Patent Publication Number: US-11665288-B2

Title: Methods and apparatus to identify media using hybrid hash keys

Description:
RELATED APPLICATION 
     This patent arises from a continuation of U.S. patent application Ser. No. 16/227,524, which was filed on Dec. 20, 2018, which is a continuation of U.S. patent application Ser. No. 14/866,755 (now U.S. Pat. No. 10,200,546), which was filed on Sep. 25, 2015. U.S. patent application Ser. Nos. 16/227,524 and 14/866,755 are hereby incorporated herein by reference in their entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to audience measurement and, more particularly, to methods and apparatus to identify media using hash keys. 
     BACKGROUND 
     Audience measurement of media, such as television, music, movies, radio, Internet websites, streaming media, video games, etc., is typically carried out by monitoring media exposure of panelists that are selected to represent a particular demographic group. The captured media exposure data is processed using various statistical methods to determine audience size and demographic composition(s) for programs of interest. The audience size and demographic information is valuable to advertisers, broadcasters and/or other entities. For example, audience size and demographic information may be used as factors in selecting the placement of advertisements, and may be used as factors in valuing commercial time slots during a particular program. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example system constructed in accordance with the teachings of this disclosure and having a media meter in communication with an audience measurement entity to monitor media presentations. 
         FIG.  2    illustrates the example reference hash key generator of  FIG.  1    which may be used to generate the example reference records of  FIGS.  3 A and/or  3 B . 
         FIGS.  3 A and  3 B  illustrate example configurations of the reference database of  FIG.  1    that may be used to store reference metadata in association with reference hash keys of corresponding media. 
         FIG.  4    illustrates an example diagram that depicts generating the example reference records of  FIGS.  3 A and/or  3 B . 
         FIG.  5    illustrates an example implementation of the example hash key identifier of  FIG.  1    which may be used to compare metered hash keys with reference hash keys to generate a monitoring report and/or to store impressions in a monitoring database. 
         FIG.  6    is a flow diagram of example machine readable instructions that may be executed to implement the hash key manager of  FIGS.  1  and/or  5    to compare metered hash keys to reference hash keys. 
         FIG.  7    is another flow diagram of example machine readable instructions that may be executed to implement the hash key manager of  FIGS.  1  and/or  5    to compare metered hash keys to reference hash keys. 
         FIG.  8    is a flow diagram of example machine readable instructions that may be executed to implement the reference hash key generator of  FIGS.  1  and/or  2    to generate reference hybrid-hash keys. 
         FIG.  9    is a block diagram of an example processor system that may execute any of the machine readable instructions represented by  FIGS.  6 ,  7   , and/or  8  to implement the apparatus of  FIGS.  2  and/or  5   . 
     
    
    
