Method and system for music recommendation

Abstract

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. An embodiment enables a user to evaluate a new song's similarity to songs already established as commercially valuable.

1 . A method of analyzing music, said method comprising the steps of: 
 a) providing a digital database comprising a plurality of digital song files;    b) selecting one of said song files for analysis;    c) dividing said selected song file into a plurality of discrete parts;    d) using Fast Fourier Transform techniques on each part of said selected song file to establish a plurality of coefficients, wherein said coefficients are representative of predetermined quantifiable characteristics of said selected song, and; 
 wherein each said predetermined characteristic is a physical parameter based on human perception selected from the group consisting of: 
 brightness;  
 bandwidth;  
 tempo;  
 volume;  
 rhythm;  
 low frequency;  
 noise; and  
 octave, and  
 how said parameters change over time;  
 
   e) determining an average value of the coefficients for each characteristic from each said part of said selected song file;    f) compiling a song vector comprising a sequential list of said average values of the coefficients for each said characteristic for said selected song file; and    g) repeating steps b) through f) for each song in said database.    
   
   
       2 . The method according to  claim 1 , wherein said digital database comprises a plurality of compressed digital song files, said method further comprising the step of: 
 b1) decompressing said selected song file prior to dividing said selected song file into a plurality of discrete parts.    
   
   
       3 . A method of determining a user's music preference, said method comprising the steps of: 
 a) providing 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 said characteristic is a physical parameter based on human perception comprising: 
 brightness;  
 bandwidth;  
 tempo;  
 volume;  
 rhythm;  
 low frequency;  
 noise; and  
 octave, and  
 how said parameters change over time;  
 
   c) compiling a song vector comprising a sequential list of said numerical values for each of said plurality of selected characteristic for each said song file;    d) dividing each said song file into portions of selected size and mathematically analyzing each said portion to determine a numerical value for said plurality of selected characteristics for each said portion and compiling a portion vector comprising a sequential list of numerical values for each of said plurality of characteristics for each said portion;    e) selecting and storing a representative portion of each said song file wherein the portion vector of said representative portion substantially mathematically matches the song vector of said song file;    f) choosing two dissimilar representative portions and enabling said user to listen to both representative portions;    g) permitting said user to indicate which of said two dissimilar representative portions said user prefers; and    h) repeating steps f) and g), as necessary, to establish a taste vector for said user comprising song characteristics that said user prefers.    
   
   
       4 . The method according to  claim 3 , said mathematically analyzing steps further comprising the step of: 
 using fast Fourier Transform techniques to establish a plurality of coefficients, wherein said coefficients are representative of said characteristics of said song.    
   
   
       5 . The method according to  claim 3 , wherein said method is performed via a computer website.  
   
   
       6 . A method of comparing a new song to previously commercially successful songs, said method comprising: 
 a) establishing a digital database comprising a plurality of digital song files wherein said songs have been identified as commercially successful;    b) mathematically analyzing each said digital song file to determine a numerical value for a plurality of selected quantifiable characteristics; 
 wherein said characteristic is a physical parameter based on human perception selected from the group consisting of: 
 brightness;  
 bandwidth;  
 tempo;  
 volume;  
 rhythm;  
 low frequency;  
 noise; and  
 octave, and  
 how said parameters change over time;  
 
   c) compiling a song vector comprising a sequential list of said numerical values for each of said plurality of selected characteristic for each said song file;    d) presenting said new song as a digital music file for comparison;    e) mathematically analyzing said new song file to determine a numerical value for the same plurality of selected quantifiable characteristics;    f) compiling a new song vector comprising a sequential list of said numerical values for each of said plurality of selected characteristic for said new song file;    g) establishing an affinity value for said new song as compared to each song vector in the database by summing the square of the difference between the numerical values of each characteristic in each said vector; and    j) determining the potential for commercial success if said affinity value is below a predetermined threshold.    
   
   
       7 . The method according to  claim 6 , said mathematically analyzing steps further comprising the step of: 
 using fast Fourier Transform techniques to establish a plurality of coefficients, wherein said coefficients are representative of said characteristics of said song.    
   
   
       8 . The method according to  claim 6 , wherein said new song is presented via a computer network.