including the music of the sole musician and music of other musicians.
7 . The process of claim 1 , wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms.
8 . The process of claim 1 , wherein the music data comprise audio music data.
9 . A non-transitory machine-readable medium comprising instructions that when executed by a computer processor executes a process comprising:
receiving into the computer processor output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music;
receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track; and
modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music.
10 . The non-transitory machine-readable medium of claim 9 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track.
11 . The non-transitory machine-readable medium of claim 9 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track.
12 . The non-transitory machine-readable medium of claim 9 , wherein the work of music is generated by a sole musician.
13 . The non-transitory machine-readable medium of claim 9 , wherein the work of music is generated by a plurality of musicians.
14 . The non-transitory machine-readable medium of claim 9 , wherein the work of music comprises a raw data track of music of a sole musician, or the work of music comprises a composite music track including the music of the sole musician and music of other musicians.
15 . The non-transitory machine-readable medium of claim 9 , wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms.
16 . The non-transitory machine-readable medium of claim 9 , wherein the music data comprise audio music data.
17 . A system comprising:
a computer processor; and
a computer memory coupled to the computer processor:
wherein the computer processor and computer memory are operable for:
receiving into the computer processor output from a machine learning algorithm, the machine learning algorithm trained to learn a music characteristic of work of music;
receiving into the computer processor a musical instrument digital interface (MIDI) track, the MIDI track comprising a MIDI characteristic of the MIDI track; and
modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music.
18 . The system of claim 17 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises integrating or substituting a style of a musician associated with the work of music into the MIDI track.
19 . The system of claim 17 , wherein the modifying the MIDI characteristic of the MIDI track as a function of the music characteristic of the work of music comprises randomizing one or more instruments on the MIDI track.
20 . The system of claim 17 , wherein the music characteristic and the MIDI characteristic comprise one or more of notes, chord progressions, key changes, attack and sustain patterns, transition patterns, voicing techniques, timings and rhythms.