The system library consists of the statistics which are objectively collected from the analysis of large amount of sampled musical works. The future prospects aims to expand on the number of musical genres and styles of different composers that are involved.
The process of music learning and creating can be desolate, depressing and exhausting. However, through collective work from the cyberspace, collaboration between users will, promote the exchange of ideas, diminish the amount of work required, stimulates imaginations, and the outcome of such artwork if carefully designed will be unimaginable.
In addition, a special-purpose database can be set up for each user’s own liking.
The collaborative artworks can be arranged in different hierarchical aesthetic orders and further deduced into Markov Chain and stored in the database library. The database will no longer contain past musical works but are capable of absorbing different music styles including the post-modern composition techniques from the collective artworks. This creative force will grow continuously and thus enhance the artificial intelligence of the system.
The purpose of computer program is to process all the indispensable but repetitive and time consuming calculation. For an average musician, it takes ten to even twenty years to master the craft of composition. It does required seemingly endless practices and compositional exercises to really grasp the fundamentals. However, for this privilege is not offered to the majorities.
Newton has said, “If whom I watch farther than someone else, that is because I stand at the giant's shoulder.” In this research, piling out a giant's shoulder has been attempted for the implementation of the automated composition system. Using simple and user-friendly interface, people can composed with the proper aid from the system to create complex and intelligent music.
Life is very transient, but the creativity of life is infinite. How to create unlimited possibility within a limited boundary is left for one to interpret. This research leads users to break the wide gap of composition grammar, and creates the bridges to link music with the masses accordingly.
References:
[1] Xenakis， I. 1970. “Towards a Metamusic.” Tempo 93:2–19.
[2] Xenakis， I. 1971. Formalized Music. Bloomington: Indiana University Press.
[3] Xenakis， I. 1976. Musique， Architecture. Paris: Casterman.
[4] Xenakis， I. 1988. “Sur le Temps.” In Redécouvrir le Temps.
[5] Bruxelles: Editions de l’Université de Bruxelles 193–200.
[6] Xenakis， I. 1989. “Concerning Time.” Perspectives of New Music 27(1):84–92.
[7] Xenakis， I. 1990. “Sieves.” Perspectives of New Music 28(1):58–78.
[8] Xenakis， I. 1992. Formalized Music. Hillsdale， New York: Pendragon Press.
[9] Xenakis， I. 1994. Kéleütha. Paris: L’Arche.
[10] Xenakis， I. 1996. “Determinacy and Indeterminacy.” Organised Sound 1(3):143–155.
[11] Wiggins G， Papadopoulos G， Phon-Amnuaisuk S， Tuson A. Evolutionary methods for musical composition. 1998. http://citeseer.ist.psu.
edu/wiggins98evolutionary.html
[12] Gers FA， Schmidhuber J. LSTM recurrent networks learn simple context free and context sensitive languages. IEEE Trans. on Neural Networks， 2001，
12(6):1333−1340.
[13] Capanna A. Iannis xenakis Architect of light and sound. Nexus Network Journal，
2001，3(2). http://www.nexusjournal.com/ Capanna-en.html
[14] Cope D. MP3 Files of David Cope and Experiments in Musical Intelligence.
2005. http://arts.ucsc.edu/faculty/cope/mp3page.html
[15] Jacob BJ. Algorithmic composition as a model of creativity. Organised Sound，
1996，1(3):157−165.
[16] Jones， E. 2001. “Residue-Class Sets in the Music of Iannis
[17] Xenakis: An Analytical Algorithm and a General Intervallic Expression.”
Perspectives of New Music 39(2):229–261.
[18] Koenig， G. M. 1970. “Project Two—A Programme for Musical Composition.”
Electronic Music Reports 3. Utrecht: Institute of Sonology.
[19] Malherbe， C.， G. Assayag， and M. Castellengo. 1985. “Functional Integration of Complex Instrumental Sounds in Musical Writing.” Proceedings of the
International Computer Music Conference San Francisco， California: International
Computer Music Association: pp. 185–192.
[20] Johnson R， Cope D. Computer and musical style. 1991.
http://csml.som.ohio-state.edu/Music839C/Notes/Cope.html
[21] Biles JA. Genjam: A genetic algorithm for generating jazz solos. In: Proc. of the Int’l Computer Music Conf. San Francisco: ICMA， 1994. 131−137.
[22] Steedman M. The blues and abstract truth: Music and mental models. In:
Garnham A， Oakhill J， eds. Mental Models in Cognitive Science. Erlbaum， 1996.
305−327.
[23] Bartetzki A. CMask， a stochastic event generator for Csound. 1997.
http://gnom.kgw.tu-berlin.de/~abart/CMaskMan/ CMask-Manual.htm
[24] Leman M. Artificial neural networks in music research. In: Marsden A， Pople A，
eds. Computer Representations and Models in Music. London: Academic Press，
1992. 265−301.
