Patent Publication Number: US-11653062-B2

Title: Methods and apparatus to determine audio source impact on an audience of media

Description:
RELATED APPLICATION(S) 
     This patent is a continuation of U.S. patent application Ser. No. 17/170,480, filed on Feb. 8, 2021, now U.S. Pat. No. 11,350,164, entitled “METHODS AND APPARATUS TO DETERMINE AUDIO SOURCE IMPACT ON AN AUDIENCE OF MEDIA,” which is a continuation of U.S. patent application Ser. No. 16/820,334, filed on Sep. 24, 2018, now U.S. Pat. No. 10,917,691, entitled “METHODS AND APPARATUS TO DETERMINE AUDIO SOURCE IMPACT ON AN AUDIENCE OF MEDIA,” which is a continuation of U.S. patent application Ser. No. 16/140,238, filed on Sep. 24, 2018, now U.S. Pat. No. 10,917,691, entitled “METHODS AND APPARATUS TO DETERMINE AUDIO SOURCE IMPACT ON AN AUDIENCE OF MEDIA,” which claims the benefit of U.S. Provisional Application Ser. No. 62/660,755, filed on Apr. 20, 2018. Priority to U.S. patent application Ser. No. 16/820,334, U.S. patent application Ser. No. 16/140,238 and U.S. Provisional Application Ser. No. 62/660,755 is claimed. U.S. patent application Ser. No. 16/820,334, U.S. patent application Ser. No. 16/140,238 and U.S. Provisional Application Ser. No. 62/660,755 are hereby incorporated herein by reference in their respective entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to audience measurement, and, more particularly, to methods and apparatus to determine audio source impact on an audience of media. 
     BACKGROUND 
     Audience measurement of media (e.g., any type of content and/or advertisements, such as broadcast television and/or radio, stored audio and/or video played from a memory, such as a digital video recorder or digital video disc, a webpage, audio and/or video presented (e.g., streamed) via the Internet, a video game, etc.) often involves the collection of media identifying information (e.g., signature(s), fingerprint(s), code(s), tuned channel identification information, time of exposure information, etc.) and people data (e.g., user identifier(s), demographic data associated with audience members, etc.). The media identifying information and people data can be combined to generate, for example, audience ratings and other audience measurement metrics. In some examples, these metrics can track the changes in audience over the length of the media (e.g., minute-by-minute audience ratings). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic illustration of a system including an example speaker detector, an example speaker impact estimator and an example media verifier implemented in accordance with teachings of this disclosure to determine speaker impact on an audience of media. 
         FIG.  2    is an example implementation of the speaker detector of  FIG.  1   . 
         FIG.  3    is an example set of speaker identification data produced by the speaker detector of  FIG.  1   . 
         FIG.  4    is an example implementation of the speaker impact estimator of  FIG.  1   . 
         FIGS.  5 - 7    are flowcharts representative of example machine readable instructions that may be executed to implement the example system of  FIG.  1   . 
         FIG.  8    is a block diagram of an example processing system structured to execute the example machine readable instructions of  FIGS.  5 - 7    to implement the example system of  FIG.  1   . 
     
    
    
     DETAILED DESCRIPTION 
     Examples disclosed herein determine the impact of audio sources (e.g., on-air speaker, notable sounds, etc.) on media monitoring metrics. Some examples disclosed herein can also verify the identity of media based on the audio sources identified in the media. Some examples disclosed herein use machine learning techniques to generate confidence values for one or more audio source(s) of note being present in a segment of media and then determine the impact of that combination of audio sources on minute-by-minute audience ratings. Examples disclosed herein use a training library of samples of speakers and/or other reference sounds that can be used to train a speaker evaluator. In some examples disclosed herein, the speaker evaluator uses logistic regression and gradient descent techniques to train the speaker evaluator to detect particular speaker(s) and/or other references sound(s) in monitored media. In some examples disclosed herein, the dominant speaker in a given segment of the media is also identified. 
