Abstract:
A characteristic identifier for digital data is generated. Thereby, the information contained in a digital data set is reduced such that the resulting identifier is made comparable to another identifier made in the same manner. The generated identifiers are used for detecting identical digital data or to determine inexact copies of digital data. In one embodiment of the invention, the digital data is a digital audio signal and the characteristic identifier is called an audio signature. The comparison of identical audio data according to the invention can be carried out without a person actually listening to the audio data. The present invention can be used to establish automated processes to find potential unauthorized copies of audio data, e.g., music recordings, and therefore enables a better enforcement of copyrights in the audio industry.

Description:
FIELD OF THE INVENTION 
     The invention relates generally to digital data. More particularly, the invention relates to a method and system for generating a characteristic identifier for digital data and for detection of identical digital data. 
     BACKGROUND OF THE INVENTION 
     In recent years, an increasing amount of audio data is recorded, processed, distributed, and archived on digital media using numerous encoding and compression formats, such as WAVE, AIFF (Audio Interchange File Format), MPEG (Motion Picture Experts Group), and REALAUDIO. Transcoding or resampling techniques that are used to switch from one encoding format to another almost never produce a recording that is identical to a direct recording in the target format. A similar effect occurs with most compression schemes. Changes in the compression factor or other parameters result in a new encoding and a bit stream that bears little similarity to the original bit stream. Both effects make it rather difficult to establish the identity of one audio recording stored in two different formats. Establishing the possible identity of different audio recordings is a pressing need in audio production, archiving, and copyright protection. 
     During the production of a digital audio recording, usually numerous different versions in various encoding formats come into existence as intermediate steps. These different versions are distributed over a variety of different computer systems. In most cases, these recordings are not cross-referenced and often it has to be established by listening to the recordings whether two versions are identical or not. An automatic procedure will greatly ease this task. 
     A similar problem exists in audio archives that have to deal with material that has been issued in a variety of compilations (such as Jazz or popular songs) or on a variety of carriers (such as the famous recordings of Toscanini with the NBC Symphony orchestra). Often the archive version of the original master of such a recording is not documented and in most cases it can only be decided by listening to the audio recordings whether a track from a compilation is identical to a recording of the same piece on another sound carrier. 
     Copyright protection is a key issue for the audio industry. Copyright protection is even more relevant with the invention of new technology that makes creation and distribution of copies of audio recordings a simple task. While mechanisms to avoid unauthorized copies solve one side of the problem, it is also required to establish processes to detect unauthorized copies. 
     SUMMARY OF THE INVENTION 
     According to one aspect of the present invention, a characteristic identifier for digital data is generated. The information contained in the data is thereby reduced such that the resulting identifier is made comparable to another identifier. Identifiers generated according to the present invention are resistant against artifacts that are introduced into digital data by all common compression techniques. Using such identifiers therefore allows the identification of identical digital data independent of the chosen representation and compression methods. 
     Furthermore, the generated identifiers are used for detecting identical digital data. It is decided whether sets of digital data are identical depending on the distance between the identifiers belonging to them. A faster, cheaper and more reliable process of detection of identical digital data is established. 
     In a preferred embodiment of the present invention, the digital data is a digital audio signal and the characteristic identifier is called an audio signature. The comparison of identical audio data according to the invention can be carried out without a person actually listening to the audio data. 
     The present invention can be used to establish automated processes to find potential unauthorized copies of audio data, e.g., music recordings, and therefore enables a better enforcement of copyrights in the audio industry. 
     A more complete understanding of the present invention, as well as further features and advantages of the present invention, will be obtained by reference to the following detailed description and drawings 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a flow diagram describing the generation of an audio signature according to an embodiment of the present invention; 
     FIG. 2 is a flow diagram describing a series expansion according to an embodiment of the present invention; 
     FIG. 3 is a flow diagram describing the generation of an energy spectrum according to an embodiment of the present invention; 
     FIG. 4 shows an energy spectrum according to an embodiment of the present invention; 
     FIG. 5 shows an energy spectrum with selected peaks marked according to an embodiment of the present invention; 
     FIG. 6 shows a peak array according to an embodiment of the present invention; 
     FIG. 7 shows a flow diagram describing the generation of an interval array according to an embodiment of the present invention; 
     FIG. 8 shows an interval array according to an embodiment of the present invention; 
     FIG. 9 is a flow diagram describing the computation of a quantized interval array according to an embodiment of the present invention, 