[25] Alpen A. Techniques for algorithmic composition of music. 1995.
http://alum.hampshire.edu/~adaF92/algocomp/algocomp95.html
[26] Riotte， A. 1992. “Formalisation des échelles de hauteurs en analyse et en composition.” Actes du Colloque “Musique et Assistance Informatique.” Marseille:
Musique et Informatique de Marseille.
[27] Solomos， M. 1996. Iannis Xenakis (Echos du XXe siècle). Mercuès: P.O.
Editions.
[28] Solomos， M. 2002. “Xenakis’ Early Works: From ‘Bartokian Project’ to
‘Abstraction’.” Contemporary Music Review 21(2–3):21–34.
[29] Squibbs， R. 1996. “An Analytical Approach to the Music of Iannis Xenakis:
Studies of Recent Works.” Dissertation. Yale University.
[30] Squibbs， R. 2002. “Some Observations on Pitch， Texture， and Form in Xenakis’ Mists.” Contemporary Music Review 21(2–3):91–108.
[31] Chen CCJ， Miikkulainen R. Creating melodies with evolving recurrent neural networks. 2001. http://www.cs.utexas.edu/users/nn/
downloads/papers/chen.ijcnn01.pdf
[32] Unemi T. A tool for multi-part music composition by simulated breeding. In:
Gedau A， ed. Artificial Life VIII. Cambridge: MIT Press， 2002. 410−413.
[33] Ames C， Domino M. Cybernetic composer: an overview. In: Balaban M，
Ebcioglu K， Laske O， eds. Understanding Music with AI. Cambridge: AAAI Press，
1992. 186−205.
[34] Toiviainen P. Symbolic AI versus connectionism in music research. In: Miranda E， ed. Readings in Music and Artificial Intelligence. Amsterdam: Harwood
Academic Publishers， 2000. 47−67.
[35] Grout DJ. History of Western Music. 5th ed.， New York: W. W. Norton &
Company， 1996.
[36] Amiot， E.， et al. 1986. “Duration Structure Generation and Recognition in Musical Writing.” Proceedings of the International Computer Music Conference. San Francisco， California: International Computer Music Association， pp. 185–192.
[37] Andreatta， M. 2003. “Méthodes algébriques en musique et musicologie du XXe siècle: aspects théoriques， analytiques et compositionnels.” Dissertation. École des Hautes Etudes en Sciences Sociales.
[38] Ariza， C. 2002. “Prokaryotic Groove: Rhythmic Cycles as Real-Value Encoded Genetic Algorithms.” Proceedings of the International Computer Music Conference.
San Francisco， California: International Computer Music Association， pp. 561–567.
[39] Ariza， C. 2003. “Ornament as Data Structure: An Algorithmic Model based on Micro-Rhythms of Csángó Laments and Funeral Music.” Proceedings of the
International Computer Music Conference. San Francisco， California: International
Computer Music Association， pp. 187–193.
[40] Ariza， C. 2004. “An Object-Oriented Model of the Xenakis Sieve for Algorithmic Pitch， Rhythm， and Parameter
[41] Generation.” Proceedings of the International Computer Music Conference. San Francisco， California: International Computer Music Association， pp. 63–70.
[42] Biles JA. Autonomous genJam: Eliminating the fitness bottleneck by eliminating fitness. 2002. http://www.cs.usyd.edu.au/~josiah/ gecco_workshop_biles.pdf
[43] Eck D. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks. In: Boulard H， ed. Neural Networks for Signal Processing XII，
Proc. of the 2002 IEEE Workshop. New York: IEEE， 2002. 747−756
[44] Griffith N， Todd PM. Musical Networks. Cambridge: MIT Press， 1997.
[45] Walker E. Chaos melody theory.
2001.http://www.ziaspace.com/elaine/chaos/ChaosMelodyTheory.pdf [46] Basset BA， Neto JJ. A stochastic musical composer based on adaptive algorithms. 1999. http://gsd.ime.usp.br/sbcm/1999/papers/ Bruno_Basseto.pdf [47] Hochreiter S， Schmidhuber J. Long short-term memory. 1996.
http://citeseer.ist.psu.edu/hochreiter96long.html
[48] Ebcioglu K. An expert system for harmonizing chorales in the style of J. S. Bach.
In: Balaban M， Ebcioglu K， Laske O， eds. Understanding Music with AI.
Cambridge: AAAI Press， 1992. 294−334.
[49] Cope D. Virtual Music: Computer Synthesis of Musical Style. Cambridge: MIT Press， 2001.
[50] Järvelainen H. Algorithmic musical composition. 2000.
http://www.tml.tkk.fi/Studies/Tik-111.080/2000/papers/hanna/alco.pdf
[51] Lewis JP. Creation by refinement and problem of algorithmic music composition.
