Method and system for analyzing digital audio files

Abstract

A fingerprint is generated from an unknown audio signal by dividing the unknown audio signal into bins, where each bin includes points representing a feature space. Each of the points is mapped to one of a plurality of predetermined cluster centers based on the distance between each point and the plurality of cluster centers, each cluster center being associated with an element of a codebook. A string of elements is generated based on the mapping and compressed.

1 . A method of generating a fingerprint from an unknown audio signal, the method comprising:
 dividing the unknown audio signal into bins, each bin including a plurality of points representing a feature space;   mapping each of the plurality of points to one of a plurality of predetermined cluster centers based on the distance between each point and the plurality of cluster centers, each cluster center being associated with an element of a codebook;   generating a string of elements based on the mapping; and   compressing the string of elements.   
     
     
         2 . The method according to  claim 1 , further comprising:
 performing a Fourier transform on the plurality of bins to generate a frequency representation of each bin; and   computing at least one feature from the frequency representation of each bin.   
     
     
         3 . The method according to  claim 2 , wherein the at least one feature includes at least one of:
 (a) a spectral centroid;   (b) a spectral rolloff;   (c) a spectral flux;   (d) a peak ratio;   (e) a subband energy vector;   (f) a subband flux; and   (g) a subband energy ratio.   
     
     
         4 . The method according to  claim 1 , wherein each point representing the feature space specifies a location in a multidimensional feature space. 
     
     
         5 . The method according to  claim 1 , wherein the plurality of cluster centers are determined by using a c-means clustering algorithm. 
     
     
         6 . The method according to  claim 1 , wherein the distance between each point and the plurality of cluster centers is based on a Mahalonobis distance. 
     
     
         7 . The method according to  claim 1 , wherein each element of the codebook corresponds to a unique ASCII character. 
     
     
         8 . The method according to  claim 1 , further comprising:
 parsing the compressed set of elements into a plurality of sub-strings of a predefined length of characters.   matching each substring to a database of precomputed signatures to obtain at least one base signature;   assigning a match index value (MI) to each match obtained by the matching given by:
     MI=ΣW   i   *N   i , 
   where W i  is a weight for matches of length i and N i  is a number of substring matches of length i; and   selecting a best match based on the match index value (MI).   
     
     
         9 . A non-transitory computer-readable medium storing a computer instructions, which when executed by one or more processors, causes the one or more processors to execute the steps of:
 dividing the unknown audio signal into bins, each bin including a plurality of points representing a feature space;   mapping each of the plurality of points to one of a plurality of predetermined cluster centers based on the distance between each point and the plurality of cluster centers, each cluster center being associated with an element of a codebook;   generating a string of elements based on the mapping; and   compressing the string of elements.   
     
     
         10 . The non-transitory computer-readable medium according to  claim 9 , further comprising the steps of:
 performing a Fourier transform on the plurality of bins to generate a frequency representation of each bin; and   computing at least one feature from the frequency representation of each bin.   
     
     
         11 . The non-transitory computer-readable medium according to  claim 10 , wherein the at least one feature includes at least one of:
 (a) a spectral centroid;   (b) a spectral rolloff;   (c) a spectral flux;   (d) a peak ratio;   (e) a subband energy vector;   (f) a subband flux; and   (g) a subband energy ratio.   
     
     
         12 . The non-transitory computer-readable medium according to  claim 9 , wherein each point representing the feature space specifies a location in a multidimensional feature space. 
     
     
         13 . The non-transitory computer-readable medium according to according to  claim 9 , wherein the plurality of cluster centers are determined by using a c-means clustering algorithm. 
     
     
         14 . The non-transitory computer-readable medium according to according to  claim 9 , wherein the distance between each point and the plurality of cluster centers is based on a Mahalonobis distance. 
     
     
         15 . The non-transitory computer-readable medium according to according to  claim 9 , wherein each element of the codebook corresponds to a unique ASCII character. 
     
     
         16 . The non-transitory computer-readable medium according to according to  claim 9 , further comprising the steps of:
 parsing the compressed set of elements into a plurality of sub-strings of a predefined length of characters.   matching each substring to a database of precomputed signatures to obtain at least one base signature;   assigning a match index value (MI) to each match obtained by the matching given by:
     MI=ΣW   i   *N   i , 
   where W i  is a weight for matches of length i and N i  is a number of substring matches of length i; and   selecting a best match based on the match index value (MI).   
     
     
         17 . An apparatus for generating a fingerprint from an unknown audio signal, comprising:
 at least one processor operable to perform:   dividing the unknown audio signal into bins, each bin including a plurality of points representing a feature space;   mapping each of the plurality of points to one of a plurality of predetermined cluster centers based on the distance between each point and the plurality of cluster centers, each cluster center being associated with an element of a codebook stored in a memory;   generating a string of elements based on the mapping; and   compressing the string of elements.   
     
     
         18 . The apparatus according to  claim 17 , the processor further operable to perform a Fourier transform on the plurality of bins to generate a frequency representation of each bin and compute at least one feature from the frequency representation of each bin. 
     
     
         19 . The apparatus according to  claim 18 , wherein the at least one feature includes at least one of:
 (a) a spectral centroid;   (b) a spectral rolloff;   (c) a spectral flux;   (d) a peak ratio;   (e) a subband energy vector;   (f) a subband flux; and   (g) a subband energy ratio.   
     
     
         20 . The apparatus according to  claim 17 , wherein each point representing the feature space specifies a location in a multidimensional feature space. 
     
     
         21 . The apparatus according to  claim 17 , wherein the plurality of cluster centers are determined by using a c-means clustering algorithm. 
     
     
         22 . The apparatus according to  claim 17 , wherein the distance between each point and the plurality of cluster centers is based on a Mahalonobis distance. 
     
     
         23 . The method according to  claim 17 , wherein each element of the codebook corresponds to a unique ASCII character. 
     
     
         24 . The apparatus according to  claim 17 , wherein the at one processor is further operable to perform the following steps:
 parsing the compressed set of elements into a plurality of sub-strings of a predefined length of characters.   matching each substring to a database of precomputed signatures to obtain at least one base signature;   assigning a match index value (MI) to each match obtained by the matching given by:
     MI=ΣW   i   *N   i , 
   where W i  is a weight for matches of length i and N i  is a number of substring matches of length i; and   selecting a best match based on the match index value (MI).