     DETAILED DESCRIPTION 
     Examples disclosed herein may be used to identify media (e.g., movies, music, television programs, radio programming, television advertisements, radio advertisements, video games, etc.) using hash keys associated with the media. To create indexable identifiers for portions of media of interest, in examples disclosed herein, the media is sampled at a particular frequency (e.g., 15 kHz, 30 kHz, 64 kHz, etc.). Using one or more fingerprinting techniques, such as robust audio hashing, hash keys are generated based on the samples of the media. In some robust audio hashing examples, binary values represent differences in energy between frequency bands of a sample. In some such examples, a hash key has a length in bits corresponding to the number of energy bands used to create the hash key (e.g., a 64-bit length hash key corresponds to the differences between 65 energy bands). Samples of the media may be hashed, for example, in accordance with the techniques described by Haitsma et al. in an article entitled, “Robust Audio Hashing for Content Identification.” 
     To generate reference hash keys, a reference version of media is sampled at a sampling frequency (e.g., 15 kHz, 30 kHz, 64 kHz, etc.). In some examples, reference media is media (e.g., a song, a television program, a radio program, a video and/or audio spot or clip, an advertisement, streaming media, etc.) that has the same or higher quality than media typically obtained by and/or presented to a user. In some examples, the reference media is free from noise (e.g., white noise, pink noise, brown noise, etc.) and/or is stored and/or decoded using a lossless format (e.g., Free Lossless Audio Codec (FLAC), Waveform Audio File Format (WAV), Apple® Lossless Audio Codec (ALAC), etc.). For example, a reference version (or reference media) of audio (e.g., collected in a controlled environment, such as a studio) may be a high quality, lossless digital copy of the song relative to whereas a streamed version (e.g., measured media) of the same audio will typically exhibit lower quality and less accuracy in its reproduction and playback due to environmental noise, transmission losses, etc. 
     In some examples, an audience measurement entity (AME) contacts and/or enlists panelists using any desired methodology (e.g., random selection, statistical selection, phone solicitations, Internet advertisements, surveys, advertisements in shopping malls, product packaging, etc.). Demographic information (e.g., gender, occupation, salary, race and/or ethnicity, marital status, highest completed education, current employment status, etc.) is obtained from a panelist when the panelist joins (i.e., registers for) a panel. Additionally or alternatively, demographic information may be obtained through other methods during an enrollment process (e.g., via a telephone interview, by having the panelist complete an online survey, etc.). In some examples, the AME provides a media meter (e.g., a set top meter, a personal portable meter (PPM), an on-device meter, a portable media player meter, etc.) to the panelist after the panelist enrolls into the panel. 
     In some examples, the media meters collect metered samples by sampling media from media sources that are within sufficient detection proximity to the meter. For example, a set top meter may sample audio from a movie presented via a media presentation device, such as a television located in the same room as the set top meter, or a portable media player meter may sample audio presented via a media presentation device such as a portable media player (e.g., an MP3 player, an Apple® iPod®, etc.). In some examples, the sample is captured using a microphone of the media meter. In some examples, the media meter obtains the metered sample through a wired connection (e.g., to an audio out jack) via a splitter or an in-line configuration via which the media meter intercepts portions of the media as they are communicated between a media source and headphones, etc. In some examples, the media samples are sampled by the media meters at the same frequency as the reference samples were sampled. In some examples, the metered samples are sent to a central office of the AME where metered hash keys are generated based on the metered samples. In some examples, the media meter is provided with a hash key generator to locally generate metered hash keys. In some such examples, the media meter sends metered hash keys to the central office. 
     In examples disclosed herein, a reference record is constructed by generating a reference hash key for a sample of reference media. In some examples, the reference hash key may be 40-bits long or 64-bits long. Metadata (e.g., the name of the corresponding media, a time and/or offset in the media corresponding to the sample, etc.) related to the sample is stored in the reference record in association with the reference hash key. The reference records also includes confirmation data that corresponds to the reference hash key. The confirmation data is another sample of the reference media that is related to the sample used to generate the reference hash key. For example, the confirmation data may be 32-bits of the reference media sample that immediately follow the sample used to generate the reference hash key. In some examples, a blurring function is applied to the reference hash key. The blurring function reduces the specificity of the reference hash key in order to increase error tolerance of the reference hash key. Because the specificity of the reference hash key is reduced, one of the reference hash keys may be associated with multiple sets of metadata. Additionally, in some examples, samples of more than one of the media may, by coincidence, produce the same reference hash key. In such examples, the confirmation data is used to distinguish between identical reference hash keys. 
     Errors may arise in the media presentation before the media presentation is sampled by a media meter. For example, converting media from a lossless format (e.g., Free Lossless Audio Codec (FLAC), Waveform Audio File Format (WAV), Apple® Lossless Audio Codec (ALAC), etc.) to a lossy format (e.g., MPEG Audio Layer III (MP3), Advanced Audio Coding (AAC), Ogg Vorbis, etc.) may change the media sufficiently so that a metered hash key generated based on a portion (e.g., a segment) of the lossy-format media is different from a reference hash key corresponding to a non-lossy format of the same portion (e.g., the same segment) of the media. Additionally or alternatively, ambient noise and/or attenuation may also introduce errors into samples of the measured media. Transmission errors may also be a source of errors in metered hash keys. These sources of noise, loss and/or error may cause one or more bits of the metered hash key to be different relative to a corresponding reference hash key. 
     In some examples, the blurring function may set one or more of the least significant bits in each byte of the reference hash key to zero because the least significant bit(s) of the bytes that make up the hash key are most prone to noise during the hash key generating process. In some examples, the number of bits set to zero depends on the byte-length of the reference hash key. For example, if the reference hash key is 40-bits long, the blurring function may set the least significant bit of each byte to zero. Alternatively, for example, if the reference hash key is 64-bits long, the blurring function may set the two least significant bits of each byte to zero. For example, by blurring the least significant bit, if the generated reference hash key is 0x 0D 73 E1 BD (binary: 00001101 01110011 11100001 10111101), the blurred reference hash key would be 0x 0C 72 E0 BC (binary: 00001100 01110010 11100000 10111100). 