     Example disclosed herein correlate detected speakers in the media with audience ratings data to determine the speaker impact on the audience ratings data. In some examples, the impact of speakers (and/or other sources of sound) on other media monitoring measurements metrics (e.g., audience retention, audience churn, audience tuning, etc.) is also determined. Examples disclosed herein compare the confidence levels generated by the speaker evaluator to one or more thresholds to detect different combinations of speakers present in different segments of the media. Additionally or alternatively, examples disclosed herein also can be implemented to identify and/or verify the identity of media. In some examples disclosed herein, the detected speaker(s) can be compared with a known (or reference) list of speakers (e.g., a cast list) in monitored media to verify the identity of the media. In some examples disclosed herein, other notable sounds (e.g., sounds of a basketball dribbling) can be used to determine the impact of the things associated with those sounds (e.g., a basketball game) on audience ratings. 
       FIG.  1    is a schematic illustration of a system  100  that may be implemented in accordance with teachings of this disclosure. The system  100  includes an example speaker detector  104 , an example media verifier  108 , an example speaker impact estimator  122 , an example voice to text converter  112  and an example sentiment analyzer  118 . In the illustrated example, the system  100  also includes an example closed captioning database  114 , an example social media database  116 , an example media exposure database  120  and an example training sample database  110 . The system  100  receives and processes example monitored media  102 . 
     As used herein, the term “media” includes any type of content and/or advertisement delivered via any type of distribution medium. Thus, media includes television programming or advertisements, radio programming or advertisements, movies, web sites, streaming media, etc. 
     The example monitored media  102  includes audio data to be processed by the system  100 . In some examples, the monitored media  102  is digitized in such a way to be readable by the system  100 . In the illustrated example, the monitored media  102  contains discernable voices of speakers who have reference voice samples stored in the training sample database  110 . Additionally or alternatively, the monitored media  102  may contain one or more notable sound(s) (e.g., a sound of an automobile engine, a sound of baby crying, etc.) that have related samples stored in the training sample database  110 . In some examples, the audio/video media may be stored as digital data (e.g., an .mp3 file, etc.), contained in physical media (e.g., a CD, etc.) or maybe a live event (e.g., a radio broadcast). 
     The example speaker detector  104  processes the monitored media  102  to detect at least one speaker in monitored media  102 . In some examples, the speaker detector  104  uses machine learning techniques to detect the speaker(s). In some examples, the speaker detector  104  uses and develops the training sample database  110 . Additionally or alternatively, the speaker detector  104  may identify notable non-speaker sounds in the monitored media  102  using any suitable method, such as the machine learning techniques disclosed herein. In some examples, the speaker detector  104  divides the monitored media  102  into segments and/or time intervals. An example implementation of the speaker detector  104  is described below in connection with  FIG.  2   . 
     The example speaker detector  104  outputs speaker identification data  106 . In some examples, the speaker identification data  106  includes time-variant confidence values indicating the probability of one or more speakers speaking (e.g., present) at different times and/or time intervals over the length of the monitored media  102 . For example, the speaker identification data  106  may identify the time and/or duration of when each speaker of interest (e.g., known reference speaker in the database  110 , etc.) is detected as speaking, and/or detecting when one or more speakers are speaking (e.g., present) during different time intervals of the monitored media  102 . Additionally or alternately, the speaker identification data  106  may identify the dominant speaker during different time intervals of the monitored media  102 . In some examples, the speaker identification data  106  may include a time-confidence value plot for each reference speaker of interest (e.g., for which training data is available in the database  110 ) in the monitored media  102 . Additionally or alternatively, the speaker identification data  106  may include identification data of any other reference sounds of interest (e.g., an automobile engine, a crying of baby, etc. having associated samples in the database  110 ) in the monitored media  102 . In some examples, the speaker identification data  106  may be used to generate, adjust and/or verify the accuracy of the closed captions associated with the monitored media  102  (e.g., adjusting the closed captioning time delays). 
     The example media verifier  108  compares the speaker identification data  106  to a known (reference) list of speakers (e.g., a cast list) for the media. For example, if the monitored media  102  is suspected to be a movie, the speakers in the monitored media  102 , as detected by the speaker detector  104 , may be compared to the cast list of that movie. Alternatively or additionally, the media verifier  108  may instead perform a search of a database of reference lists of speakers for a library of reference media using the detected speakers identified in the speaker identification data  106 . In some examples, the media verifier  108  may be used to correctly credit audience ratings to media events in situations where the identity of the monitored media  102  is uncertain (e.g., a television program may be interrupted by breaking news, a sporting event goes later than scheduled, etc.). Alternatively or additionally, the media verifier  108  may be used on other identified sounds to verify the monitored media  102  (e.g., the sound of a basketball dribbling may be used to identify the monitored media  102  as a basketball game). 
     The example training sample database  110  includes recorded audio samples of the speakers and/or sounds of interest to be detected in the monitored media  102 . For example the training sample database  110  may be populated by manually tagging audio clips to identify the speakers of interest (also referred to as reference speakers) and/or other notable sound (also referred to as reference sounds) in the training sample database  110 . 
     The example closed captioning database  114  and/or the example voice to text converter  112  may be used to determine which words are being said by speaker(s) in the monitored media  102 . For example, the closed captioning database  114  may be referenced to determine which words are being spoken in a given time interval of the monitored media  102 . In some examples, the example closed captioning database  114  is built by receiving closed captioning data from the broadcaster of the monitored media  102 . In some examples, the closed captioning database  114  may be generated by subtitles included in the monitored media  102  instead, or in addition to, the closed captioning data. Additionally or alternatively, the voice to text converter  112  may be used to directly recognize and convert spoken words in the monitored media  102  into text. 
     The social media database  116  includes social media messages (e.g., tweets, Facebook posts, video comments, etc.) related to the monitored media  102 . For example, the operator of the system  100  may receive bulk comments from a social media provider and populate the database. In some examples, the social media database  116  is generated by scraping a social media web site for keywords associated with the monitored media  102 . Any suitable means of collecting social media messages may be used to populate the social media database  116 . 
     In the illustrated example, the sentiment analyzer  118  is used to analyze text for sentiment and receives the speaker identification data  106  from the speaker detector  104 . For example, the sentiment analyzer  118  may receive the text transcripts of audio of the monitored media  102 , as received from the closed captioning database  114  and/or voice to text converter  112  and analyze them for sentiment. In some examples, the sentiment analyzer  118  may identify dialogue in the text transcript and correlate them with speaker identification data  106 . In some examples, the sentiment analyzer  118  can associate different portions of dialogue in the text transcript with different time intervals of the speaker identification data  106 . Additionally or alternatively, the sentiment analyzer  118  may also analysis relevant social media messages for sentiment. In some examples, the determined social media sentiment may be used to determine how people view speakers in the monitored media  102 . In some examples, the sentiment analyzer  118  may use natural language processing techniques and/or other known sentiment analysis methods. 
     The example media exposure database  120  includes rating metrics relevant to the monitored media  102 . For example, the media exposure database  120  may be populated by an audience measurement entity (e.g., The Nielsen Company (US), LLC.). In some examples, the media exposure database  120  may include audience rating metrics, such as the minute-by-minute audience ratings and/or second-by-second audience ratings, etc., of the monitored media  102 . Additionally or alternatively, the media exposure database  120  may include other metrics, such as audience turnover, etc. 
     The example speaker impact estimator  122  compares the speaker identification data  106  and the ratings data obtained from the media exposure database  120  to determine speaker impact on audience ratings. In some examples, the speaker impact estimator  122  may use sentiment analysis provided by the sentiment analyzer  118  to further characterize speaker impact on audience ratings. In some examples, the speaker impact estimator  122  may compare the minute-by-minute audience ratings of the monitored media  102  to the minute-by-minute speaker identification data  106  to determine how a detected speaker or combination of speakers affect audience viewership of the monitored media  102 . For example, the speaker impact estimator  122  can determine if audience viewership decreases when a particular speaker is the dominate speaker (e.g., a news program switches to an unpopular reporter which causes viewers to turn off the news program). In some examples, additional insight can be gathered by combing the sentiment analysis provided by the sentiment analyzer  118  with the comparison of the speaker identification data  106  with the ratings of the monitored media  102 . For example, the speaker impact estimator  122  may identify if a viewership of the monitored media  102  increases when a certain speaker or object is present (e.g., the viewership of a fantasy program increases when a dragon is on screen) and may indicate potential reasons for the viewership to change based on the sentiment analysis. In some examples, the speaker impact estimator  122  can determine the speaker impact on other metrics, such as audience retention, audience churn, audience tuning, etc. 
       FIG.  2    is an example implementation of the speaker detector  104  of  FIG.  1   . In the illustrated example, the speaker detector  104  includes an example preprocessor  202 , an example audio segmenter  204 , an example dominant speaker identifier  208 , an example speaker evaluator  206  and an example data generator  216 . In the illustrated example, the speaker evaluator  206  includes an example speech pattern identifier  210 , an example pattern comparator  212  and an example confidence value plotter  214 . The example speaker detector  104  receives the monitored media  102  and outputs the speaker identification data  106 . 
     The preprocessor  202  prepares the monitored media  102  for analysis. For example, if the monitored media  102  is a video, the preprocessor  202  may extract the audio from the video file/stream data. In some examples, the preprocessor  202  may digitize the audio of the monitored media  102 . In some examples, the preprocessor  202  may convert the monitored media  102  into a different format that can be processed. In some examples, the preprocessor  202  may convert the monitored media  102  from the time domain to the frequency domain (e.g., using the Fourier transform). In some examples, the preprocessor  202  may filter out extraneous audio from the media and/or adjust the audio spectrum. 
     The audio segmenter  204  divides the audio of the monitored media  102  into segments. For example, the audio segmenter  204  may divide the audio of the monitored media  102  into overlapping segments of a predetermined length. For example, the audio segmenter  204  may divide 5 minutes of audio into ten 35 second segments that overlap adjacent segments by 5 seconds. In some examples, the audio segmenter  204  may further divide audio segments into smaller segments. For example, the audio segmenter  204  may divide each of the ten 35 second segments into two 18 second segments which overlap by 1 second. In some examples, the segments created by the audio segmenter  204  do not overlap. In some examples, the audio segmenter  204  divides the audio into segments of the same length as the resolution of available audience ratings (e.g., if the audience ratings are minute-by-minute or second-by-second, the audio segmenter  204  divides the audio into minute or second long segments, respectively). 
     The speaker evaluator  206  processes the audio segments generated by the audio segmenter  204  and detects which speakers may be speaking during that segment. For example, the speaker evaluator  206  may assign a confidence value for a particular speaker being present in a given audio segment. For example, speaker evaluator  206  implements a pattern detecting and matching learning algorithm which uses logistic regression to assign a confidence value (e.g., a probability) of speaker speaking in a given audio segment. In some examples, the speaker evaluator  206  implements a stochastic gradient descent (SGD) to match identified patterns in the monitored media  102  to known patterns in the samples of the training sample database  110 . In some examples, any suitable type and/or combination of machine learning technique(s) may be used to implement the speaker evaluator  206  (e.g., a neural network, etc.). 
     The example speech pattern identifier  210  of the example speaker evaluator  206  identifies patterns in the speech and/or non-speech sounds of the monitored media  102 . For example, the speech pattern identifier  210  may look for and identify patterns in word choice, pitch, tone or cadence of the audio. Additionally or alternatively, the speech pattern identifier  210  can analyze speech and/or non-speech sounds to identify patterns in word rate, amplitude, pauses between words and/or use of key words. In some examples, the speech pattern identifier  210  can identify certain non-speech sounds as a particular audio source (e.g., booing, clapping, cheering, a foley audio sound effect, etc.). In some examples, the speech pattern identifier  210  may identify any suitable patterns in the audio of the monitored media  102 . The pattern comparator  212  compares the patterns in the audio of the monitored media  102  to known patterns in the samples included in the training sample database  110  to determine the confidence values described above. 
     The confidence value plotter  214  records a confidence value (e.g., a probability) of a particular speaker of interest speaking (e.g., present) in each audio segment as determined by the speaker evaluator  206 . In some examples, the confidence value plotter  214  may record a discrete confidence value for each potential speaker for the audio segment. 
     The dominant speaker identifier  208  analyses the output of the speaker evaluator  206  to identify the dominant speaker of each segment. For example, if the output confidence values for multiple speakers exceed a threshold, the dominant speaker identifier  208  may identify the speaker with the highest confidence value as the dominant speaker. In some examples, the dominant speaker identifier  208  may use any suitable metric to identify the dominant speaker (e.g., the loudest speaker in the segment, etc.). 
     In the illustrated example, the data generator  216  receives data from the example dominant speaker identifier  208  and the example speaker evaluator  206  and outputs the speaker identification data  106 . For example, the data generator  216  can format the data into the table illustrated in  FIG.  3   . In some examples, the data generator  216  can instruct the audio segmenter  204  to further subdivide the monitored media  102  into smaller segments. In some examples, the data generator  216  can issue this instruction to correlate with the size of available audience ratings (e.g., those in the media exposure database  120  of  FIG.  1   ). Additionally or alternatively, the data generator  216  can issue this instruction for any suitable reason (e.g., a setting by a user, etc.). 
     While an example manner of implementing the speaker detector  104  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 the example preprocessor  202 , the example audio segmenter  204 , the example dominant speaker identifier  208 , the speaker evaluator  206 , the speech pattern identifier  210 , the pattern comparator  212  and the confidence value plotter  214  and/or, more generally, the example speaker detector  104  of  FIG.  2    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example the example preprocessor  202 , the example audio segmenter  204 , the example dominant speaker identifier  208 , the speaker evaluator  206 , the speech pattern identifier  210 , the pattern comparator  212  and the confidence value plotter  214  and/or, more generally, the example speaker detector  104  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, the example preprocessor  202 , the example audio segmenter  204 , the example dominant speaker identifier  208 , the speaker evaluator  206 , the speech pattern identifier  210 , the pattern comparator  212  and the confidence value plotter  214  and/or, more generally, the example speaker detector  104  is/are hereby expressly defined to include a non-transitory 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. including the software and/or firmware. Further still, the example speaker detector  104  of  FIG.  2    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.  3    is an example set of speaker identification data  106  produced by the speaker detector  104  of  FIG.  1   . The example speaker identification data  106  of  FIG.  3    is formatted as a table including columns  302  associated with time segments of the monitored media  102 , rows  304  associated with example reference speakers and/or notable sounds to be identified of the monitored media  102  and an example row  308  associated with dominate speakers and/or sounds in each of the time segments of the monitored media  102 . The speaker identification data  106  includes an example first confidence value  310 A, an example second confidence value  310 B and an example dominant speakers and/or sound identification  312 . In the illustrated example, each value in the speaker identification data  106  corresponds to a confidence value for the presence of a speaker and/or notable sound in the corresponding time segment. 
     The example columns  302  are associated with successive segments of the audio/video media  103  (e.g., as segmented by the audio segmenter  204 ). The example rows  306  are associated with the presence of a first speaker, a second speaker, a third speaker, a fourth speaker, a first notable sound, and a second notable sound. For example, if the monitored media  102  is a basketball program, the first speaker, the second speaker, the third speaker, and the fourth speaker can correspond to different sports broadcasters and the first notable sounds and the second notable sound can correspond with sports noises (e.g., a ball dribbling, shoes squeaking on the court, etc.). The example row  308  are associated with identified dominant speakers and/or sound in each time segment. 
     The first confidence value  310 A indicates a confidence of greater than ninety nine percent that first speaker is speaking during the first time segment. The second confidence value  310 B indicates a confidence of less than one percent that second notable sound is present in the second time segment. In the illustrated example, each identified dominant sound in the row  308  corresponds to the notable sound with the highest confidence in that time segment. For example, the example dominant speaker identification  312  indicates that the first speaker is the dominant speaker in the fourth segment as the first speaker is associated with a confidence value of 99% in that time segment and other notable sounds each have confidence value of less than 1%. In other examples, any other suitable method may be used to determine the dominant speaker in each of the columns  302 . 
       FIG.  4    is example implementation of the speaker impact estimator  122  of  FIG.  1   . The example speaker impact estimator  122  includes an example speaker data analyzer  402 , an example audience rating correlator  404 , and an example impact analyzer  406 . The example speaker impact estimator  122  receives speaker identification data  106  from the speaker detector  104 . In some examples, the speaker impact estimator  122  can also receive sentiment analysis of the monitored media  102  from the sentiment analyzer  118 . In some examples, the speaker impact estimator  122  can also receive audience ratings from the media exposure database  120  of  FIG.  1   . 
     The example speaker data analyzer  402  determines speaker identification information from the speaker identification data  106 . For example, the speaker data analyzer  402  can process the speaker identification data  106  to determine which speakers and/or notable sounds (e.g., speakers and sounds associated with the example rows  306  of  FIG.  3   , etc.) are present during each of the audio segments (e.g., the audio segments associated with the columns  302 ). In some examples, the speaker data analyzer  402  may compare the confidence value associated with a particular speaker in an audio segment to a threshold (e.g., a speaker is present in an audio segment if the confidence value exceeds 50%, etc.) to identify if any speaker, or a group of speakers, are present in an interval and, if so, identify the present speaker or group of speakers. In some examples, the speaker data analyzer  402  can use any suitable means to determine if a speaker and/or notable sound is present in an audio segment in the speaker identification data  106 . 
     The example audience rating correlator  404  correlates the speaker identification data  106  to audience rating and/or sentiment analysis results. For example, the audience rating correlator  404  can correlate the audience ratings (e.g., minute by minute audience ratings) of the monitored media  102  to the speaker identification data  106 . For example, the audience rating correlator  404  can use the output of the audience rating correlator  404  and the audience ratings to associate the particular speakers and/or notable sounds with a particular audience rating. For example, the audience rating correlator  404  may correlate changes in the minute-to-minute audience ratings to changes in the dominant speaker and/or combination of active speakers in a given segment of the audio. In some examples, the audience rating correlator  404  can relate certain phrases with changes in audience ratings (e.g., audience ratings decreasing could be correlated with a speaker saying “we will be right back after this short break,” etc.). In some examples, the audience rating correlator  404  can augment the minute-to-minute audience ratings to include the output of the sentiment analyzer  118 . For example, the audience rating correlator  404  can correlate a change in audience ratings with a change in one or more speaker&#39;s sentiment (e.g., a sport reporter becoming excited could be correlated with an increase in audience ratings, etc.). 
     The impact analyzer  406  determines the impact of speakers on media monitoring metrics. For example, the impact analyzer  406  can, using the correlations determined by the audience rating correlator  404 , determine a particular dominant speaker and/or combination of speakers is associated with a decrease or increase in audience ratings. Additionally or alternatively, the impact analyzer  406  can use other identified sounds to determine the impact of speakers on media monitoring metrics (e.g., determining that audience rates drop when coverage of basketball games switches to a reporter). 
     Flowcharts representative of example machine readable instructions for implementing the system  100  of  FIG.  1    are shown in  FIGS.  5 - 7   . In this example, the machine readable instructions comprise one or more programs for execution by a processor such as the processor  812  shown in the example processor platform  800  discussed below in connection with  FIG.  8   . The program(s) may be embodied in software stored on a non-transitory 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  812 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  812  and/or embodied in firmware or dedicated hardware. Further, although the example program(s) are described with reference to the flowcharts illustrated in  FIGS.  5 - 7   , many other methods of implementing the example system  100  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. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, a Field Programmable Gate Array (FPGA), an Application Specific Integrated circuit (ASIC), a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. 
     As mentioned above, the example processes of  FIGS.  5 - 7    may be implemented using executable 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. 
     “Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc. may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, and (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. 
     The example process  500  of  FIG.  5    begins at block  502 . At block  502 , the speaker detector  104  receives the monitored media  102  For example, the speaker detector  104  may receive a live stream of television or radio broadcast. In other examples, the speaker detector  104  may instead receive a digital or physical recording of the media to be processed t. 
     At block  504 , the preprocessor  202  preprocesses the monitored media  102 . For example, the preprocessor  202  may extract audio from the monitored media  102 . In some examples, the preprocessor  202  may perform any appropriate operation(s) (e.g., changing the frequency spectrum, adjusting audio properties, changing file format, etc.) to the monitored media  102  to prepare the audio of the monitored media  102  for speaker identification analysis and speaker impact analysis. 
     At block  506 , the audio segmenter  204  divides the preprocessed audio into successive blocks. For example, the audio segmenter  204  may divide the audio into equally sized blocks of predetermined length. In some examples, time adjacent blocks overlap with one another. In some examples, the length of the blocks is chosen to be of the same length as available audience rating information. 
     At block  508 , the speaker evaluator  206  generates confidence values for each speaker of interest in each segmented audio block. For example, the speaker evaluator  206  may use logistic regression methods to determine the confidence values for each speaker of interest for each block. An example process to implement block  508  is discussed below in connection with  FIG.  6   . 
     At block  510 , the speaker detector  104  determines if a confidence value satisfies a threshold. For example, the speaker detector  104  compares the highest value of the confidence values generated by the speaker evaluator  206  to a threshold. In some examples, the threshold is indicative of a minimum confidence value reflecting that at least one speaker is actually speaking in the considered block. In some examples, each block of the segmented audio is considered individually. If a confidence value in at least one block satisfies the threshold, the process  500  advances to block  512 . If a confidence value in no block satisfies the threshold, the process  500  advances to block  516 . 
     At block  512 , the dominant speaker identifier  208  identifies the dominant speaker in each block with a confidence value that satisfies the threshold. For example, the dominant speaker identifier  208  may identify the speaker associated with the highest confidence value as the dominant speaker. In some examples, the dominant speaker identifier  208  may use any other suitable metric to determine the dominant speaker in the block. 
     At block  514 , the data generator  216  determines if the audio blocks are to be further subdivided. For example, if additional resolution (e.g., smaller blocks) are desired, the user may request further subdivision. If the blocks are to be subdivided, the process  500  advances to block  516 . If the blocks are not to be subdivided, the data generator  216  creates the speaker identification data  106  and the process  500  then advances to block  518 . 
     At block  516 , the audio segmenter  204  subdivides the audio block into smaller blocks. The process than returns to block  508 . At block  518 , the system  100  conducts postprocessing. Additional detail in the execution is described below in connection with  FIG.  7   . 
       FIG.  6    is a subprocess  600  that may be implemented by the speaker evaluator  206  to implement the processing at block  508  of  FIG.  5    and begins at block  602 . At block  602 , the speech pattern identifier  210  recognizes patterns in the audio block. For example, the speech pattern identifier  210  may use machine learning technique(s) to recognize patterns in the audio block. In some examples, these patterns include cadence, diction, tone, pitch, word choice (e.g., a catchphrase associated with a speaker), and/or frequency structures. In some examples, the speech pattern identifier  210  may recognize any suitable type of pattern. 
     At block  604 , the pattern comparator  212  compares recognized patterns to training samples. For example, the pattern comparator  212  may compare recognized patterns to samples from the training sample database  110 . In some examples, the pattern comparator  212  may use machine learning techniques to match patterns to determine the confidence values representing the likelihood of the reference speaker(s)/sound(s) associated with the sample patterns from the training sample database  110 . 
     At block  606 , the confidence value plotter  214  records confidence values for each speaker of interest for each block. For example, the confidence value plotter  214  may report a confidence value (e.g., a probability) for each speaker of interest in the training sample database  110  for each audio block. In some examples, the confidence value plotter  214  may also include a confidence value for a speaker not included in the training database. 
       FIG.  7    is a subprocess  700  that maybe implemented by the speaker impact estimator  122  and/or the media verifier  108  to implementing the processing at block  518  of  FIG.  5    and begins at block  702 . At block  702 , speaker impact estimator  122  determines if the speaker impact estimator  122  should perform speaker impact analysis. If speaker impact analysis is to be performed, the subprocess  700  advances to block  704 . If speaker is not to be performed, the subprocess  700  advances to block  708 . 
     At block  702 , speaker data analyzer  402  processes speaker identification information (e.g., the speaker identification data  106  of  FIG.  1   ) from dominant speaker identification and time variant speaker confidence values. For example, the speaker impact estimator  122  may use dominant speaker identification (e.g., as determined and recorded by the dominant speaker identifier  208 ) and the time variant speaker confidence value(s) (e.g., as created by the confidence value plotter  214 , or more generally, the speaker evaluator  206 ) to characterize each segment of monitored media  102 . In some examples, characterizing a segment of the monitored media  102  involves determining the nature of what is happening in that segment (e.g., a speaker is delivering a monologue, two characters are speaking, etc.). Additionally or alternatively, other identified sounds in the analyzed audio may be used to process the speaker identification information (e.g., sounds of a basketball dribbling may be used to identify the speaker as a sports reporter). The subprocess  700  then advances to block  706 . 
     At block  706 , the audience rating correlator  404  correlates the processed speaker identification data  106  to audience ratings and/or sentiment analysis results. For example, the speaker impact estimator  122  may correlate changes in the minute-to-minute audience ratings to changes in the dominant speaker and/or combination of active speakers in a given segment of the audio. Additionally or alternatively, the speaker impact estimator  122  may incorporate sentiment analysis into the correlation. The subprocess  700  advances to block  708 . At block  708 , the impact analyzer  406  determines the impact of speakers on media monitoring metrics. For example, the speaker impact estimator  122  may, using the correlations determined in block  708 , determine a particular dominant speaker and/or combination of speakers is associated with a decrease or increase in audience ratings. Additionally or alternatively, the speaker impact estimator  122  may use other identified sounds to determine the impact of speakers on media monitoring metrics (e.g., determining that audience rates drop when coverage of basketball games switches to a reporter). The subprocess  700  advances to block  710 . 
     At block  710 , the media verifier  108  determines if the media verifier  108  is to verify the identity of the media. If the media verifier  108  is to verify the media, the subprocess  700  advances to block  712 . If the media verifier  108  is not to verify the media, the subprocess  700  ends. 
     At block  712 , the media verifier  108  compares the identified speakers in the monitored media  102  with known speakers for given reference media content. For example, the media verifier  108  may compare the speaker identification for the monitored media to a cast list of a reference movie or television program. In some examples, the media verifier  108  may use other sounds to verify the identity of the media (e.g., basketball dribbling to verify the media is a basketball game). In some examples, the media verifier  108  may instead generate a list of identity probabilities of the monitored media  102  based on speaker identification data  106 . The subprocess  700  then ends. 
       FIG.  8    is a block diagram of an example processor platform  1000  structured to execute the instructions of  FIGS.  5 - 7    to implement the system  100  of  FIG.  1   . The processor platform  800  can be, for example, a server, a personal computer, a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a set top box, or any other type of computing device. 
     The processor platform  800  of the illustrated example includes a processor  812 . The processor  812  of the illustrated example is hardware. For example, the processor  812  can be implemented by one or more integrated circuits, logic circuits, microprocessors or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor implements the example preprocessor  202 , the example audio segmenter  204 , the example dominant speaker identifier  208 , the speaker evaluator  206 , the speech pattern identifier  210 , the pattern comparator  212  and the confidence value plotter  214  and/or, more generally, the example speaker detector  104  of  FIG.  2   . 
     The processor  812  of the illustrated example includes a local memory  813  (e.g., a cache). The processor  812  of the illustrated example is in communication with a main memory including a volatile memory  814  and a non-volatile memory  816  via a bus  818 . The volatile memory  814  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  816  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  814 ,  816  is controlled by a memory controller. 
     The processor platform  800  of the illustrated example also includes an interface circuit  820 . The interface circuit  820  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  822  are connected to the interface circuit  820 . The input device(s)  822  permit(s) a user to enter data and/or commands into the processor  812 . 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  824  are also connected to the interface circuit  820  of the illustrated example. The output devices  824  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 tactile output device, a printer and/or speakers). The interface circuit  820  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  820  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  826  (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The processor platform  800  of the illustrated example also includes one or more mass storage devices  828  for storing software and/or data. Examples of such mass storage devices  828  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and digital versatile disk (DVD) drives. 
     The coded instructions of  FIGS.  4 - 6    may be stored in the mass storage device  728 , in the volatile memory  714 , in the non-volatile memory  716 , and/or on a removable tangible computer readable storage medium such as a CD or DVD. 
     From the foregoing, it will be appreciated that example methods, apparatus and articles of manufacture have been disclosed that allow for the impact of audio sources to be accounted for when determining audience ratings. 
     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.