In: Todd PM， Loy DG， eds. Music and Connectionism. Cambridge: MIT Press/Bradford Books， 1991. 212−228.
[52] Todd PM， Loy G. Music and Connectionism. Cambridge: MIT Press， 1991.
[53] Mozer MC. Neural network composition by prediction: Exploring the benefits of psychophysical constraints and multiscale processing. Cognitive Science， 1994，
6:247−280.
[54] Biles JA. GenJam in transition: From genetic jammer to generative jammer. 2002.
http://www.generativeart.com/papersGA2002/ 8.pdf
Appendix
MIDI note code and GM timbre table
MIDI note code table
No. Note code octave Note
name
（Binary code） （Hex code）
0 0000000 00 -1 C
1 0000001 01 -1 C#
2 0000010 02 -1 D
3 0000011 03 -1 D#
4 0000100 04 -1 E
5 0000101 05 -1 F
6 0000110 06 -1 F#
7 0000111 07 -1 G
8 0001000 08 -1 G#
9 0001001 09 -1 A
10 0001010 0A -1 A#
11 0001011 0B -1 B
12 0001100 0C 0 C
13 0001101 0D 0 C#
14 0001110 0E 0 D
15 0001111 0F 0 D#
16 0010000 10 0 E
17 0010001 11 0 F
18 0010010 12 0 F#
19 0010011 13 0 G
20 0010100 14 0 G#
21 0010101 15 0 A
22 0010110 16 0 A#
23 0010111 17 0 B
24 0011000 18 1 C
25 0011001 19 1 C#
26 0011010 1A 1 D
27 0011011 1B 1 D#
28 0011100 1C 1 E
29 0011101 1D 1 F
30 0011110 1E 1 F#
31 0011111 1F 1 G
32 0100000 20 1 G#
33 0100001 21 1 A
34 0100010 22 1 A#
35 0100011 23 1 B
36 0100100 24 2 C
37 0100101 25 2 C#
38 0100110 26 2 D
39 0100111 27 2 D#
40 0101000 28 2 E
41 0101001 29 2 F
42 0101010 2A 2 F#
43 0101011 2B 2 G
44 0101100 2C 2 G#
45 0101101 2D 2 A
46 0101110 2E 2 A#
47 0101111 2F 2 B
48 0110000 30 3 C
49 0110001 31 3 C#
50 0110010 32 3 D
51 0110011 33 3 D#
52 0110100 34 3 E
53 0110101 35 3 F
54 0110110 36 3 F#
55 0110111 37 3 G
56 0111000 38 3 G#
57 0111001 39 3 A
58 0111010 3A 3 A#
59 0111011 3B 3 B
60 0111100 3C 4 C
61 0111101 3D 4 C#
62 0111110 3E 4 D
63 0111111 3F 4 D#
64 1000000 40 4 E
65 1000001 41 4 F
66 1000010 42 4 F#
67 1000011 43 4 G
68 1000100 44 4 G#
69 1000101 45 4 A
70 1000110 46 4 A#
71 1000111 47 4 B
72 1001000 48 5 C
73 1001001 49 5 C#
74 1001010 4A 5 D
75 1001011 4B 5 D#
76 1001100 4C 5 E
77 1001101 4D 5 F
78 1001110 4E 5 F#
79 1001111 4F 5 G
80 1010000 50 5 G#
81 1010001 51 5 A
82 1010010 52 5 A#
83 1010011 53 5 B
84 1010100 54 6 C
85 1010101 55 6 C#
86 1010110 56 6 D
87 1010111 57 6 D#
88 1011000 58 6 E
89 1011001 59 6 F
90 1011010 5A 6 F#
91 1011011 5B 6 G
92 1011100 5C 6 G#
93 1011101 5D 6 A
94 1011110 5E 6 A#
95 1011111 5F 6 B
96 1100000 60 7 C
97 1100001 61 7 C#
98 1100010 62 7 D
99 1100011 63 7 D#
100 1100100 64 7 E
101 1100101 65 7 F
102 1100110 66 7 F#
103 1100111 67 7 G
104 1101000 68 7 G#
105 1101001 69 7 A
106 1101010 6A 7 A#
107 1101011 6B 7 B
108 1101100 6C 8 C
109 1101101 6D 8 C#
110 1101110 6E 8 D
111 1101111 6F 8 D#
112 1110000 70 8 E
113 1110001 71 8 F
114 1110010 72 8 F#
115 1110011 73 8 G
116 1110100 74 8 G#
117 1110101 75 8 A
118 1110110 76 8 A#
119 1110111 77 8 B
120 1111000 78 9 C
121 1111001 79 9 C#
122 1111010 7A 9 D
123 1111011 7B 9 D#
124 1111100 7C 9 E
125 1111101 7D 9 F
126 1111110 7E 9 F#
127 1111111 7F 9 G
GM timbre table
Piano 0 Acoustic Grand Piano 1 Bright Acoustic Piano 2 Electric Grand Piano 3 Honky-tonk Piano 4 Rhodes Piano 5 Chorused Piano 6 Harpsichord 7 Clavichord Pitched Percussion 8 Celesta
9 Glockenspiel 10 Music box 11 Vibraphone 12 Marimba
13 Xylophone 14 Tubular Bells 15 Dulcimer
Organ 16 Hammond Organ 17 Percussive Organ 18 Rock Organ 19 Church Organ 20 Reed Organ 21 Accordian 22 Harmonica 23 Tango Accordian Guitar 24 Acoustic Guitar (nylon)
25 Acoustic Guitar (steel) 26 Electric Guitar (jazz) 27 Electric Guitar (clean) 28 Electric Guitar (muted) 29 Overdriven Guitar 30 Distortion Guitar 31 Guitar Harmonics
Bass 32 Acoustic Bass 33 Electric Bass(finger) 34 Electric Bass (pick) 35 Fretless Bass 36 Slap Bass 1 37 Slap Bass 2
38 Synth Bass 1 39 Synth Bass 2 Strings 40 Violin
41 Viola 42 Cello 43 Contrabass 44 Tremolo Strings 45 Pizzicato Strings 46 Orchestral Harp 47 Timpani
Ensemble 48 String Ensemble 1 49 String Ensemble 2 50 Synth Strings 1 51 Synth Strings 2 52 Choir Aahs 53 Voice Oohs 54 Synth Voice 55 Orchestra Hit Brass 56 Trumpet
57 Trombone 58 Tuba
59 Muted Trumpet 60 French Horn 61 Brass Section 62 Synth Brass 1
63 Synth Brass 2 Reed 64 Soprano Sax
65 Alto Sax 66 Tenor Sax 67 Baritone Sax 68 Oboe
69 English Horn 70 Bassoon 71 Clarinet Pipe 72 Piccolo
73 Flute 74 Recorder 75 Pan Flute 76 Bottle Blow 77 Shakuhachi 78 Whistle 79 Ocarina
Synth Lead 80 Lead 1 (square) 81 Lead 2 (sawtooth) 82 Lead 3 (caliope lead) 83 Lead 4 (chiff lead) 84 Lead 5 (charang) 85 Lead 6 (voice) 86 Lead 7 (fifths) 87 Lead 8 (bass+lead)
Synth Pad 88 Pad 1 (new age) 89 Pad 2 (warm) 90 Pad 3 (polysynth) 91 Pad 4 (choir) 92 Pad 5 (bowed) 93 Pad 6 (metallic) 94 Pad 7 (halo) 95 Pad 8 (sweep) Synth Effects 96 FX 1 (rain)
97 FX 2 (soundtrack) 98 FX 3 (crystal) 99 FX 4 (atmosphere) 100 FX 5 (brightness) 101 FX 6 (goblins) 102 FX 7 (echoes) 103 FX 8 (sci-fi) Ethnic 104 Sitar
105 Banjo 106 Shamisen 107 Koto 108 Kalimba 109 Bagpipe 110 Fiddle 111 Shanai
Percussive 112 Tinkle Bell
113 Agogo 114 Steel Drums
115 Woodblock 116 Taiko Drum 117 Melodic Tom 118 Synth Drum 119 Reverse Cymbal Sound Effects 120 Guitar Fret Noise
121 Breath Noise 122 Seashore 123 Bird Tweet 124 Telephone Ring 125 Helicopter 126 Applause 127 Gunshot General MIDI percussion
instruments
MIDI percussion instruments 35 Acoustic Bass Drum 36 Bass Drum 1
37 Side Stick 38 Acoustic Snare 39 Hand Clap 40 Electric Snare 41 Low Floor Tom 42 Closed Hi-Hat 43 High Floor Tom
44 Pedal Hi-Hat 45 Low Tom 46 Open Hi-Hat 47 Low-Mid Tom 48 Hi-Mid Tom 49 Crash Cymbal 1 50 High Tom 51 Ride Cymbal 1 52 Chinese Cymbal 53 Ride Bell 54 Tambourine 55 Splash Cymbal 56 Cowbell
57 Crash Cymbal 2 58 Vibraslap 59 Ride Cymbal 2 60 Hi Bongo 61 Low Bongo 62 Mute Hi Conga 63 Open Hi Conga 64 Low Conga 65 High Timbale 66 Low Timbale 67 High Agogo 68 Low Agogo
69 Cabasa 70 Maracas 71 Short Whistle 72 Long Whistle 73 Short Guiro 74 Long Guiro 75 Claves
76 Hi Wood Block 77 Low Wood Block 78 Mute Cuica 79 Open Cuica 80 Mute Triangle 81 Open Triangle