     FIG. 10 shows a quantized interval array according to an embodiment of the present invention; 
     FIG. 11 is a flow diagram describing the peak folding according to an embodiment of the present invention; 
     FIG. 12 shows a signature vector according to an embodiment of the present invention; 
     FIG. 13 is a flow diagram describing the computation of a distance between two audio signatures according to an embodiment of the present invention; 
     FIG. 14 shows a signature generator and a signature analyzer according to an embodiment of the present invention; and 
     FIG. 15 shows a system for comparing audio files against a set of reference audio files according to an embodiment of the present invention. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In FIG. 1, a flow diagram describing the method of generation of an audio signature according to an embodiment of the present invention is shown. In a preferred embodiment of the present invention, the procedure of generating an audio signature according to the present invention (steps  101  to  105  in FIG. 1) is carried out in a signature generator  1401  as illustrated in FIG.  14 . Referring to FIGS. 1 and 14, a single channel digital audio signal  100  in WAVE data format may be used as an input to the signature generator  1401  and the method of FIG.  1 . The terms ‘monophonic’ audio or ‘mono’ audio are used to describe such single channel audio data. If the audio signal  100  is available in another input format, it should to be converted into the mono WAVE format prior to step  101 . However, the present invention can be adapted for any arbitrary input format, e.g., mono AIFF (Audio Interchange File Format). 
     Operating on monophonic files is no restriction but is a matter of convenience. For multi-track recordings, like stereophonic or multi-track studio masters, the described method can easily be used to compute an audio signature for each individual channel. If an audio signature for the multi-track recording is needed or desired, the audio signatures for individual channels are preferably combined into a signature vector wherein each element i of the vector corresponds to the signature of track i. 
     In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, comprises an input module  1402 , through which the audio signals  100  are fed into or retrieved by the signature generator  1401 . In a preferred embodiment, analog audio material is digitized prior to step  101  of FIG. 1 by appropriate means. 
     In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, further comprises a series expansion module  1403 , wherein the series expansion of the audio signal  100  (step  101 ) is carried out. In step  101 , the coefficients a i  of the series expansion of the audio signal  100  are calculated with respect to a complete set of elementary signals {φ i }. Thereby the following formula is used, wherein the audio signal  100  is denominated x, and the index i denominates the number of elementary signals φ i .        x   =       ∑   i            α   i          ϕ   i                                
     A set of elementary signals {φ i } is complete, if all signals x can be written as a linear combination of the elementary signals φ i . 
     If a set of elementary signals {φ i } is complete, there exists a dual set {φ′ i } such that the coefficients a i  can be computed as the inner product of the signal x with the dual set {φ′ i }:          α   i     =         ∑   n          ϕ   i   ′                  =       C   [   n   ]          x        [   n   ]                                  
     wherein the index i identifies the elementary signal φ i  and the index n runs through all data points of the audio signal x. 
     In cases where the elementary signals φ i  are localized in time (e.g., whenever the signal x is segmented into smaller blocks for processing or the elementary signals φ i  are only defined for a finite time interval), a local series expansion is computed for each block of data or each support interval of the elementary signals φ i . These local series expansions are accumulated in a vector A of series expansions where each element of A comprises the coefficients a i  of the series expansion for a single window. 
     Those skilled in the art will not fail to realize that a great variety of sets of complete elementary signals exist that can be used to calculate a series expansion. These include, but are by no means restricted to, the sets of elementary signals used in Fourier, wavelet, or bilinear transformations. 
     In the preferred embodiment of the present invention, the monophonic input audio signal  100  is segmented into blocks of preferably one second duration, and, for each block, a discrete Fourier transformation is computed (step  101  in FIG.  1 ). FIG. 2 illustrates this process according to an embodiment of the present invention. Each block of the audio signal  100  is retrieved in step  201  and it is checked whether all audio data is processed (e.g., whether the block includes audio data or not) in step  202 . If all audio data is processed the process exits in step  203  (and continues with step  102  in FIG.  1 ). Otherwise, the process continues with step  204 , wherein it is checked whether or not the block is completely filled with audio data. If the block is a truncated block, the empty space is filled with the data content ‘0’ in step  205  and the process continues with step  206 . If the block is not truncated, a Fourier transformation is carried out in step  206 . The resulting coefficients a i  of the series expansion are assembled into the vector A and stored into a memory and/or directly forwarded in step  207  for use in step  102 , as described below. The process returns to step  201  where the next block of the audio signal  100  is retrieved. 
     The discrete Fourier transformation, as carried out in step  20 , 6  can be interpreted as a series expansion of the audio signal  100  with respect to the set of elementary signals {φ i }: 
     
       
         {φ i }=exp(−2 iπkn/N ) 
       
     
     For a signal with N data points the coefficients a i  of the series expansion can be computed as:          α   k     =       ∑     n   =   0       N   -   1              x        [   n   ]                     exp                   (     2      i                 π                   kn   /   N       )                                
     wherein the index n runs through all data points and the index k identifies the elementary signal φ i . 
     In a preferred embodiment, the signature generator  1401  as illustrated in FIG. 14 further comprises a spectrum module  1404 , wherein the calculation of a spectrum S is carried out (step  102  in FIG.  1 ). 
     Preferably, the energy spectrum S is calculated from the coefficients a i  of the series expansion that result from step  101 . The energy spectrum S is defined as: 
     
       
           S   i   =|a   i | 2   
       
     
     In cases where the series expansion results in a vector A, a partial spectrum is computed for each element of this vector and the partial spectra are averaged to obtain the energy spectrum S. 
     In a preferred embodiment wherein a Fourier transformation is used to calculate the series expansion, the energy spectrum S generated in step  102  is usually known as power spectrum. In step  102 , for each block of the audio input  100 , the energy spectrum is calculated from the Fourier transformation and the spectra from all blocks are averaged. 
     FIG. 3 illustrates the process of step  102  according to an embodiment of the present invention. It is assumed that the results of step  101  have been previously stored (in step  207 ). In a first step  301  a counter n, a power spectral density vector (PSD) is created and initialized (=set to ‘0’). In step  302 , the coefficients a i  of the series expansion for a first block resulting from step  101  are retrieved. In step  103 , it is checked whether the coefficients a i  of all local series expansions have been processed. If they are not yet completely processed, the energy spectrum of that block is computed in step  304 . This spectrum is added to the PSD vector and the counter n is incremented by a value of ‘1’ in step  305 . The process continues with returning to step  302 , retrieving the coefficients a i  of the series expansion of the next block. If all blocks have been processed, the process continues with step  306 , subsequent to step  303 . In step  306 , the PSD vector (sum of all spectra) is divided by n to produce an average energy spectrum. The process of generating the energy spectrum exits with step  307 , wherein the spectrum is stored and/or forwarded for use in the peak selection step  103  as described below. 
     An example of a resulting energy spectrum  400  according to an embodiment of the present invention is shown in FIG.  4 . The power of the audio signal  100  is plotted against a logarithmic frequency scale labeled using the standard musicological notation for frequencies with ‘C4’ corresponding to 261.63 Hz. 
     In a further embodiment of the present invention, the signature generator  1401  additionally comprises a format check module (not shown in FIG.  14 ), preferably connected between the input module  1402  and the series expansion module  1403 . In the format check module, it is determined whether the audio signal  100  is already encoded as a series expansion. The format check module can be designed for carrying out any suitable method as known in the art, e.g., parsing of ‘magic’ strings in the header of the audio data  100 . In most cases, where the audio signal  100  is already encoded as a usable series expansion, it is then preferably directly fed into or retrieved by the spectrum module  1404  without undergoing a series expansion. This could be performed by bypassing the series expansion module  1403 . 
     In a further embodiment of the present invention, the energy spectrum is calculated whenever appropriate by other methods such as autoregressive spectral estimation, minimum variance spectral estimation, or Prony&#39;s method. Then the energy spectrum can be generated without carrying out a previous series expansion of the input data. 
     In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, further comprises a peak selection module  1405 , wherein the peak selection (step  103  in FIG. 1) is carried out. In step  103 , the n most prominent local peaks are selected from the spectrum S as resulting from step  102 . In a preferred embodiment of the present invention, the twenty-five most prominent local peaks are selected from the energy spectrum  400 . FIG. 5 shows the marked energy spectrum  500  according to an embodiment of the present invention. The marked energy spectrum  500  corresponds to the energy spectrum  400  as shown in FIG. 4, with the twenty-five largest peaks marked with dots. 
     Furthermore, a peak array is generated in step  103 . Such a peak array (PA)  600  according to an embodiment of the present invention is shown in FIG.  6 . The peak array  600  results from the marked energy spectrum  500  and contains the location frequency  601  of the n peaks in the first column and their magnitudes  602  in the second column. The peak array  600  is stored in a memory and/or forwarded directly to be used in peak quantization. 
     In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, further comprises a peak quantization module  1406 , wherein the peak quantization (step  104  in FIG. 1) is carried out. In the first part of step  104 , the frequency coordinates  601  of the n peaks are transformed to an interval scale. FIG. 8 shows a resulting interval array  800  according to an embodiment of the present invention. The interval array  800  contains the interval scale  801  of the n peaks in the first column and their magnitudes  602  in the second column. Thereby the intervals are expressed in cents (an octave is divided into twelve-hundred cents). For carrying out the transformation, the following general formula may be used: 
     
       
         ƒ′= a (log base (ƒ)−log base (ƒ max )) 
       
     
     In the above formula, ƒ′ denominates the result of the transformation, whereas the frequencies are named ƒ. Any logarithmic scale may be used. The multiplicand a is introduced to obtain results in an appropriate user friendly numeric range. 
     In a preferred embodiment, the frequency column  601  of the peak array  600  is transformed into a musicological interval scale  801  with the frequency of the strongest partial of the energy spectrum as zero point. The interval array computation according to an embodiment of the present invention is described in FIG.  7 . 
     In a first step  701 , the peak array  600  is retrieved. In step  702 , an interval array (IA)  800  is created, e.g., by duplicating the peak array  600 . Next a counter i is set to the value ‘1’ and the first frequency entry of the IA array  800  is set to ‘0’ in step  703 . 
     In step  704 , the frequencies  601  are transformed to intervals  801  relative to the most prominent peak using the formula 
     
       
           IA[i ,0]=α(log 10 ( PA[i, 0])−log 10 ( PA[ 0,0])) 
       
     
     wherein the interval array IA and the peak array PA are indexed in the following way: the first index specifies the array&#39;s row (starting with row zero) and the second index specifies the array&#39;s column (starting with column zero). 
     The multiplicand a is the appropriate conversion factor to express the intervals in cents. Thereby, a is preferably set to 3986.31371392. 
     This process is repeated until the complete interval scale  801  has been computed, for which reason the counter i is incremented by the value of ‘1’ in step  704 . If the complete interval scale  801  has been computed (i=n), see step  705 , the process of generating the interval scale exits with step  706 . The resulting interval array  800  comprising the interval scale  801  is stored and/or forwarded directly to the quantization part of step  104  as described below. 
     In the second part of step  104 , the values of the interval scale  801  are quantized by rounding them to the nearest value of an appropriate scale. For instance, for classical European music, a well-tempered scale is the most natural choice. Those skilled in the art will not fail to realize that, depending on the type and cultural background of the content to be described, a variety of different scale functions, including identity, may be used. 
     A quantized interval array according to an embodiment of the present invention and as derived from step  104  is shown in FIG.  10 . The quantized interval array  1000  contains the quantized interval scale  1001  of the n peaks in the first column and their magnitudes  602  in the second column. 
     The procedure of quantization according to an embodiment of the present invention is shown in FIG.  9 . In a first step  901 , the interval array  800  as generated during the first part of step  104  is retrieved. In the following step  902 , a quantized interval array (QIA) is created, e.g. by duplicating the interval array  800 . A counter i is initialized in step  903 . In step  904 , the quantized interval scale  1001  is calculated by 
     
       
           QIA[i, 0]=round ( IA[i, 0]/100)*100 
       
     
     wherein the quantized interval array QIA is indexed in the following way: the first index specifies the array&#39;s row (starting with row zero) and the second index specifies the array&#39;s column (starting with column zero). The counter i is incremented by the value of ‘1’. 
     This process is repeated until the complete quantized interval scale  1001  has been computed (i=n), see step  905 , in which case the process of generating the quantized interval array exits with step  906 . The quantized interval array is stored and/or forwarded for use in the peak folding step  105  as described below. 
     In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, further comprises a peak folding module  1407 , wherein the peak folding (step  105  in FIG. 1) is carried out. In step  105 , an equivalence transformation is applied to the quantized interval scale  1001  of the peaks and the power values  602  for all equivalent frequency values are added at the location of the representative element creating cumulated power values. An example for such an equivalence relationship is the octave relationship (frequency doubling) between frequencies. It should be noted that amongst a variety of other equivalence relations, the identity relation which maps each frequency onto itself may be used as well. The resulting signature  106 , which is used for unambiguously characterizing the input audio data  100 , is the vector of cumulated power values for all representative elements sorted in an arbitrary, but fixed way, e.g. in increasing coordinates of the representative elements and normalized to a sum of ‘1’. 
     In the described embodiment, the power values  602  of all intervals that are an octave apart is added and the resulting cumulated power value is assigned to the smallest equivalent interval above the strongest partial of the energy spectrum (the zero point of the interval scale  1001 ). 
     The procedure of peak folding, according to an embodiment of the present invention, is shown in FIG.  11 . In a first step  1101 , the quantized interval array  1000  is retrieved. In the following step  1102 , a signature vector comprising twelve elements (if using a well-tempered scale) is created and a counter i, corresponding to the rows of the quantized interval array  1000 , is initialized. In the next step  1103 , the representative element (‘index’) for an octave equivalence relation is calculated by 
     
       
         index= QIA[i, 0]/100 mod 12 
       
     
     and the power value (QIA[i,1]) is added to the signature value of the representative element 
     
       
         sig[index]=sig[index]+ QIA[i, 1] 
       
     
     and the counter i is incremented by the value of ‘1’. This process is repeated until the complete signature has been computed (i=n), see step  1104 . The process continues with step  1105 , wherein the normalization of the signature takes place. Therein the signature is normalized to a sum of ‘1’. 
     
       
         sig=sig/sum(sig) 
       
     
     Subsequently, the process exits with step  1106 , at which time a normalized signature vector is generated. This signature vector is the audio signature  106 . In a preferred embodiment, the signature generator  1401 , as illustrated in FIG. 14, further comprises an output module  1408 , through which the generated audio signature  106  is output. 
     FIG. 12 shows the resulting audio signature vector  106  comprising n=12 elements according to an embodiment of the invention. 
     With the procedure illustrated in FIG. 1, a characteristic audio signature  106  of audio signal  100  is generated. In order to determine whether two input audio signals are identical, the audio signature  106  for each audio signal  100  are computed as described above and compared with each other. 
     Two audio signatures  106  can be compared using any method appropriate for the comparison of vectors. A decision rule with a separation value D depending on the chosen method is used to distinguish identical from non-identical audio data. 
     In a preferred embodiment of the invention, the method used to compare two signatures is a variant of the Kolomogorov-Smimov statistic and computes the maximal distance between the two cumulated signatures (Cum 1 , Cum 2 ). Signatures where the maximal distance is larger than the separation value D are judged to be different. This separation value D has to be determined empirically and may be set manually or automatically as parameter of a signature analyzer. 
     In a preferred embodiment of the present invention, the analysis of audio signatures  106 , performed in order to determine whether sets of audio data (e.g., two audio files) are identical, is carried out in a signature analyzer  1410 , as illustrated in FIG.  14 . In a preferred embodiment, the signature analyzer  1410  comprises an input module  1411 , through which audio signatures  106  are fed into or retrieved by the signature analyzer  1410 . Signature Analyzer  1401  also comprises a computing and evaluating module  1412 , wherein the distance computing and evaluating (steps  1301 - 1309  in FIG. 13) are carried out. 
     The procedure of calculating the distance between the two signatures, according to an embodiment of the present invention, is shown in FIG.  13 . In a first step  1301 , the two audio signatures  106  to be compared are retrieved. In the following, these signatures are denominated sig 1  and sig 2 . 
     In step  1302 , the vectors (cum 1 , cum 2 ) of the cumulated signatures are created. Thereby, the length of the cumulated signatures (cum 1 , cum 2 ) equals the length of the signatures (sig.  1 , sig.  2 ). 
     In the following step  1303 , the first element of each of the cumulated signature vectors (cum 1 , cum 2 ) is set to the first element of the according signature (sig 1 [0], sig 2 [0]), and a counter i representing the number of elements of the vectors (cum 1 , cum 2 ) is set to the value ‘1’. In step  1304 , the cumulation signature vectors (cum 1 , cum 2 ) are calculated by 
     
       
         cum 1 [ i ]=cum 1 [ i− 1]+sig 1   [i]   
       
     
     
       
         cum 2 [ i ]=cum 2 [ i− 1]+sig 2   [i]   
       
     
     and the counter i is incremented by the value of ‘1’. This process is repeated until all elements of the cumulated vectors (i=12) have been completely processed, see step  1305 . 
     Next the process continues with step  1306  wherein the maximal distance (MD) between the cumulated vectors (cum 1 , cum 2 ) is computed. 
     
       
         MD=max( abs (cum 1 −cum 2 )) 
       
     
     The process continues with step  1307 , wherein the maximal distance MD is used to compare the two audio signals  100 . In a preferred embodiment, the maximal distance MD is compared with the separation value D, such as 0.05, in order to determine whether the two audio signals  100  are equal (the analyzing process ends with step  1308 ) or different (the process ends with step  1309 ). Depending on whether the two audio signals  100  are equal or not, further procedures may be employed subsequent to step  1308  and/or step  1309  respectively (not shown in FIG.  13 ). For instance, a report showing the result of the signature analysis may be generated and output to an external device such as a monitor or printer, stored in a report file, and/or transferred to a database. In a preferred embodiment, the signature analyzer  1410  as illustrated in FIG. 14 further comprises an output module  1413 , through which the result  1414  of the signature analysis is output. 
     It will be understood and appreciated by those skilled in the art that the inventive concepts set forth in the embodiments described in this application and pertaining to the provision of detecting equal audio data, like audio recordings, may be embodied in a variety of system contexts. 
     For example, the above described procedure according to the present invention may be used in a system to compare a series of audio files against a set of reference audio in order to find recordings in the set of audio under proof that are part of the set of reference audio. An example of such an application is a system that automatically scans a computer network, e.g. the Internet, for audio files, and compares them against a set of audio of a specific recording company, for instance, and find identical audio data according to the method of the present invention. This would help to find copyright infringements. 
     The structure of such a system  1500  according to an embodiment of the present invention is shown in FIG.  15 . In FIG. 15, the input of the reference information comprises the reference audio file  1501  and descriptive information, the reference metadata  1502 . Sources of this information can be sources like physical recordings (e.g., compact disks or master tapes) or computer systems that store the information. The metadata  1502  is an identifier of the audio file such as a catalogue number. Optionally, the metadata  1502  includes other identifying information like title or artist. 
     A format converter  1503  transforms the original reference audio file  1501  into a formatted reference audio file  1504 , which is in a format that is supported by the signature generator  1505  and appropriate for the signature comparison later in the process. If the input format already fulfills these requirements, the conversion can be omitted or reduced to an identity transformation. 
     The signature generator  1505  (preferably an implementation of the signature generator  1401  as described above, carrying out the steps  101  to  105  of this invention) uses the reference audio file  1504  as input file and generates an audio signature  1506  as specified in the above description. 
     This signature  1506  of reference audio is stored in a database (signature database)  1507  together with the reference metadata  1502 . 
     The input of the information under proof comprises the audio file  1508  under proof and descriptive information (the metadata  1509  of the audio file under proof). Sources of this information can be physical recordings like CDs, files on computer systems or files that result from a (preferably automatic) scanning of storage systems like computer networks  1516  (e.g. the Internet) by an according scanning device  1517 . The metadata  1509  is a characteristic identifier of the audio file, typically the source address (location) of the audio file (e.g., an Internet URL). Additional information might be included if available. 
     A format converter  1510  transforms the audio file  1508  under proof into a formatted audio file  1511 , which is in a format that is supported by the signature generator  1512  and appropriate for the signature comparison later in the process. Again, if the input format already fulfills these requirements, the conversion can be omitted or reduced to an identity transformation. 
     The signature generator  1512  works preferably according to the procedure described above and can be physically identical with the signature generator  1505 . The signature generator  1512  uses the formatted audio file  1511  under proof as input file and generates an audio signature  1513  as specified in this invention. 
     This audio signature  1513  of the audio  1508  under proof is preferably stored in the database (signature database)  1507 , together with the reference metadata  1502 . 
     In a preferred embodiment, after filling the signature database  1507  with the sets of signatures  1506  of reference audio and signatures  1513  of audio under proof, the report generator  1514  scans the signature database  1507  and generates a report  1515 . The report generator  1514  preferably comprises an implementation of the signature analyzer  1410  as described above. The report  1515  preferably comprises a list of all reference audio files  1501  and audio files  1508  under proof that have the same signature. Preferably the metadata  1502 ,  1509  for each listed audio file  1501 ,  1508  are included in the report  1515 . 
     A system as described above may take all audio of an audio owning company (e.g., record company) as reference input. Furthermore, it may carry out an Internet scanning process (e.g., by a network scanner) to collect audio files  1508  to be examined and then generate a report  1515  of all files found on the Internet that have an identical signature to a signature made from a reference audio. The report  1515  may than be used to check the Internet files  1508  for copyright infringement. This whole process is preferably carried out automatically. 
     A similar system can be used to establish a service that takes the audio of several audio owning companies as references, scan the Internet and generates reports for each company to check copyright infringements. 
     In the above description, the invention has been described with regard to digital audio signals. However, the present invention is by no means restricted to audio signals. Other digital data may be used as well. 
     The present invention can be realized in hardware, software, or a combination of hardware and software. The signature generator  1401  and/or the signature analyzer  1410 , as well as the modules used in the system  1500  for comparing a series of audio files against a set of reference audios, can be realized in a centralized fashion in one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system—or other apparatus adapted for carrying out the methods described herein—is suitable. A typical combination of hardware and software could be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein. The present invention can also be embedded in a computer program product, which comprises all the features enabling the implementation of the methods described herein, and which—when loaded in a computer system—is able to carry out these methods. Computer program means or computer program in the present context mean any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following a) conversion to another language, code or notation; b) reproduction in a different material form. 
     It is to be understood that the embodiments and variations shown and described herein are merely illustrative of the principles of this invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention.