Music intelligence universe server
An artificial intelligence song/music recommendation system and method is provided that allows music shoppers to discover new music. The system and method accomplish these tasks by analyzing a database of music in order to identify key similarities between different pieces of music, and then recommends pieces of music to a user depending upon their music preferences.
1. A method of recommending music comprising:
a) establishing a digital database comprising a plurality of digital song files;
b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics;
wherein each selected quantifiable characteristic is a physical parameter based on human perception selected from the group consisting of:
brightness;
bandwidth;
tempo;
volume;
rhythm;
low frequency;
noise;
octave, and
how said characteristics change over time;
c) compiling a song vector comprising a list of said numerical values for each of said plurality of selected characteristic for each said song file;
d) comparing the song vector for each song in the database with the song vector for every other song in the database and establishing a plurality of affinity values for each song, wherein each said affinity value corresponds to the similarity of one song to another; and
e) using a topological clustering engine based on physical pattern recognition to display a representation of at least one song vector on a two-dimensional array wherein the distance between the at least one song vector representation and any other song vector representation corresponds to the affinity value.
2. The method according to claim 1 , wherein said representation of the song vector uses color codes or other symbols to identify different genres of songs.
3. The method according to claim 1 , wherein at least some of the characteristics are based on audio perceived global characteristics.
4. The method according to claim 3 , wherein said global characteristics are selected from the group consisting of:
happy;
sad;
calm; and
energetic.
5. The method according to claim 1 , wherein said two-dimensional array comprises a computerized graphic user interface.
6. The method according to claim 5 , wherein said computerized graphic user interface is displayed in a location selected from the group consisting of:
in-store retail terminals;
retail internet websites;
personal computers
personal music player devices; and
mobile phone handsets.
7. The method according to claim 1 , wherein said digital database comprising a plurality of digital song files is selected from the group consisting of:
private music collection;
radio station music library;
recording label music library;
music store song library; and
combinations of the above.
8. A computer implemented method of determining a user's preference of music, comprising the steps of:
providing a digital database comprising a plurality of digital song files:
providing an analysis engine having software for use in a computer processor adapted to execute said software;
using said computer processor to analyze each said digital song file to determine a numerical value for each of a plurality of quantifiable characteristics;
wherein each quantifiable characteristic is a physical parameter based on human perception selected from the group consisting of:
brightness;
bandwidth;
tempo;
volume;
rhythm;
low frequency;
noise;
octave, and
how said characteristics change over time;
using said computer processor to create a multidimensional song vector for each said digital song file, said multidimensional song vector representing the numerical values for each of said quantifiable characteristics;
using said computer processor to compare the song vector for each song in the database with the song vector for every other song in the database and to establish a plurality of affinity values for each song, wherein each said affinity value corresponds to the similarity of one song to another;
said computer processor using a topological clustering engine based on physical pattern recognition to determine the distance between each song vector in two dimensional space based on the affinity value and calculating a position for plotting each song on a two-dimensional array; and
displaying at least a portion of the two-dimensional array with a representation of at least one song vector, wherein the distance between the at least one song vector representation and any other song vector representation corresponds to the affinity value.
9. The method according to claim 8 , wherein said two-dimensional array comprises a computerized graphic user interface.
10. The method according to claim 9 , wherein said computerized graphic user interface is displayed in a location selected from the group consisting of:
in-store retail terminals;
retail internet websites;
personal computers
personal music player devices; and
mobile phone handsets.
11. The method according to claim 8 , wherein said representation of the song vector uses color codes or other symbols to identify a genre of the represented song.
12. The method according to claim 8 , further comprising the steps of combining the plurality of quantifiable characteristics with a plurality of audio perceived global characteristics.
13. The method according to claim 12 , wherein said global characteristics are selected from the group consisting of:
happy;
sad;
calm; and
energetic.
14. The method according to claim 12 , wherein the step of displaying at least a portion of the two-dimensional array with a representation of at least one song vector further comprises the step of displaying at least a portion of the two-dimensional array based on the combination of quantifiable characteristics and global characteristics.
15. The method according to claim 8 , wherein said digital database comprising a plurality of digital song files is selected from the group consisting of:
private music collection;
radio station music library;
recording label music library;
music store song library; and
combinations of the above.