Abstract:
Various systems and methods for comparing media signals are disclosed. In some embodiments, media signals are compared by identifying characteristic features in the respective signals and then analyzing the characteristic features to determine if the signals contain corresponding content. In other embodiments, the characteristic features are analyzed to determine the extent to which the signals are synchronized in time.

Description:
FIELD 
     The described embodiments relate to systems and methods for comparing media signals. The media signals may be video signal, audio signals, video/audio signals or the like. More particularly, the described embodiments relate to systems and methods for comparing media signals by extracting one or more characteristic features from the media signals to produce extracted feature data and comparing the extracted feature data. 
     BACKGROUND 
     In many broadcast systems and other communication systems, it is desirable to switch from one version or instance of a media signal or stream to another version or instance of the media stream or signal. For example, a broadcast facility may produce a primary version and a secondary version of an audio/video signal. The primary signal may be broadcast on a particular channel. If the primary signal becomes unavailable, it may be desirable to broadcast the secondary signal on the channel. When switching the source for the channel from the primary to the secondary signal, it can be desirable to ensure that the primary and secondary signals are synchronized in time such that the transmission on the channel remains synchronized in content. 
     Many broadcast facilities receive, generate and transmit a large number of signals. When intending to make a switch from one version of a signal to another version of a signal it is possible to inadvertently switch to an unrelated signal resulting in an undesirable transition on a channel from one program to another program. 
     In some cases, two versions of a signal in a broadcast system may be out of synchronization such that one of the signals is running ahead of the other. When a switch is made from one version of the signal to another version of the signal, it is possible that a portion of the media signal will be presented twice, or a portion of the media signal may be skipped altogether. 
     Accordingly, there is a need for systems and methods for assessing the synchronization of two media streams and for identifying whether two streams contain corresponding content. 
     SUMMARY 
     The embodiments described herein provide in one aspect a method of comparing media signals comprising: receiving a first input media signal and a second input media signal; extracting a first characteristic feature from the first input media signal to generate a first feature signal; extracting a second characteristic feature from the second input media signal to generate a second feature signal; and providing a match confidence signal based on the first and second feature signals, wherein the match confidence signal is an estimate of the likelihood that that the first input media signal corresponds to the second input media signal. 
     In one feature of that aspect, providing the match confidence signal includes: sampling the first feature signal to produce a first sampled feature signal; sampling the second feature signal to produce a second sampled feature signal; cross-correlating the first and second sampled feature signals to generate a cross-correlation signal; and modifying the match confidence signal based on the cross-correlation signal. 
     In another feature of that aspect, the match confidence signal has a value between a high match value and a low match value and wherein modifying the match confidence signal includes: analyzing the cross-correlation signal to identify a peak exceeding a matching peak threshold; if a peak exceeding the matching peak threshold is identified, then modifying the match confidence signal to be closer to the high match value; and if a peak exceeding the matching peak threshold is not identified, then modifying the match confidence signal to be closer to the low match value. 
     In another feature of that aspect, the match confidence signal has a value between a high match value and a low match value and wherein providing the match confidence signal includes: analyzing the cross-correlation signal to identify a current peak position within the cross-correlation signal; if the current peak position corresponds to one or more previous peak positions, then modifying the match confidence signal to be closer to the high match value; and if the current peak position does not correspond to the one or more previous peak positions, then modifying the match confidence signal to be closer to the low match value. 
     In another feature of that aspect, the method further includes: comparing the first feature signal to the second feature signal; and modifying the match confidence signal based on the results of the comparison. 
     In another feature of that aspect, comparing the first feature signal to the second feature signal includes: identifying a portion of the first feature signal; identifying a portion of the second feature signal corresponding to the identified portion of the first feature signal; and comparing the identified portions of the first and second feature signals. 
     In another feature of that aspect, identifying the portion of the second feature signal corresponding to the identified portion of the first feature signal includes: analyzing the cross-correlation signal to identify a current peak position within the cross-correlation signal; converting the current peak position into a delay value, wherein the delay value represents the time delay between the first and second input media signals; and identifying the portion of the second feature signal based on the delay value. 
     In another feature of that aspect, providing the match confidence signal includes: sampling the first feature signal to produce a first sampled feature signal; sampling the second feature signal to produce a second sampled feature signal; cross-correlating a first portion of the first sampled feature signal and a first portion of the second sampled feature signal to generate a first cross-correlation signal; identifying a second portion of the first sampled feature and a second portion of the second sampled feature, wherein the second portions of the first and second sampled feature signals are smaller than the first portions of the first and second sampled feature signals respectively; cross-correlating the second portion of the first sampled feature signal and the second portion of the second sampled feature signal to generate a second cross-correlation signal; and modifying the match confidence signal based on the first and second cross-correlation signals. 
     In another feature of that aspect, identifying the second portion of the first sampled feature and the second portion of the second sampled feature includes: analyzing the first cross-correlation signal to identify a current peak position within the first cross-correlation signal; converting the current peak position into a delay value, wherein the delay value represents the time delay between the first and second input media signals; and identifying the second portions of the first and second feature signals based on the delay value. 
     In another feature of that aspect, the match confidence signal is provided as a series of discrete values. In another feature of that aspect, the match confidence signal is provided as an analog signal. 
     In another feature of that aspect, first characteristic feature includes at least one characteristic selected from the group consisting of: average luma value, average color value, average motion distance, and contrast level. In another feature of that aspect, the first characteristic feature includes at least one characteristic selected from the group consisting of: envelope of signal amplitude, average loudness level, peak formant, and average zero crossing rate. 
     The embodiments described herein provide in another aspect a system for comparing media signals comprising: a first input port for receiving a first input media signal; a second input port for receiving a second input media signal; a first extraction module for extracting a first characteristic feature from the first input media signal to generate a first feature signal; a second extraction module for extracting a second characteristic feature from the second input media signal to generate a second feature signal; and a match confidence signal generator for providing a match confidence signal based on the first and second feature signals, wherein the match confidence signal is an estimate of the likelihood that that the first input media signal corresponds to the second input media signal. 
     In another feature of that aspect, the first extraction module comprises: a first feature extractor for extracting the first characteristic feature from the first input media signal to generate the first feature signal; and a first sampling module for sampling the first feature signal to produce a first sampled feature signal; the second extraction module comprises: a second feature extractor for extracting the second characteristic feature from the second input media signal to generate the second feature signal; and a second sampling module for sampling the second feature signal to produce a second sampled feature signal; and the match confidence signal generator comprises: a cross correlation module for cross-correlating the first and second sampled feature signals to generate a cross-correlation signal; and a strength and consistency analyzer for modifying the match confidence signal based on the cross-correlation signal. 
     In another feature of that aspect, the match confidence signal has a value between a high match value and a low match value and wherein the strength and consistency analyzer comprises: a peak locator for analyzing the cross-correlation signal to identify a current peak value within the cross-correlation signal; and a match confidence signal adjustment module for: if the current peak value exceeds a matching peak threshold, then modifying the match confidence signal to be closer to the high match value; and if the current peak value does not exceed the matching peak threshold, then modifying the match confidence signal to be closer to the low match value. 
     In another feature of that aspect, the match confidence signal has a value between a high match value and a low match value and wherein the strength and consistency analyzer comprises: a peak locator for analyzing the cross-correlation signal to identify a current peak position within the cross-correlation signal; and a match confidence signal adjustment module for: if the current peak position corresponds to one or more previous peak positions, then modifying the match confidence signal to be closer to the high match value; and if the current peak position does not correspond to the one or more previous peak positions, then modifying the match confidence signal to be closer to the low match value. 
     In another feature of that aspect, the match confidence signal generator further comprises a small window analyzer for: comparing the first feature signal to the second feature signal; and modifying the match confidence signal based on the results of the comparison. 
     In another feature of that aspect, comparing the first feature signal to the second feature signal includes: identifying a portion of the first feature signal; identifying a portion of the second feature signal corresponding to the identified portion of the first feature signal; and comparing the identified portions of the first and second feature signals. 
     In another feature of that aspect, identifying the portion of the second feature signal corresponding to the identified portion of the first feature signal includes: analyzing the cross-correlation signal to identify a current peak position within the cross-correlation signal; converting the current peak position into a delay value, wherein the delay value represents the time delay between the first and second input media signals; and identifying the portion of the second feature signal based on the delay value. 
     In another feature of that aspect, the first extraction module comprises: a first feature extractor for extracting the first characteristic feature from the first input media signal to generate the first feature signal; and a first sampling module for sampling the first feature signal to produce a first sampled feature signal; the second extraction module comprises: a second feature extractor for extracting the second characteristic feature from the second input media signal to generate the second feature signal; and a second sampling module for sampling the second feature signal to produce a second sampled feature signal; and the match confidence signal generator comprises: a cross correlation module for cross-correlating a first portion of the first sampled feature signal and a first portion of the second sampled feature signal to generate a first cross-correlation signal; a second cross correlation module for cross-correlating a second portion of the first sampled feature signal and a second portion of the second sampled feature signal to generate a second cross-correlation signal, wherein the second portions of the first and second sampled feature signals are smaller than the first portions of the first and second sampled feature signals respectively; and at least one strength and consistency analyzer for modifying the match confidence signal based on the first and second cross-correlation signals. 
     In another feature of that aspect, the second cross correlation module is adapted to identify the second portions of the first and second sampled feature signals, wherein identifying the second portions of the first and second sampled feature signals includes: analyzing the first cross-correlation signal to identify a current peak position within the first cross-correlation signal; converting the current peak position into a delay value, wherein the delay value represents the time delay between the first and second input media signals; and identifying the second portions of the first and second feature signals based on the delay value. 
     In another feature of that aspect, the match confidence signal is provided as a series of discrete values. In another feature of that aspect, the match confidence signal is provided as an analog signal. 
     In another feature of that aspect, wherein the first characteristic feature includes at least one characteristic selected from the group consisting of: average luma value, average color value, average motion distance, and contrast level. In another feature of that aspect, the first characteristic feature includes at least one characteristic selected from the group consisting of: envelope of signal amplitude, average loudness level, peak formant, and average zero crossing rate. 
     Further aspects and advantages of the embodiments described will appear from the following description taken together with the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a better understanding of embodiments of the systems and methods described herein, and to show more clearly how they may be carried into effect, reference will be made, by way of example, to the accompanying drawings in which: 
         FIG. 1  is a block diagram of a system for determining the extent to which two media signals are out of sync with each other in accordance with at least one embodiment; 
         FIG. 2  is a block diagram of the feature extraction module of  FIG. 1  in accordance with one embodiment; 
         FIG. 3  is a block diagram of the feature extraction module of  FIG. 1  in accordance with another embodiment; 
         FIG. 4  is a chart illustrating a method of determining the delay between two signals using a simple sliding technique; 
         FIG. 5  is a block diagram of the delay calculation module of  FIG. 1  in accordance with at least one embodiment; 
         FIG. 6  is a chart illustrating a method of determining the peak position using linear interpolation in accordance with an embodiment; 
         FIG. 7  is a block diagram of a system for determining the delay between media signals in accordance with an embodiment; 
         FIG. 8  is a block diagram a system for determining the likelihood that two media signals match in accordance with a first embodiment; 
         FIG. 9  is a block diagram of the strength and consistency analyzer of  FIG. 8  in accordance with an embodiment; 
         FIG. 10  is a block diagram of a system for determining the likelihood that two media signals match in accordance with a second embodiment; 
         FIG. 11  is a chart illustrating exemplary first and second media signals as a function of time; and 
         FIG. 12  is a block diagram of a system for determining the likelihood that two media signals match in accordance with a third embodiment. 
     
    
    
     It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. 
     DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     It will be appreciated that numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the embodiments described herein. Furthermore, this description is not to be considered as limiting the scope of the embodiments described herein in any way, but rather as merely describing the implementation of the various exemplary embodiments described herein. 
     Embodiments described herein relate to methods and systems for comparing two or more media signals. The media signals may be video signals, audio signals, video/audio signals or the like. The methods and systems involve extracting one or more characteristic features from the media signals to produce extracted feature data for each media signal, and then comparing the extracted feature data. In some embodiments, the extracted feature data may be used to determine the synchronization error between the media signals. In other embodiments, the extracted feature data may be used to determine the delay between the media signals. In still other embodiments, the extracted feature data may be used to determine the likelihood that the media signals match. Two media signals are said to match if they represent the same content. For example, a high quality video of a movie and a DVD version of the same movie are said to match. 
     The systems described herein may be implemented in hardware or software, or a combination of both. However, preferably, at least part of the system is implemented in computer programs executing on programmable computers or other processing devices, including programmable, application specific, embedded and other devices. For example, a processing device may typically comprise a processor, a data storage system, at least one input device, and at least one output device. For example and without limitation, the programmable computers may be a personal computer or laptop, logic arrays such as a programmable logic array (PLA), gate arrays such a floating point gate array (FPGA), a suitable configured circuit, such as integrated circuit or an application specific integrated circuit (ASIC). Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices, in known fashion. 
     Each program is preferably implemented in a high level procedural or object oriented programming and/or scripting language to communicate with a computer system. However, the programs can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Each such computer program is preferably stored on a storage media or a device (e.g. ROM or magnetic diskette) readable by a general or special purpose programmable computer, for configuring and operating the computer when the storage media or device is read by the computer to perform the procedures described herein. The inventive system may also be considered to be implemented as a computer-readable storage medium, configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner to perform the functions described herein. 
     Furthermore, the system is capable of being distributed in a computer program product comprising a physical computer readable medium that bears computer usable instructions for one or more processors. The medium may be provided in various forms, including one or more diskettes, compact disks, tapes, chips, magnetic and electronic storage media, and the like. The computer useable instructions may also be in various forms, including compiled and non-compiled code. 
     Reference is now made to  FIG. 1 , in which a system  100  for determining the extent to which two media signals are out of sync with each other in accordance with an embodiment is illustrated. The system  100  includes four feature extraction modules  102   a ,  102   b ,  102   c  and  102   d , a signal transport network  104 , two delay calculation modules  106   a  and  106   b  and a synchronization error module  108 . 
     Two input media signals  110  and  112  are input into the system  100  at input terminals  114  and  116 . Typically, the input media signals  110  and  112  are reproduced continuously and are synchronized such that corresponding portions of each signal are reproduced at about the same time. Each of the input terminals  114  and  116  is coupled to a feature extraction module  102   a ,  102   b , and also to the signal transport network  104 . The input media signals  110  and  112  are transported through the signal transport network  104  and output as output media signals  118  and  120  respectively at output terminals  122  and  124 . 
     In this embodiment, the first and second input media signals  110  and  112  may be video signals, audio signals, video/audio signals or the like. For example, the first input media signal  110  may be a video signal and the second input media signal  112  may be an associated audio signal. Typically, the video signal and the audio signal are synchronized such that the audible contents of the audio signal are synchronized with the visual contents of the video signal. For example, the audio and video signals may be produced by an audio/video source such as a live video/audio capture module, a video tape player, a video server, a DVD player or a set-top television decoder. 
     The signal transport network  104  will typically include audio and video signal transportation devices which transport the input media signals  110  and  112  from one point to another. The signal transport network  104  may also include audio and video processing devices (i.e. a decoder, an MPEG compressor, a video standard converter) which modify the input media signals  110  and  112 . Where the signal transport network  104  includes processing devices, the output media signals  118 ,  120  may be different than the corresponding input media signals  110 ,  112 . For example, an MPEG compressor introduces compression artifacts in a video signal and a video standard converter changes the video size and/or frame rate of the video signal. Typically, the first and second input media signals  110  and  112  will travel through different transmission paths through the signal transport network  104 , although this is not necessary. 
     For example, where the first input media signal  110  is a video signal, it may travel through various devices including a composite decoder, an MPEG compressor, a transport stream multiplexer, a transport link, a transport stream de-multiplexer, an MPEG de-compressor or a composite encoder. The transport link may include an uplink modulator, a ground to satellite link, a satellite to ground link and a satellite receiver. Each of the processing units (i.e. the MPEG compressor, transport stream multiplexer) and the transport link will introduce a certain amount of delay so that the first output media signal  118  will be a delayed version of the first input media signal  110 . 
     Where the second input media signal  112  is an audio signal, it may travel the through an audio dynamic range processor, an audio compressor, a transport stream multiplexer, a transport link, a transport stream de-multiplexer and an audio de-compressor. Each of these processing units will also introduce delay so that the second output media signal  120  will be a delayed version of the second input media signal  112 . The delay in the first output media signal  118  will typically be different from the delay in the second output media signal  120 , with the result that the first and second output media signals  118  and  120  will not be synchronized when they reach the output terminals  122  and  124 . Processing elements in the network  104  may shift the audio signal relative to a reference element in the audio signal such that the audio generated by the audio signal appears to be advanced or delayed compared to the position of the reference element. 
     The feature extraction modules  102   a ,  102   b ,  102   c  and  102   d , the delay calculation modules  106   a ,  106   b  and the synchronization error module  108  operate to determine the extent to which the two output media signals  118  and  120  have become unsynchronized. Specifically, each of the feature extraction modules  102   a ,  102   b ,  102   c ,  102   d  extracts at least one characteristic feature of the input and output media signals  110 ,  112 ,  118  and  120  to produce a corresponding extracted feature signal  126   a ,  126   b ,  126   c  and  126   d . The delay calculation modules  106   a  and  106   b  determine the amount of delay between corresponding input and output signals (e.g.  110 ,  118 ;  112 ,  120 ) from the extracted characteristic feature signals  126   a ,  126   b ,  126   c  and  126   d , and output the delay as a delay signal  130   a  or  130   b . The synchronization error module  108  determines the difference between the two delay signals  130   a  and  130   b  and provides a synchronization error signal  132  corresponding to the difference. 
     The first feature extraction module  102   a  extracts one or more characteristic features of the first input media signal  110  and produces a first extracted feature signal  126   a . The second feature extraction module  102   b  extracts one or more characteristic features of the second input media signal  112  and produces a second extracted feature signal  126   b . The third feature extraction module  102   c  extracts one or more characteristic features of the first output media signal  118  and produces a third extracted feature signal  126   c . The fourth feature extraction module  102   d  extracts one or more characteristic features of the second output media signal  120  and produces a fourth extracted feature signal  126   d.    
     Reference is now made to  FIG. 2 , which is a block diagram of the first feature extraction module  102   a  in accordance with an embodiment. The first feature extraction module  102   a  shown in  FIG. 2  and described herein is intended to be an example of a feature extraction module and the principles and concepts described in relation to  FIG. 2  should not be limited to the first feature extraction module  102   a . Specifically, any or all of the feature extraction modules  102   a ,  102   b ,  102   c  and  102   d  of  FIG. 1  may be implemented in a similar manner to the feature extraction module  102   a  shown in  FIG. 2 . 
     The first feature extraction module  102   a  shown in  FIG. 2  includes a feature extractor  202 , a sampling module  204  and a storage module  206 . 
     The first feature extractor  102   a  receives the first input media signal  110  and extracts one or more characteristic features from the media signal  110  and outputs a feature signal  208 . Depending on the characteristic feature used, the feature signal  208  may be a continuous time varying signal or a set of discrete values. 
     A characteristic feature of a media signal is a feature that varies over time. Various aspects of a media signal may be used as characteristic features and aspects that have a pattern that is not easily varied or corrupted by the processing in the network  104  are preferred. Where the first input media signal  110  is an audio signal, one or more of the following may be used as a characteristic features: the envelope of audio signal amplitude, the average loudness level, the peak formant of the audio signal and the average zero crossing rate. Where the first input media signal  110  is a video signal, one or more of the following may be used as a characteristic features: the average luma or color value, the average motion distance, and the contrast level of the signal. Other aspects of the audio and video signals could also be used as a characteristic feature. 
     The sampling module  204  receives the feature signal  208  from the feature extractor  202 , samples it at a predetermined sampling frequency, f s , and outputs a sampled feature signal  210 . As noted above, in system  100  there are four feature extraction modules  102   a ,  102   b ,  102   c , and  102   d —one for each of the input and output media signals  110 ,  112 ,  118  and  120 . The sampling frequency of the four feature extraction modules  102   a ,  102   b ,  102   c , and  102   d  need not be the same. The sampling frequency, f s , may be different for different types of media signals. For example, there may be one sampling frequency for video signals and a different sampling frequency for audio signals. The sampling frequency, f s , may also be different between corresponding input and output signals. For example, the sampling frequency for the first input media signal  110  may be different than the sampling frequency for the first output media signal  118 . 
     In general, the sampling frequency is proportional to the accuracy of the synchronization error. The higher the sampling frequency the more accurate the calculated synchronization error. However, a higher sampling frequency may also increase the amount of storage and processing required. 
     In one embodiment, the sampling frequency, f s , is set to the frame frequency of the video signal. Typically, a video signal is transmitted as a series of frames. Each frame is identified by a start of frame (“SOF”) marker, which may vary depending on the format of the video signal. For example, an analog video signal may have a vertical sync pulse to indicate the beginning of a frame, and a digital video signal may have an embedded datum that indicates the beginning of data for a frame. The frame frequency (or frame rate) is the frequency at which an imaging device produces successive frames. Since a lip-sync error of plus or minus 1 video frame is not usually noticeable, a sampling frequency equal to the video frame frequency produces synchronization error at precision of around 1 video frame period or better, and this is usually sufficient. 
     In this embodiment, the sampling module  204  may be triggered to sample the received feature signal  208  based on the SOF markers in the corresponding media signal. Specifically, the feature extractor  202  may generate a feature signal  208  that includes SOF indicators corresponding to the SOF markers in the media signal. The SOF indicators may be any type of signal. For example, if the feature signal  208  is a continuous analog signal, the SOF indicators may be pulses added to the continuous analog signal. If the feature signal  212  is a set of discrete values, the SOF indicators may be a tag or bit pattern that indicates the timing of the SOF markers. 
     The storage module  206  receives the sampled feature signal  210  output by the sampling module  204  and stores the most recent T seconds of the sampled feature signal  210 . The storage module  206  is continuously updated by the sampling module  204  and can be generally described as a first-in-first-out (FIFO) buffer. 
     The time period, T, is typically chosen to be greater than the longest expected delay of the input media signals (e.g.  110  and  112 ) through the signal transport network  104 . In some embodiments, T is chosen to be twice as long as the expected maximum delay, or even longer. 
     The time period T may be different for corresponding input and output media signals (e.g. first input media signal  110  and first output media signal  118 ). In one embodiment, the time period T for the output media signal is smaller than the time period T for the corresponding input media signal. 
     Reference is now made to  FIG. 3 , in which a block diagram of a first feature extraction module  302   a  in accordance with an alternative embodiment is illustrated. The first feature extraction module  302   a  shown in  FIG. 3  and described herein is intended to be an example of a feature extraction module and the principles and concepts described in relation to  FIG. 3  should not be limited to the first feature extraction module  102   a . Specifically, any or all of the feature extraction modules  102   a ,  102   b ,  102   c  and  102   d  of  FIG. 1  may be implemented in a similar manner to the feature extraction module  302   a  shown in  FIG. 3 . 
     The first feature extraction module  302   a  is identical to feature extraction module  102   a  of  FIG. 2  except that it also includes a re-sampling module  304 . 
     In some situations it is preferable that the sampling rates for corresponding input and output media signals (e.g.  110  and  118 ) be the same. Accordingly, the feature extraction module  302   a  may also include a re-sampling module  304 . The re-sampling module  304  re-samples the extracted feature signal  126   a  at a different sampling frequency, f r , than the sampling frequency, f s , used by the sampling module  204 . The re-sampling module  304  may be used when corresponding input and output media signals (e.g.  110  and  118 ) are initially sampled at different sampling frequencies. For example, if the feature signal corresponding to an input media signal (e.g.  110  or  112 ) was sampled at 24 Hz and the feature signal corresponding to the output media signal (e.g.  118  or  120 ) was sampled at 30 Hz, then both feature signals can be re-sampled at 120 Hz, or alternatively the feature signal corresponding to the input media signal may be resampled at 30 Hz. The resampling module  304  can also be used to resample the feature signal at a higher sampling frequency so as to improve the accuracy of lip sync error produced. 
     The stored feature data for corresponding input and output media signals is retrieved by a delay calculation module  106   a  or  106   b  as an extracted feature signal  126   a ,  126   b ,  126   c  or  126   d  to determine the delay between corresponding input and output media signals (e.g. first input media signal  110  and first output media signal  118 ). In system  100  there are two delay calculation modules  106   a  and  106   b , the first delay calculation module  106   a  uses the extracted feature signals  126   a  and  126   c  generated by the first and third feature extraction modules  102   a  and  102   c  respectively to determine the delay between the first input and output media signals  110  and  118 ; and the second delay calculation module  106   b  uses the extracted feature signals  126   b  and  126   d  generated by the second and fourth feature extraction modules  102   b  and  102   d  respectively to determine the delay between the second input and output media signals  112  and  120 . 
     In systems where it is known that the characteristic features of the input media signals  110  and  112  will not be altered as they traverse the signal transport network  104 , then basic matching methods may be used to determine the delay from the extracted feature signals (i.e.  126   a  and  126   c ). An example of a basic matching method is the simple sliding technique where one feature signal is essentially slid along and compared to the second feature signal to determine a match. A match occurs when the sum of the absolute difference between the two signals is at a minimum. 
     Reference is now made to  FIG. 4 , which illustrates the simple sliding technique referred to above. The first sequence  402  comprises fifty samples and represents a first feature signal. The second sequence  404  also comprises 50 samples and represents a second feature signal which is a delayed version of the first feature signal. Using the simple sliding technique the first signal  402  is shifted to the right one sample at a time until a match is found. It can be seen from  FIG. 4  that the first and second sequences  402 ,  404  will “match” when the first sequence  402  is shifted to the right 10 samples. Accordingly, the delay between the first and second sequences  402  and  404  is equivalent to 10 samples. 
     However, in systems where it is possible that the characteristic features of the input media signals  110  and  112  will be altered as they traverse the signal transport network  104 , then more sophisticated matching methods, such as cross-correlation, may be used. 
     Reference is now made to  FIG. 5 , in which a block diagram of the first delay calculation module  106   a  in accordance with an embodiment is illustrated. The first delay calculation module  106   a  shown in  FIG. 5  and described herein is intended to be an example of a delay calculation module and the principles and concepts described in relation in  FIG. 5  should not be limited to the first delay calculation module  106   a . Specifically, any or all of the delay calculation modules  106   a  and  106   b  of  FIG. 1  may be implemented in a similar manner to the delay calculation module  106   a  shown in  FIG. 5 . 
     The first delay calculation module  106   a  includes a cross-correlation module  502  and a peak locator module  504 . 
     The cross-correlation module  502  receives the first extracted feature signal  126   a  corresponding to the first input media signal  110 , and the third extracted feature signal  126   c  corresponding to the first output media signal  118 . The cross-correlation module  502  may retrieve the extracted feature signals ( 126   a  and  126   c ) from the relevant feature extraction modules  102   a  and  102   c  or the feature extraction modules  102   a  and  102   c  may send the extracted feature signals  126   a  and  126   c  to the cross-correlation module  502  automatically. The cross-correlation module  502  then performs cross correlation on the extracted feature signals  126   a  and  126   c  and outputs a cross-correlation signal  506 . Cross-correlation is a measure of the similarity of two signals, f(x) and g(x), and is defined by equation (1) where the integral is over the appropriate values of t and a superscript asterisk indicates the complex conjugate.
 
( f*g )( x )=∫ f *( t ) g ( x+t ) dt   (1)
 
     Cross-correlation works by essentially sliding one signal along the x-axis of the other signal, and calculating the integral of the product of the two signals for each possible amount of sliding. The integral is maximized when the functions match. 
     Where the signals are discrete functions, f i  and g i , the cross-correlation is defined by equation (2) where the sum is over the appropriate values of the integer j. 
     
       
         
           
             
               
                 
                   
                     
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     Where the first discrete function, f i , has N 1  discrete values and the second discrete function, g i , has N 2  discrete values then N 1 +N 2 −1 cross-correlation values can be generated. 
     The cross-correlation module  502  may be implemented in the time domain, or in the frequency domain using a discrete fourier transform (DFT). 
     The cross-correlation signal  506  output by the cross-correlation module  502  is input to the peak locator  504 . The peak locator  504  determines the current peak position from the cross-correlation signal  506 . The current peak position is the position at which characteristic features of corresponding input and output media signals have the best match. 
     The peak locator  504  then determines a delay value representing the time delay between corresponding input and output media signals (e.g.  110  and  118 ) based on the current peak position. The peak locator  504  then outputs the delay value as a delay signal  130   a . In one embodiment, the delay value is equal to the current peak position divided by the sampling rate of the feature signal. Accordingly, the accuracy of the current peak position is directly proportional to the sampling frequency f s . The higher the sampling frequency, the more accurate the current peak position. 
     In one embodiment the accuracy of the current peak position is increased by re-sampling the feature signal at a sampling frequency, f r , greater than the original sampling frequency, f s , prior to cross-correlation. 
     In another embodiment, the accuracy of the current peak position is increased by determining the current peak position from the peak value and the values surrounding the peak value. For example, a fine resolution peak position may be determined using interpolation such as linear interpolation or parabolic interpolation. 
     Reference is now made to  FIG. 6 , in which a method of determining a fine resolution peak position using linear interpolation in accordance with an embodiment is illustrated. As is known to those of skill in the art, linear interpolation typically involves comparing the value of interest (i.e. the current peak) with two or more values within a predetermined distance from the value of interest. 
     In the exemplary method shown in  FIG. 6 , the current peak  602  of the cross correlation signal  506  has an amplitude p 2  and a position pos 2 . The cross-correlation value immediately preceding the peak  604  has an amplitude p 1 , and the cross-correlation value immediately following the peak  606  has an amplitude p 3 . A more accurate peak position, pos A , can be determined according to equation (3) when p 3  is greater than or equal to p 1 , and according to equation (4) in all other cases. 
     
       
         
           
             
               
                 
                   
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     In some cases the peak locator  504  may incorrectly identify the current peak position. This may occur, for example, where the cross-correlation is poor due to feature corruption caused by the signal transport network  104  or the nature of the feature data itself. Another example in which an incorrect current peak position may be identified is where the two media signals (e.g. the first input media signal  110  and the corresponding first output media signal  118 ) match at multiple positions. In this case there will be multiple peaks in the cross-correlation signal  506 , and the highest of these peaks may not accurately represent the delay between the two media signals (e.g. first input media signal  110  and first output media signal  118 ). To eliminate possible false peaks, in some embodiments the peak locator  504  implements thresholding. For example, a peak may be eliminated from consideration if the cross-correlation value at the peak is lower than a predetermined percentage of the product of the total cross-correlation values from the two media signals (e.g. first input media signal  110  and first output media signal  118 ). In one embodiment the predetermined percentage is 5%. 
     The synchronization error module  108  receives the two delay signals  130   a  and  130   b  generated by the delay calculation modules  106   a  and  106   b , and outputs a synchronization error signal  132 . The synchronization error signal  132  represents the difference between the two delay signals  130   a  and  130   b . The synchronization error signal  132  is fed to the signal transport network  104  where it is used to correct the synchronization error. In some embodiments, the synchronization error may be corrected by adding a delay to the path that has the shorter delay, reducing the delay to the path that has the longer delay, or both. 
     In some embodiments, one or more of the feature extraction modules  102   a ,  102   b ,  102   c  or  102   d  further includes a processing module. The processing module processes the feature signal (e.g. feature signal  208 ) to improve cross-correlation. For example, the processing module may be a differentiator or may be a combination of a differentiator and a logarithmic module. The processing module may be situated between the sampler  204  and the storage module  206  or alternatively it may be situated after the storage module  206 . 
     In some embodiments, system  100  is used to generate the synchronization error once and in other embodiments the synchronization error is generated periodically. Where the synchronization error is generated on a periodic basis, either or both of the peak locator  504  and the synchronization error module  108  may further include a filter for smoothing the peak signal  508  and the synchronization error signal  132  respectively. The filters may be moving average filters. 
     System  100  has been described in the context of synchronizing two media signals  110  and  112 . However, in other embodiments three or more media signals are synchronized by extracting the characteristic features of each media signal at the input and output of the signal transport network  104  and detecting the delay of each media signal. 
     Reference is now made to  FIG. 7 , in which a system  700  for determining the time delay between two media signals in accordance with an embodiment is illustrated. Components of system  700  that correspond to components of system  100  are identified with similar reference numerals. 
     Where one of the media signals is a version of the other media signal after it traversed a signal network (e.g. one of the media signals is the input to a signal transport network and the other media signal is the output from the signal transport network), the time delay represents the amount of time it takes for the media signal to travel through the signal transport network. It some applications it is desirable to know the delay for a media signal to travel through a signal transport network. 
     The system  700  includes two feature extraction modules  702   a  and  702   b , a signal transport network  704 , a sampler monitoring module  740 , a delay calculation module  706 , and a delay adjustment module  742 . 
     A first media signal  710  is input into the system  700  at an input terminal  714 . The input terminal  714  is coupled to one of the feature extraction modules  702   a , and also to the signal transport network  704 . The first media signal  710  is transported through the signal transport network  704  and output as a second media signal  718  at output terminal  722 . The first and second media signals  710  and  718  may be video signals, audio signals or video/audio signals. 
     The signal transport network  704  corresponds to the signal transport network  104  of  FIG. 1 . Specifically, the signal transport network  704  will typically include audio and video signal transportation devices which transport the first media signal  710  from one point to another. The signal transport network  704  may also include audio and video processing devices which modify the first media signal  710 . Where the signal transport network  704  includes processing devices, the second media signal  718  may be different than the first media signal  710 . For example, an MPEG compressor introduces compression artifacts in a video signal and a video standard converter changes the video size and/or frame rate of the video signal. 
     The feature extraction modules  702   a  and  702   b , the sampler monitoring module  740 , the delay calculation module  706 , and the delay adjustment module  742  operate to determine the time delay between the first and second media signals  710  and  718 . 
     Each feature extraction module  702   a  and  702   b  extracts at least one characteristic feature from the first or second media signal  710  and  718 , and outputs an extracted feature signal  726   a  or  726   b . Specifically, the first feature extraction module  702   a  extracts at least one characteristic feature from the first media signal  710 , and outputs a first extracted feature signal  726   a  The second feature extraction module  702   b  extracts at least one characteristic feature from the second media signal  718  and outputs a second extracted feature signal  726   b . The feature extraction modules  702   a  and  702   b  may be implemented as the feature extraction modules  106   a  and  306   a  described in reference to  FIGS. 2 and 3  respectively. In particular, the feature extraction modules  702   a  and  702   b  may include a feature extractor, a sampling module, and a storage module. 
     As described above, the feature extractor receives a media signal (i.e. first media signal  710 , or second media signal  718 ), extracts one or more characteristic features from the media signal, and outputs a feature signal. The feature signal corresponding to the first media signal  710  will be referred to as the first feature signal and the feature signal corresponding to the second media signal  718  will be referred to as the second feature signal. The sampling module receives the feature signal from the feature extractor, samples it at a sampling frequency, and outputs a sampled feature signal. The sampled feature signal corresponding to the first media signal  710  will be referred to as the first sampled feature signal and the sampled feature signal corresponding to the second media signal  718  will be referred to as the second sampled feature signal. The storage module receives the sampled feature signal output by the sampling module and stores the most recent T seconds of the sampled feature signal. 
     It is possible that the sampling of the first feature signal and the second feature signal occur at different times. This may occur, for example, because the second media signal  718  is out of phase with the first media signal  710 . This may also occur if the second media signal  718  is in a different format than the first media signal  710  and has SOF markers at a different frequency than the first media signal  710 . The sampler monitoring module  740  is designed to determine the difference between the first feature signal sampling time and the second feature signal sampling time. This time difference will be referred to as the sampler time difference. 
     In some embodiments, the difference between the first feature signal sampling time and the second feature signal sampling time may be determined each time that the feature signals are sampled. For example, in one embodiment the sampler monitoring module  740  may include a high-resolution clock that is started (or reset) when the first feature signal is sampled, and stopped when the second feature signal is sampled. In other embodiments the high-resolution clock may be started (or reset) when the second feature signal is sampled, and stopped when the first feature signal is sampled. 
     The delay calculation module  706  corresponds to delay calculation module  106  of  FIG. 1 . Specifically, the delay calculation module  706  determines the amount of delay between the first and second media signals  710  and  718  from the first and second extracted feature signals  726   a  and  726   b  generated by the first and second feature extraction modules  702   a  and  702   b  respectively. The delay calculation module  706  outputs a delay signal  730  that represents the calculated delay. The delay signal  730  may be provided as a series of discrete values or as an analog signal. 
     In systems where it is known that the characteristic features of the first media signal  710  will not be altered as they traverse the signal transport network  704 , basic matching methods may be used to determine the delay from the extracted feature signals  726   a  and  726   b . An example of a basic method matching method is the simple sliding technique, which was described in reference to  FIG. 4 . However, in systems where it is possible that the characteristic features of the first media signal  710  will be altered as they traverse the network  704 , more sophisticated matching methods may be used. An example of a more sophisticated matching method is cross-correlation, which was described in reference to  FIG. 5 . The delay calculation module  706  may be implemented as the delay calculation module  106   a  described in reference to  FIG. 5 . 
     The delay adjustment module  742  adjusts the delay signal  730  produced by the delay calculation module  706  to account for the different sampling times, and outputs an adjusted delay signal  744 . The adjusted delay signal  744  may be provided as a series of discrete values or as an analog signal. In one embodiment, if the most recent extracted feature signal  126   a  and  126   b  data corresponds to the second media signal  718 , the adjusted delay signal  744  is calculated in accordance with equation (5), and if the most recent extracted feature signal  126   a  and  126   b  data corresponds to the first media signal  710 , the adjusted delay signal  744  is calculated in accordance with equation (6). However, it will be evident to a person of skill in the art that the adjusted delay signal  744  may be calculated in other ways.
 
adjusted delay signal=delay signal+sampler time difference  (5)
 
adjusted delay signal=delay signal+sampler time difference−input sampling period  (6)
 
     In some embodiments, the delay adjustment module  742  may include a filter (not shown) for smoothing the adjusted delay signal  744 . The filter may be a moving average filter. 
     Reference is now made to  FIG. 8 , in which a system  800  for determining the likelihood that two media signals match in accordance with an embodiment is illustrated. Components of system  800  that correspond to components of system  100  are identified with similar reference numerals. 
     As described above, two media signals are said to match if they represent the same content. For example, a high quality video of a movie and a DVD version of the same movie are said to match. Such information is often required in the video dubbing/conversion industry. For example, a high quality video content on a professional video tape may be reproduced onto a DVD. It is important to ensure that the content of the high quality video has been faithfully copied onto the DVD. Typically, a human is required to watch the entire DVD to manually verify its contents. However, such a method is time consuming and prone to human error. In other prior art systems, the media signals are aligned and a subtraction or signal to noise ratio (SNR) is performed. The problems with these types of prior are systems, however, is that they typically require a large amount of memory or storage and they require that the medial signals be of the same temporal rate and size. 
     The system  800  of  FIG.8  includes two feature extraction modules  802   a  and  802   b  and a match confidence signal generator  849 . The match confidence signal generator  849  includes a cross correlation module  850 , and a strength and consistency analyzer  852 . 
     First and second media signals  810  and  812  are input into the system  800  at first and second input terminals  814  and  816  respectively. Each input terminal  814 ,  816  is coupled to one of the feature extraction modules  802   a ,  802   b.    
     Each feature extraction module  802   a ,  802   b  extracts at least one characteristic feature from a media signal  810  or  812  and outputs an extracted feature signal  826   a  or  826   b . Specifically, the first feature extraction module  802   a  extracts at least one characteristic feature from the first input media signal  810  to produce a first extracted feature signal  826   a ; and, the second feature extraction module  802   b  extracts at least one characteristic feature from the second input media signal  812  to produce a second extracted feature signal  826   b . The feature extraction modules  802   a  and  802   b  may be implemented as either of the feature extraction modules  106   a  and  306   a  described in reference to  FIGS. 2 and 3  respectively. Specifically, each feature extraction module  802   a  and  802   b  may include a feature extractor, a sampling module, and a storage module. 
     As described above, the feature extractor receives an input media signal (i.e. first or second input media signal  810  or  812 ), extracts one or more characteristic features from the media signal, and outputs a feature signal. The sampling module receives the feature signal from the feature extractor, samples it at a sampling frequency, and outputs a sampled feature signal. The storage module receives the sampled feature signal output by the sampling module and stores the most recent T seconds of the sampled feature signal. 
     The match confidence signal generator  849  receives the first and second extracted feature signals  826   a  and  826   b  generated by the first and second feature extraction modules  802   a  and  802   b  and generates a match confidence signal  856 . The match confidence signal  856  represents the likelihood or probability that the first and second input media signals  810  and  818  “match” (i.e. they represent the same content). In one embodiment, the match confidence signal generator  849  includes a cross correlation module  850  and a strength and consistency analyzer  852 . 
     The cross correlation module  850  performs cross correlation on the first and second extracted feature signals  826   a  and  826   b  generated by the first and second feature extraction modules  802   a  and  802   b  respectively, and outputs a cross-correlation signal  854 . Cross-correlation was described in detail in reference to  FIG. 5 . The cross correlation module  850  may be implemented as the cross-correlation module  502  described in reference to  FIG. 5 . 
     The strength and consistency analyzer  852  analyzes the cross-correlation signal  854  generated by the cross correlation module  850  and outputs the match confidence signal  856 . An exemplary strength and consistency analyzer  852  will be described in reference to  FIG. 9 . 
     Reference is now made to  FIG. 9 , wherein a strength and consistency analyzer  852  in accordance with an embodiment is illustrated. The strength and consistency analyzer  852  includes a peak locator module  902  and a match confidence signal adjustment module  904 . 
     The peak locator module  902 , similar to peak locator module  504  of  FIG. 5 , determines the current peak position from the cross-correlation signal  854  generated by the cross-correlation module  850 . As described above, the current peak position is the position at which the characteristic features of two media signals (i.e. first and second input media signals  810  and  812 ) have the best match. The current peak position is typically the position at which the highest cross-correlation value occurs. This value is referred to as the current peak value. The peak locator module  902  outputs a peak signal  906  that represents the current peak position and the current peak value. 
     In some cases, the peak locator module  902  may incorrectly identify the current peak position. This may occur, for example, due to feature corruption, or the nature of the characteristic feature data itself. In these cases, the current peak value is typically low. To eliminate these false peaks, in some embodiments, the peak locator module  902  implements thresholding. For example, a peak may be eliminated from consideration if the cross-correlation value at the peak is lower than a predetermined percentage of the product of the total cross-correlation values. In one embodiment, the predetermined percentage is 5%. 
     The match confidence signal generator  904  receives the peak signal  906  (representing the current peak position and current peak value) from the peak locator module  902  and generates the match confidence signal  856 . The match confidence signal  856  may be provided as a series of discrete values or an analog signal. As described above, the match confidence signal  856  represents the likelihood or the probability that the two input media signals  810  and  812  match (i.e. represent the same content). The match confidence signal  856  may be generateed from the current peak value or the current peak position. However, since two different media streams may still produce a high peak value, the current peak value is preferably determined from the current peak value and the current peak position. The match confidence signal  856  typically ranges between a high match value, which indicates a high probability that the media signals match; and a low match value, which indicates a low probability that the media signals match. 
     In one embodiment, the match confidence signal  856  is calculated as follows. If the current peak value is low then the match confidence signal  856  is adjusted to be closer to the low match value. In some embodiments, this involves decreasing the match confidence signal  856 . A current peak value may be deemed to be low if it falls below a predetermined matching peak threshold. 
     If, however, the current peak value is not low (e.g. the current peak value meets or exceeds the predetermined matching peak threshold) then the match confidence signal  856  is adjusted to be closer to the high match value (e.g. the match confidence signal  856  may be increased) if the current peak position is similar to one or more previous peak positions, and adjusted to be closer to the low match value (e.g. the match confidence level may be decreased) if the current peak position is not similar to one or more previous peak positions. In one embodiment, an average of the peak positions is generated and the current peak position is compared against the average of the previous peak positions. In this embodiment, a new average peak position is calculated after each new current peak position. 
     It will be evident to a person of skill in the art that the match confidence signal  856  may be calculated in accordance with other algorithms. 
     Reference is now made to  FIG. 10 , in which a system  1000  for determining the likelihood that two media signals match in accordance with a second embodiment is illustrated. The only difference between the system  1000  of  FIG. 10  and the system  800  of  FIG. 8  is the addition of a short window analyzer  1060  to the match confidence signal generator  1049 . Components of system  1000  that correspond to components of system  800  are identified with similar reference numerals. 
     In general, the cross correlation length (the time period over which the cross correlation is performed) used by the cross-correlation module  1050  is longer than the delay between the input media signals  1010  and  1012 . However, the longer the cross correlation length, the longer it takes for the match confidence level to drop when the input media signals start to differ. To speed up the time it takes for the match confidence level to reflect the fact that the two media signals  1010  and  1012  no longer match, a short window analyzer  1060  is added to the system  1000 . The short window analyzer  1060  (i) analyzes the first and second feature data over a shorter period or length than the cross correlation module  1050 ; and (ii) updates the match confidence signal  856  accordingly. 
     Reference is now made to  FIG. 11  to illustrate the short window analyzer concept.  FIG. 11  illustrates the first input media signal  1010  and the second input media signal  1012  as a function of time. Each input media signal  1010  and  1012  has been divided into portions. The first input media signal  1010  has a first portion  1102  and a second portion  1104 . Similarly, the second input media signal  1012  has first and second portions  1106 ,  1108  respectively. 
     As shown in  FIG. 11 , the first input media signal  1010  is “ahead” of second media signal  1012 , meaning that if the first and second input media signals  1010  and  1012  have the same content, the content will appear in the first input media signal  1010  before it appears in the second input media signal  1012 . 
     If the first portion  1102  of the first input media signal  1010  matches the first portion  1106  of the second input media signal  1012  then the match confidence level will be closer to the high match value for the cross-correlation window shown in  FIG. 11 . However, if the second portion  1104  of the first input media signal  1010  does not match the second portion  1108  of the second input media signal  1012 , it will take a long time for the match confidence level to be adjusted to be closer to the low match value since the majority of the window still matches. 
     In one embodiment, the short window analyzer  1060  selects a window of the first sampled feature signal (the sampled feature signal corresponding to the first media signal  1010 ) and a window, of a corresponding size, of the second sampled feature signal (the sampled feature signal corresponding to the second media signal  1012 ) to analyze. The windows used by the short window analyzer  1060  are shorter than the cross-correlation length used by the cross-correlation module  1050 . In one embodiment, one of the windows represents the most recent feature data for a particular input media signal, and the other window represents the corresponding feature data for the other input media signal. For example, one window may represent the second portion  1108  of the second input media signal  1012 , and the other window may represent the second portion  1104  of the first input media signal. 
     The location of the second portion  1104  of the first input media signal  1010  can easily be determined from the average peak position calculated by the strength and consistency analyzer  1052 . Specifically, as described above in relation to  FIG. 3 , the peak position reflects the amount of delay between the two input media signals  1010  and  1012 . Specifically, the amount of delay is equal to the peak position divided by the sampling frequency. 
     Once the two windows are selected, the short window analyzer  1060  compares the data in the two windows to see if they match. In some embodiments, this may involve a basic comparison. For example, in one embodiment, the comparison involves calculating the sum of absolute difference between the first and second sampled feature data. If the result is lower than a predetermined threshold then the match confidence signal  1056  is considered to be valid and is not adjusted. If, however, the result is higher than a predetermined threshold, then the match confidence signal  1056  is not considered to be valid and is adjusted to be closer to the low match value (e.g. in some embodiments this may involve decreasing the match confidence signal  1056 ). In other embodiments, more complex comparison techniques may be used 
     Reference is now made to  FIG. 12 , in which a system  1200  for determining the likelihood that two media signals match in accordance with a third embodiment is illustrated. The only difference between the system  1200  of  FIG. 12  and the system  1000  of  FIG. 10  is that the short window analyzer  1060  of the match confidence signal generator  1049  of  FIG. 10  has been replaced with a second cross correlation module  1270  (referred to as the short cross correlation module) and a second strength and consistency analyzer  1272 . Components of system  1200  that correspond to components of systems  800  and  1000  are identified with similar reference numerals. 
     The second cross correlation module  1270  and the second strength and consistency analyzer  1272  work together to perform the same function as the short window analyzer  1060  of  FIG. 10 . Specifically, they operate to analyze the extracted feature data over a smaller window than the first cross correlation module  1250  and the first strength and consistency analyzer  1252  so as to more quickly adapt to sudden mismatches or matches between the two media signals. 
     The second cross correlation module  1070  operates in a similar manner to the first cross correlation module  1250  except it uses a smaller cross correlation window, and it uses the average peak position generated by the first strength and consistency analyzer  1252  to select the extracted feature data to analyze. After performing a cross correlation on the selected data, the second cross correlation module  1070  outputs a second cross correlation signal  1274 . 
     The second strength and consistency analyzer  1272  received the second cross correlation signal  1274  and adjusts the match confidence signal  1256  generated by the first strength and consistency analyzer  1252  to produce an adjusted match confidence signal  1276 . 
     While the above description provides examples of various embodiments of the invention, it will be appreciated that some features and/or functions of the described embodiments are susceptible to modification without departing from the spirit and principles of operation of the described embodiments. What has been described above has been intended to be illustrative of the invention and non-limiting and it will be understood by persons skilled in the art that other variants and modifications may be made without departing from the scope of the invention as defined in the claims appended hereto.