     In examples disclosed herein, the media meter generates metered hash keys and corresponding confirmation data. In such examples, the confirmation data generated by the media meter has the same length and offset as the confirmation data generated for the reference hash keys. In some examples, the media meter blurs the generated metered hash keys using the same blurring function applied to the reference hash keys to the generated metered hash keys. Alternatively, in some examples, the media meter sends the metered hash keys without applying the blurring function and the blurring function is applied to the generated metered hash keys before the metered hash key is compared to the reference hash keys. 
     In examples disclosed herein, the AME receives metered hash keys and corresponding confirmation data from the media meter and compares the metered hash keys to reference hash keys in the reference hash table. If a metered hash key is found in the reference hash table, the confirmation data corresponding to the metered hash key is compared to the confirmation data corresponding to the reference hash key. If the confirmation data corresponding to the metered hash key matches the confirmation data corresponding to the reference hash key, an impression for corresponding media (e.g., reference media corresponding to the matching reference hash key) is logged. In some examples, metadata corresponding to the reference hash key is retrieved from a corresponding reference record, and the metadata is stored in association with the logged impression. In some examples, information (e.g., demographics, panelist ID, etc.) associated with one or more panelists and/or a timestamp indicative of a time at which the metered media was presented is stored in association with the logged impression. 
     In examples disclosed herein, when the metered hash key is compared to the reference hash keys in the reference hash key table, multiple candidate reference hash keys may exist. For example, when the reference hash keys are generated, the least significant bit is blurred. As such, a reference hash key of 0x0C 72 E0 BC may correspond to the following non-blurred reference hash keys: 0x0C 73 E0 BC, 0x0D 73 E0 BC, 0x0D 72 E0 BC, 0x0C 72 E0 BD, 0x0C 73 E0 BD, 0x0D 73 E0 BD, 0x0D 72 E0 BD, 0x0C 72 E1 BD, 0x0C 73 E1 BD, 0x0D 73 E1 BD, 0x0D 72 E1 BD, 0x0C 72 E1 BC, 0x0C 73 E1 BC, 0x0D 73 E1 BC, and 0x0D 72 E1 BC. In such examples, when multiple candidate reference hash keys exist in the reference hash key table, the confirmation data corresponding to the metered hash key is compared to the confirmation data corresponding to the reference hash keys. In some such examples, error levels are calculated between the confirmation data corresponding to the metered hash key and the confirmation data corresponding to the reference hash keys. In such examples, metered hash key is determined to match the reference hash key that has the lowest error level that satisfies (e.g., is less than, etc.) an error threshold. 
       FIG.  1    illustrates an example system constructed in accordance with the teachings of this disclosure and having the media meter  100  in communication with the AME  102  to monitor media  104  presented by the media presentation device  106 . In the illustrated example, the media meter  100  samples the example media  104  output by the example media presentation device  106  and generates example exposure records  108 . In some examples, a people meter  110  is associated with the media meter  100  to identify persons in the audience at the time the exposure records  108  are collected. In some examples, people identification data collected by the people meter  110  is returned with the exposure records  108 . From time to time, the example media meter  100  sends the example exposure records  108  to the example AME  104  via an example network  112  (e.g., the Internet, a local area network, a wide area network, etc.) via wired and/or wireless connections (e.g., a cable/DSL/satellite modem, a cell tower, etc.). 
     In the illustrated example, the exposure records  108  include an example metered hash key  114 , example metered confirmation data  116 , an example media meter identifier (ID)  118 , and an example timestamp  120 . In some examples, the exposure records  108  also include identifiers associated with the persons in the audience as detected by the people meter(s)  110 . The example metered hash key  114  is a value that characterizes a portion of the media  104  or is representative of a portion of the media  104  at a certain point in time (e.g., as indicated by the timestamp  120 ) of the media  104 . In some examples, the metered hash key  114  is taken from a stream of the media  104 . Alternatively, in some examples, the stream of media  104  is preprocessed by a signature generation engine that hashes the stream of the media  104 . In such examples, the metered hash key  114  is taken from the hashed stream of the media  104 . In some examples, the media meter  100  applies a blurring function after generating the hash key  114 . In such examples, the blurring function sets a number of least significant bits in each byte of the hash key  114  to zero. 
     The example metered confirmation data  116  includes a number of bits of the media  104  offset from an end of the metered hash key  114  by a number of bits. For example, the metered confirmation data  116  may include twenty-four bits corresponding to a subsequent portion of the media  104  following the portion of the media  104  corresponding to the metered hash key  114 . In the illustrated example, the media meter ID  118  is an alphanumeric value which identifies (preferably uniquely) the media meter  100  and/or one or more of the people associated with the people meter  110 . The example timestamp  120  corresponds to a time when the portion of the media  104  represented by the metered hash key  114  is presented by the example media presentation device  106 . 
     The AME  102  of the illustrated example includes an example metering database  122 , an example hash key identifier  124 , an example monitoring database  126 , an example reference database  128 , and an example reference hash key generator  130 . The example exposure records  108  are collected and stored in the example metering database  122 . 
     As disclosed in more detail in  FIG.  5    below, the example hash key identifier  124  compares the exposure records  108  to reference records in the reference database  128  to identify the portion of the media  104  corresponding to the metered hash key  114 . When one of the exposure records  108  corresponds to one of the reference records, the example hash key identifier  124  generates an impression. The impression associates the media meter ID  118  and/or the timestamp  120  to the portion of the media  104  (e.g., as a media segment ID) and/or metadata identifying the portion of the media  104  corresponding to the matching reference record. 
     As discussed in more detail in  FIG.  2    below, the reference hash key generator  130  samples the reference media  132  (e.g., media that has the same or higher quality than media  104  obtained by and/or presented to a user) to generate the reference hash keys. In some examples, the reference hash key generator  130  applies the burring function to the reference hash key. The example reference hash key generator  130  also generates reference confirmation data using the same size and offset as the media meter  100  uses to generate the metered confirmation data  116 . The example reference hash key generator  130  creates reference records that include the reference hash key and corresponding reference confirmation data. In the illustrated examples, the example reference hash key generator  130  stores the generated reference records created based on the reference hash keys in the example reference database  128 . In some examples, the reference hash key generator  130  does not create the reference confirmation data for particular ones of the reference hash keys at the beginning and/or at the end of the reference media  132  because there are not enough samples of the reference media  132  to generate the reference confirmation data. 
       FIG.  2    illustrates an example implementation of the example reference hash key generator  130  of  FIG.  1   . The example reference hash key generator  130  generates reference records  202  to be stored in the example reference database  128 . The example reference hash key generator  130  includes an example hybrid hash key generator  204 , an example hash key modifier  206 , and an example reference generator  208 . The example hybrid hash key generator  204  samples the reference media  130  at a sampling frequency (e.g., 16 kHz, 32 kHz, 64 kHz, etc.). 
     The example hybrid hash key generator  204  generates reference hash keys  210  based on the samples. The example reference hash keys  210  are representative of a particular portion of the reference media. The example reference hash keys  210  are used as an index to identify the corresponding portion of the reference media when compared to metered hash keys. Additionally, the example hybrid hash key generator  204  generates reference confirmation data  212  based on the samples. The example hybrid hash key generator  204  uses a size (e.g., in bytes) and an offset to determine which samples are to be used for the reference confirmation data  212 . For example, the reference confirmation data  212  may have a size of twenty-four bits and an offset of two bits. In such an example, because the offset is two bits, the reference confirmation data  212  begins at two bits from the end of the reference hash key  210  to which the reference confirmation data  212  corresponds. In some examples in which the offset is a negative number, the reference confirmation data  212  overlaps with the corresponding reference hash key  210 . The size and the offset are defined by the example AME  102  ( FIG.  1   ) so that the size and offset used by the example hybrid hash key generator  204  are the same as the size and the offset used by the example media meter  100  ( FIG.  1   ) to generate there metered hash key  114  and the metered confirmation data  116  of the exposure record  108  ( FIG.  1   ). 
     In some examples, when the size and the offset specify samples that are not generated for the reference media  132  (e.g., at the end of the reference media  132 ), the hybrid hash key generator  204  does not generate the reference confirmation data  212 . For example, if the size and the offset specify that 32-bits of the samples of the reference media  132  after the reference hash key  210  are to be used to generate the reference confirmation data  212  and only 16-bits remain until the end of the reference media  132 , the hybrid hash key generator  204  may not generate the reference confirmation data  212 . In some such examples, the hybrid hash key generator  204  may instead generate the reference confirmation data  212  with a placeholder value (e.g., 0x00 00 00 00, 0xFF FF FF FF, 0xAA AA AA AA, etc.). 
     The example hash key modifier  206  applies the blurring function to the reference hash key  210  to generate a blurred reference hash key  214 . The blurring function sets a number of the least significant bits of each byte of the reference hash key  210  to zero. In some examples, the number of bits that the hash key modifier  206  sets to zero depends on the bit-length of the reference hash key  210 . For example, longer metered hash keys  114  represent a greater degree of precision (e.g., 64-bits representing a portion of the media instead of 40-bits, etc.), but are also more likely to have least significant bits subject to noise. For example, if the reference hash key  210  is 40-bits long, the hash key modifier  206  may set the least significant bit of each byte of the reference hash key  210  to zero. Alternatively, for example, if the reference hash key  210  is 64-bits long, the hash key modifier  206  may set the two least significant bits of each byte of the reference hash key  210  to zero. For example, if the reference hash key  210  is 0x 37 01 D2 02 2B 3D 5D 76 and if the least significant bit of each byte are set to zero, the blurred reference hash key is 0x 36 00 D2 02 2A 3C 5C 76. As another example, if the reference hash key  210  is 0x 37 01 D2 02 2B 3D 5D 76 and if the two least significant bits of each byte are set to zero, the blurred reference hash key is 0x 34 00 D0 00 28 3C 5C 74. By applying the blur function, the example hash key modifier  206  makes the blurred reference hash key  214  less precise than the reference hash key  210 , but also makes the blurred reference hash key  214  more error tolerant than the reference hash key  210 . 
     The example reference generator  208  receives or retrieves the blurred reference hash keys  214  and the reference confirmation data  212 . The example reference generator  208  generates the example reference records  202  that associate the blurred reference hash key  214  to corresponding reference media metadata  216  and the corresponding reference confirmation data  212 .  FIGS.  3 A and  3 B  illustrate examples of the reference records  202  stored in the reference database  128 . In the example illustrated in  FIG.  3 A , the hash key modifier  206  does not apply the blurring function to the reference hash key  210 . As such, the example reference records  202  has a one-to-one relationship between one of the reference hash keys  210 , one set of reference metadata  216  (e.g., a media ID, a station ID, a station call sign, a timestamp corresponding to a portion of the media  104 , etc.) and the confirmation datum  212 . 
     In the example illustrated in  FIG.  3 B , the hash key modifier  206  applies the blurring function to the reference hash key  210 . As such, in the illustrated example, a single blurred reference hash key  214  can be associated with multiple pairs of reference metadata  216  and confirmation data  212 . For example, the blurred reference hash key  214  of “0xF8 00 D0 0A” may be associated with (a) the pair of reference metadata  216  and confirmation data  212  including “KBLR, 2015-01-12T11:04:59Z” and “0xC7 43 9D A2” respectively, and (b) the pair of reference metadata  216  and confirmation data  212  including “KGLA, 2015-10-26T12:46:35Z” and “0xB0 F2 44 68” respectively. In the example illustrated in  FIG.  3 B , the number of least significant bits blurred by the hash key modifier  206  and the number of bits in the reference hash key  210 , increase a likelihood that multiple portions of the media  104  will have a reference hash key  210  that, when blurred, corresponds to the same blurred reference hash key  214 . 
     While an example manner of implementing the example reference hash key generator  130  of  FIG.  1    is illustrated in  FIG.  2   , one or more of the elements, processes and/or devices illustrated in  FIG.  2    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example hybrid hash key generator  204 , the example hash key modifier  206 , the example reference generator  208  and/or, more generally, the example reference hash key generator  130  of  FIG.  1    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example hybrid hash key generator  204 , the example hash key modifier  206 , the example reference generator  208  and/or, more generally, the example reference hash key generator  130  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example hybrid hash key generator  204 , the example hash key modifier  206 , and/or the example reference generator  208  is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example reference hash key generator  130  of  FIG.  1    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  2   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
       FIG.  4    illustrates an example diagram that depicts an example manner of how the example reference records  202  of  FIGS.  2 ,  3 A and/or  3 B  may be generated (e.g. by the reference hash key generator  130  of  FIGS.  1  and  2   ). The example illustrated in  FIG.  4    depicts a data stream  402  that includes the samples of the reference media  132  ( FIGS.  1  and  2   ) that are analyzed by the reference hash key generator  130 . The illustrated example also depicts the media metadata  216  chronologically corresponding to the data stream  402 . In some examples, the data stream  402  may be stored in a buffer of the hybrid hash key generator  204  ( FIG.  2   ). In the illustrated example, the metadata  216  includes a media source identifier  404 , a date  406 , and timestamps  408 . The example media source identifier  404  is a value (e.g., a call sign, a television channel number, a radio station tuning frequency, a media stream URL, etc.) that identifies the entity (e.g., broadcaster, streaming media service, producer, etc.) that is making the media  104  available. The example hybrid hash key generator  204  analyzes the samples of the reference media  132  and produces the example data stream  402  which includes hashed values of the samples. 
     The example hybrid hash key generator  204  selects a first portion  410  of the data stream  402  corresponding to a timestamp  408  of interest to be a reference hash key  210  ( FIG.  2   ). For example, to generate a reference hash key  210  corresponding to a first time, the hybrid hash key generator  204  may select the first portion  410  having a value of 0xF9 00 D1 0E corresponding to the timestamp  408  of 14:06:52. As another example, to generate a reference hash key  210  corresponding to a second time, the hybrid hash key generator  204  may select an additional first portion  412  with a value of 0xA6 7F D7 F1 corresponding to the timestamp  408  of 14:06:53. In the illustrated example, the hash key modifier  206  ( FIG.  2   ) applies the blurring function  414  to transform the reference hash key  210  into the blurred reference hash key  214 . 
     In the illustrated example, the hybrid hash key generator  204  selects a second portion  416  of the example data stream  402  to be the reference confirmation data  212 . The example location of the second portion  416  in the data stream  402  is determined by an offset  418  and a size  420 . The example offset  418  is a value, in bits, that defines the location of the second portion  416  relative to the first portion  410 . For example, an offset of sixteen would locate the start of the second portion  416  sixteen bits (two bytes) of the data stream  402  chronologically after the first portion  410 . In some examples, the offset  418  may be negative. For example, if the offset  418  is negative sixteen, the sixteen bits (two bytes) of the first portion  410  would be included in the second portion  416 . The example size  420  defines a quantity of bits that are included in the second portion  416 . In some examples, the size  420  of the second portion  416  is a percentage (e.g., 25%, 50%, etc.) of the size of the first portion  410 . For example, if the size  420  of the second portion  416  is 25% of the size of the first portion  410 , and the first portion  410  includes 40 bits, the size  420  of the second portion  416  would be 10 bits. Alternatively, in some examples, the size  420  of the second portion  416  is a multiple (e.g., 1.25, 1.5, 2, etc.) of the size of the first portion  410 . For example, if the size  420  of the second portion  416  is 1.5 times the size of the first portion  410  and the first portion  410  includes 40 bits, the size  420  of the second portion  416  would be 60 bits. In the illustrated example, the example reference generator  208  ( FIG.  10   ) generates the reference records  202  by associating the blurred reference hash key  214 , the reference metadata  216  corresponding to the blurred reference hash key  214 , and the reference confirmation data  212 . 
       FIG.  5    illustrates an example implementation of the example hash key identifier  124  of  FIG.  1    which may be used to compare the exposure records  108  with the reference records  202  to generate a monitoring report and/or to store impressions in the monitoring database  126 . The hash key identifier  124  of the illustrated example includes an example hybrid hash key analyzer  502 , an example error handler  504 , and an example impression logger  506 . The example hybrid hash key analyzer  502  of the illustrated example retrieves the exposure records  108  from the example metering database  122  Initially, to generate an impression, the example hybrid hash key analyzer  502  queries the reference database  128  for the reference record(s)  202  that include(s) the reference hash keys  210  ( FIGS.  2  and  3 A ) and/or the blurred reference hash keys  214  ( FIGS.  2 ,  3 B, and  4   ) that match the metered hash key  114  ( FIG.  1   ) of the exposure record  108 . 
     In the illustrated example, the hybrid hash key analyzer  502  compares the metered confirmation data  116  ( FIG.  1   ) corresponding to the metered hash key  114  to the reference confirmation data  212  ( FIGS.  2 ,  3 A,  3 B, and  4   ) of the retrieved reference record(s)  202 . In some examples in which the metered hash key  114  is blurred (e.g., by the meter  100  of  FIG.  1   ) and the blurred reference hash key  214  is blurred, the query of the reference database  128  may return more than one reference record  202 . Also, in some examples, the query of the reference database  128  may return more than one reference record  202  because the samples of more than one of the media  104  ( FIG.  1   ) may, by coincidence, produce the same reference hash key  210 . If the metered confirmation data  116  matches the reference confirmation data  212  of one of the retrieved reference records  202 , the example hybrid hash key analyzer  502  sends the example exposure record  108  and the corresponding reference record  202  to the example impression logger  506 . 
     In the illustrated example, if the metered confirmation data  116  does not match the reference confirmation data  212  of one of the retrieved reference records  202 , the error handler  504  determines an error level between the metered confirmation data  116  and the reference confirmation data  212  of each of the retrieved reference record  202 . In some examples, to generate the error level (e), the error handler  504  performs a bitwise comparison (e.g., a bitwise exclusive OR, etc.) between the metered confirmation data  116  and the reference confirmation data  212  using Equation 1 below.
 
 e =BitCount( C   m   ⊕C   r )   Equation 1
 
     In Equation 1 above, C m  is the metered confirmation data  116 , C r  is the reference confirmation data  212 , and the BitCount( ) function returns the number of ones in a binary number. For example, as shown in Table 1 below, if the metered confirmation data  116  is 0xA6 00 85 69 and if the reference confirmation data  212  is 0xA2 10 85 E9, the error level (e) is 3 (BitCount(0xA6008569⊕0xA21085E9)=3) because two bit positions have non-matching values. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 EXAMPLE ERROR LEVEL (e) CALCULATION 
               
            
           
           
               
               
               
            
               
                   
                 Hexadecimal 
                 Binary 
               
               
                   
               
            
           
           
               
               
               
            
               
                   
                 0xA6008569 
                 1010 0110 0000 0000 1000 0101 0110 1001 
               
               
                 ⊕ 
                 0xA21085E9 
                 1010 0010 0001 0000 1000 0101 1110 1001 
               
               
                   
                 0x04100080 
                 0000 0100 0001 0000 0000 0000 1000 0000 
               
               
                   
               
            
           
         
       
     
     The example error handler  504  selects one of the retrieved reference records  202  corresponding to the corresponding reference confirmation data  212  having an error level that is the smallest of the calculated error levels that is less than an error threshold. The example error level is indicative of the number of bits that are different between the reference confirmation data  212  and the metered confirmation data  116 . In some examples, the error threshold is be set to a percentage (e.g. 5%, 10%, etc.) of the bit length of the metered hash key  114 . For example, an error threshold of 4 bits may be selected for a 40-bit metered hash key  114 . Table 2 below illustrates an example of reference confirmation data  212  and the associated error levels (e). 
                     TABLE 2                  EXAMPLE ERROR LEVELS (e) CALCULATED FOR EXAMPLE       REFERENCE RECORDS                         0x6B BE 95 F0                                 Error Level       Metered Confirmation Data   Reference Confirmation Data   (e)               First Reference Record   0x7B BB 95 F0   2 bits       Second Reference Record   0x6F BE 9D D8   4 bits       Third Reference Record   0x9C 28 71 A3   19 bits                     
In the example illustrated in Table 2 above, the error handler  504  would select the First Reference Record because the Error Level (e) for the First Reference Record is the lowest error level.
 
     In the illustrated example of  FIG.  5   , the impression logger  506  retrieves or otherwise receives the exposure record  108  and the selected reference record  202  from the example hybrid hash key analyzer  502 . The impression logger  506  creates an impression record  508  by associating the meter ID  118  and the timestamp  120  of the exposure record  108  with the reference metadata  216  of the reference record  202 . In the illustrated example, the impression logger  506  stores the impression record  508  into the monitoring database  126 . In some examples, the example impression logger  506  credits the portion of the media represented by the reference metadata  216 . In some such examples, to assign credit to the portion of the media represented by the reference metadata  216 , the example impression logger  506  stores a value, sets a flag, and/or stores a tag in association with the impression record indicative of the portion of the media being exposed to the household represented by the meter ID  118 . 
     While an example manner of implementing the example hash key identifier  124  of  FIG.  1    is illustrated in  FIG.  5   , one or more of the elements, processes and/or devices illustrated in  FIG.  5    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example hybrid hash key analyzer  502 , the example error handler  504 , the example impression logger  506 , and/or, more generally, the example hash key identifier  124  of  FIG.  1    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example hybrid hash key analyzer  502 , the example error handler  504 , the example impression logger  506 , and/or, more generally, the example hash key identifier  124  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example hybrid hash key analyzer  502 , the example error handler  504 , and/or the example impression logger  506  is/are hereby expressly defined to include a tangible computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. storing the software and/or firmware. Further still, the example hash key identifier  124  of  FIG.  1    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  5   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowcharts representative of example machine readable instructions for implementing the hash key identifier  124  of  FIGS.  1  and  5    are shown in  FIGS.  6  and  7   . A flowchart representative of example machine readable instructions for implementing the reference hash key generator  130  of  FIGS.  1  and  2    is shown in  FIG.  8   . In this example, the machine readable instructions comprise programs for execution by a processor such as the processor  912  shown in the example processor platform  900  discussed below in connection with  FIG.  9   . The program may be embodied in software stored on a tangible computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-ray disk, or a memory associated with the processor  912 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  912  and/or embodied in firmware or dedicated hardware. Further, although the example programs are described with reference to the flowcharts illustrated in  FIGS.  6 ,  7   , and/or  8 , many other methods of implementing the example hash key identifier  124  and/or the example reference hash key generator  130  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. 
     As mentioned above, the example processes of  FIGS.  6 ,  7   , and/or  8  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a tangible computer readable storage medium such as a hard disk drive, a flash memory, a read-only memory (ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, a random-access memory (RAM) and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term tangible computer readable storage medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, “tangible computer readable storage medium” and “tangible machine readable storage medium” are used interchangeably. Additionally or alternatively, the example processes of  FIGS.  6 ,  7   , and/or  8  may be implemented using coded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. 
       FIG.  6    is a flow diagram of example machine readable instructions that may be executed to implement the hash key identifier  124  of  FIGS.  1  and/or  5    to compare the exposure records  108  ( FIGS.  1  and  5   ) to the reference records  202  ( FIGS.  2 ,  3 A,  3 B, and  4   ). In the example process of  FIG.  6   , the metered hash keys  114  ( FIGS.  1  and  5   ) and the reference hash keys  210  ( FIGS.  2    and  3 A) are not blurred. Initially, at block  602 , the hybrid hash key analyzer  502  ( FIG.  5   ) retrieves the next metered exposure record  108  from the metering database  122  ( FIGS.  1  and  5   ). At block  604 , the hybrid hash key analyzer  502  determines whether the metered hash key  114  of the metered exposure record  108  retrieved at block  602  corresponds to one of the reference records  202  in the reference database  128 . For example, the example hybrid hash key analyzer  502  may query the reference database  128  using the metered hash key  114 . In such examples, if the reference database  128  returns a reference record  202 , the hybrid hash key analyzer  502  determines that the metered hash key  114  does substantially match or correspond to a reference record  202  in the reference database  128 . If the metered hash key  114  corresponds to one of the reference records  202 , program control advances to block  605 . Otherwise, if the metered hash key  114  does not correspond to one of the reference records  202 , program control advances to block  610 . At block  605 , the hybrid hash key analyzer  502  accesses the reference record  202  from the reference database  128  corresponding to the metered hash key  114 . 
     At block  606 , the example error handler  504  ( FIG.  5   ) determines whether the metered confirmation data  116  ( FIG.  1   ) of the metered exposure record  108  retrieved at block  602  matches the reference confirmation data  212  ( FIGS.  2 ,  3 A,  3 B, and  4   ) of the reference record  202  accessed at block  605 . In some examples, the error handler  504  performs a bitwise comparison of the metered confirmation data  116  and the reference confirmation data  212  to generate an error level (e). In some such examples, the error handler  504  determines that the metered confirmation data  116  matches the reference confirmation data  212  if the error level (e) satisfies (e.g., is less) than an error threshold. If the metered confirmation data  116  matches the reference confirmation data  212 , program control advances to block  608 . Otherwise, if the metered confirmation data  116  does not match the reference confirmation data  212 , program control advances to block  610 . 
     At block  608 , the example impression logger  506  ( FIG.  5   ) generates an impression record based on the metered exposure record  108 . For example, the impression logger  506  associates the meter ID  118  ( FIG.  1   ) corresponding to the metered exposure record  108  with the reference metadata  216  of the reference record  202 . At block  610 , the example impression logger  1306  indicates that the metered exposure record  108  is erroneous. In some examples, the example impression logger  506  marks (e.g., sets a flag, etc.) the metered exposure record  108  as erroneous so that the metered exposure record  108  is not used to generate an impression record (e.g., the impression record  508  of  FIG.  5   ). Alternatively, in some examples, the impression logger  506  discards the metered exposure record  108 . At block  612 , the hybrid hash key analyzer  502  determines whether there is another exposure record  108  to analyze. If there is another metered exposure record  108  to analyze, program control returns to block  602  to retrieve the next exposure record  108 . Otherwise, if there is not another metered exposure record  108  to analyze, the example program of  FIG.  6    ends. 
       FIG.  7    is a flow diagram of example machine readable instructions that may be executed to implement the hash key identifier  124  of  FIGS.  1  and/or  5    to compare metered hash keys  114  ( FIG.  1   ) corresponding to the metered exposure records  108  ( FIGS.  1  and  5   ) to blurred reference hash keys  214  ( FIGS.  2 ,  3 B, and  4   ) in the reference database  128  ( FIG.  1   ). Initially, at block  702 , the hybrid hash key analyzer  502  ( FIG.  5   ) retrieves the next metered exposure record  108  from the metering database  122  ( FIG.  1   ). At block  704 , the hybrid hash key analyzer  502  determines whether the metered hash key  114  of the metered exposure record  108  retrieved at block  702  corresponds to one of the reference records  202  in the reference database  128 . For example, the example hybrid hash key analyzer  502  may query the reference database  128  using the metered hash key  114 . In such examples, if the reference database  128  returns a reference record  202 , the hybrid hash key analyzer  502  determines that the metered hash key  114  does substantially match or correspond to a reference record  202  of the reference database  128 . If the metered hash key  114  corresponds to one of the reference records  202 , program control advances to block  705 . Otherwise, if the metered hash key  114  does not correspond to one of the reference records  202 , program control advances to block  714 . At block  705 , the hybrid hash key analyzer  502  accesses the reference record  202  from the reference database  128  corresponding to the metered hash key  114 . 
     Because the blurred reference hash key  214  accessed at block  705  may be associated with more than one portion of the media  104  and/or portion(s) of different media, the reference record  202  accessed at block  705  may be associated with multiple candidate reference confirmation data-reference metadata pairs ((e.g., the metadata  216  and the reference confirmation data  212  of  FIG.  4   ). At block  706 , the example error handler  504  retrieves the next candidate reference confirmation data-reference metadata pair. 
     At block  708 , the example error handler  504  determines whether the metered confirmation data  116  corresponding to the metered exposure record  108  retrieved at block  702  matches the candidate reference confirmation data  212  retrieved at block  706 . For example, the error handler  504  may perform a bitwise comparison between the metered confirmation data  116  of the metered exposure record  108  selected at block  702  and the candidate reference confirmation data  212  selected at block  706  to generate an error level (e). In such examples, the error handler  504  determines that the metered confirmation data  116  matches the candidate reference confirmation data  212  if the error level satisfies (e.g., is less than) an error threshold (e). If the metered confirmation data  116  matches the candidate reference confirmation data  212 , program control advances to block  710 . Otherwise, if the metered confirmation data  116  does not match the candidate reference confirmation data  212 , program control advances to block  712 . At block  710 , the example impression logger  506  ( FIG.  5   ) generates an impression record based on the metered exposure record  108 . For example, the impression logger  506  associates the meter ID  118  ( FIG.  1   ) of the metered exposure record  108  with the reference metadata  216  associated with the candidate reference confirmation data  212  determined to be matching at block  708 . Program control then advances to block  716 . 
     At block  712 , the example error handler  504  determines whether the reference record  202  retrieved at block  714  is associated with more candidate reference confirmation data  212 . If the reference record  202  is associated with more candidate reference confirmation data  212 , program control returns to block  706 . Otherwise, if the reference record  2002  is not associated with more candidate reference confirmation data  212 , program control advances to block  714 . 
     At block  714 , the example impression logger  506  indicates that the metered exposure record  108  is erroneous. In some examples, the example impression logger  506  marks (e.g., sets a flag, etc.) the metered exposure record  108  as erroneous so that the metered exposure record  108  is not used to generate an impression record (e.g., the impression record  508  of  FIG.  5   ). Alternatively, in some examples, the impression logger  506  discards the metered exposure record  108 . At block  716 , the hybrid hash key analyzer  502  determines whether there is another metered exposure record  108  to analyze. If there is another metered exposure record  108  to analyze, program control returns to block  702  to retrieve the next exposure record  108 . Otherwise, if there is not another metered exposure record  108  to analyze, the example program of  FIG.  7    ends. 
       FIG.  8    is a flow diagram of example machine readable instructions that may be executed to implement the reference hash key generator  130  of  FIGS.  1  and/or  2    to generate reference records  202  ( FIGS.  2 ,  3 A,  3 B, and  4   ). Initially, at block  802 , the hybrid hash key generator  204  ( FIG.  2   ) generates samples of the reference media  132  ( FIGS.  1 ,  2 , and  4   ). In some examples, the hybrid hash key generator  1004  continuously applies a hash function to the samples of the reference media  132  and places the hashed samples of the reference media  132  into, for example, a circular buffer. 
     At block  804 , the example hybrid hash key generator  204  selects a first portion (e.g., the first portion  410  of  FIG.  4   ) of the samples of the reference media  132 . At block  805 , the example hybrid hash key generator  204  generates a reference hash key  210  based on the first portion  410  of the samples of the reference media  132  selected at block  804 . For example, if the samples of the reference media  132  have a length of 8-bits and the reference hash key is to have a length of 40-bits, the hybrid hash key generator  204  selects the next five samples (e.g., sample N 0  through N 4 ) of the reference media  132  as the reference hash key  1010 . When the next reference hash key  210  is generated, the example hybrid hash key generator  204  selects five additional samples of the reference media  132 , some of which may overlap with the previously generated reference hash key  210 . For example, a first reference hash key (k)  210  may include samples N 10  through N 14 , and a second reference hash key (k+1)  210  may include samples N 11  through N 15 . 
     At block  806 , the example hybrid hash key generator  204  selects a second portion (e.g., the second portion  416  of  FIG.  4   ) of the samples of the reference media  132  to generate reference confirmation data  212  ( FIGS.  2 ,  3 A,  3 B and  4   ). The example hybrid hash key generator  204  selects the second portion  416  based on an offset (e.g., the offset  418  of  FIG.  4   ) and a size (e.g., the size  420  of  FIG.  4   ) set by the AME  102  ( FIG.  1   ). For example if the offset  416  is −16 bits (−2 bytes), the size  420  is five bytes, and the reference hash key  210  was generated from samples N 12  through N 16 , the confirmation data  212  is generated using samples N 8  through N 13 . 
     At block  808 , the example hash key modifier  204  ( FIG.  2   ) determines whether to apply the blurring function to the reference hash key  210  generated at block  805 . If the example hash key modifier  204  is to apply the blurring function to the reference hash key  210 , program control advances to block  812 . Otherwise, if the example hash key modifier  204  is not to apply the blurring function to the reference hash key  210 , program control advances to block  810 . At block  810 , the example reference generator  208  ( FIG.  2   ) generates a reference record  202  by associating the reference hash key  210  generated at block  805  with (i) reference metadata  216  corresponding to the first portion of the reference media  132  obtained at block  804  to generate the reference hash key  210 , and (ii) the reference confirmation data  212  selected at block  806 . For example, the reference hash key  210  may correspond to a station with the call sign WSNS, a date of Sep. 18, 2015, and a timestamp of 14:06:52.0825. 
     At block  812 , the example hash key modifier  204  applies the blurring function to the reference hash key  210  to generate a blurred reference hash key  214  ( FIGS.  2 ,  3 B and  4   ). To apply the blurring function in the illustrated example, the example hash key modifier  204  sets a number of the least significant bits of each byte of the reference hash key  210  to zero. For example, if the reference hash key  210  is 0xC3 41 D2 52 (binary: 11000011 01000001 11010010 01010010) and the two least significant bits of each byte are set to zero by the blurring function, the blurred reference hash key  214  is 0xC0 40 D0 50 (binary: 11000000 01000000 11010000 01010000). Alternatively, if only the least significant bit of each byte is set to zero by the blurring function, the blurred reference hash key  214  is 0xC2 40 D2 52 (binary: 11000010 01000000 11010010 01010010). At block  814 , the example reference generator  208  generates a reference record  202  by associating the blurred reference hash key  214  generated at block  812  with (i) reference metadata  216  corresponding to the first portion of the reference media  132  used to generate the reference hash key  210 , and (ii) the reference confirmation data  212  selected at block  806 . 
     At block  816 , the example hybrid hash key generator  204  determines whether another reference record  202  is to be generated. For example, if all the reference hash keys  210  for the reference media  132  have been generated (e.g., the hybrid hash key generator  204  has reached the end of the reference media  132 ), the hybrid hash key generator  204  determines that another record  202  is not to be generated. If another reference record  202  is to be generated, program control returns to block  804 . Otherwise, if another reference hash key  210  or blurred reference hash key  214  is not to be generated, the program ends. 
       FIG.  9    is a block diagram of an example processor platform  900  capable of executing the instructions of  FIGS.  6 ,  7  and/or  8    to implement the hash key identifier  124  of  FIGS.  1  and  5   , and/or the reference hash key generator  130  of  FIGS.  1  and  2   . The processor platform  900  can be, for example, a server, a personal computer, a workstation, or any other type of computing device. 
     The processor platform  900  of the illustrated example includes a processor  912 . The processor  912  of the illustrated example is hardware. For example, the processor  912  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. In the illustrated example, the processor  912  is structured to include the example hybrid hash key analyzer  502 , the example error handler  504 , and the example  505 . Additionally or alternatively, in some examples, the processor  912  is structured to include the example hybrid hash key generator  204 , the example hash key modifier  206 , and the example reference generator  208 . 
     The processor  912  of the illustrated example includes a local memory  913  (e.g., a cache). The processor  912  of the illustrated example is in communication with a main memory including a volatile memory  914  and a non-volatile memory  916  via a bus  918 . The volatile memory  914  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or any other type of random access memory device. The non-volatile memory  916  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  914 ,  916  is controlled by a memory controller. 
     The processor platform  900  of the illustrated example also includes an interface circuit  920 . The interface circuit  920  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a PCI express interface. 
     In the illustrated example, one or more input devices  922  are connected to the interface circuit  920 . The input device(s)  922  permit(s) a user to enter data and commands into the processor  912 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  924  are also connected to the interface circuit  920  of the illustrated example. The output devices  924  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display, a cathode ray tube display (CRT), a touchscreen, a printer). The interface circuit  920  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip or a graphics driver processor. 
     The interface circuit  920  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem and/or network interface card to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  926  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  900  of the illustrated example also includes one or more mass storage devices  928  for storing software and/or data. Examples of such mass storage devices  928  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     Coded instructions  932  of  FIGS.  6 ,  7  and/or  8    may be stored in the mass storage device  928 , in the volatile memory  914 , in the non-volatile memory  916 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will appreciate that examples have been disclosed which allow error-tolerant identification of metered hash keys produced from media sources that introduce noise into the metered hash keys. Additionally, examples have been disclosed which generate reference records that include information pertaining to additionally portions of a medium. Examples have been disclosed which increase the accuracy of impression data and reduce processing (e.g., reduce the burden on a semiconductor based processor) required to perform a match and/or to adjust for erroneous and/or missing impression data. Moreover, because erroneous hash keys can be identified efficiently, search time in a database to identify media is reduced. Reducing search time saves processing resources and reduces the energy consumption required to perform media monitoring. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent.