Abstract:
Methods and apparatus to monitor audio/visual content from various sources are disclosed. An example method disclosed herein comprises determining first information based on a video signal corresponding to monitored media content, determining second information based on an audio signal corresponding to the monitored media content, and identifying a selected one of a plurality of media content sources based on a decision tree comprising a plurality of decision nodes, a first decision node of the decision tree corresponding to a first classification decision resulting from processing at least one of the first information or second information, and a second decision node of the decision tree corresponding to a second classification decision resulting from processing at least one of the first information or second information, the second decision node of the decision tree being selected for evaluation based on an outcome of evaluating the first decision node of the decision tree.

Description:
RELATED APPLICATIONS 
     This patent is a continuation of U.S. patent application Ser. No. 12/725,018, entitled “Methods and Apparatus to Monitor Audio/Visual Content from Various Sources,” which was filed on Mar. 16, 2010, which is a continuation of U.S. patent application Ser. No. 11/672,706 (now U.S. Pat. No. 7,712,114), entitled “Methods and Apparatus to Monitor Audio/Visual Content from Various Sources,” which was filed on Feb. 8, 2007, which is a continuation of International Application Serial Number PCT/US2005/028106, entitled “Methods and Apparatus to Monitor Audio/Visual Content from Various Sources,” which was filed on Aug. 9, 2005, which claims priority from U.S. Provisional Application Ser. No. 60/600,007, entitled “Methods and Apparatus to Monitor Audio/Visual Content from Various Sources,” which was filed on Aug. 9, 2004. U.S. patent application Ser. No. 12/725,018, U.S. Patent Application Serial No. 11/672,706, International Application Serial Number PCT/US2005/028106 and U.S. Provisional Application Ser. No. 60/600,007 are hereby incorporated 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 monitor audio/visual content from various sources. 
     BACKGROUND 
     Television ratings and metering information is typically generated by collecting viewing records and/or other viewing information from a group of statistically selected households. Each of the statistically selected households typically has a data logging and processing unit commonly referred to as a “home unit.” In households having multiple viewing sites (e.g., multiple television systems), the data logging and processing functionality may be distributed among a single home unit and multiple “site units,” one site unit for each viewing site. The home unit (or the combination of the home unit and the site unit) is often in communication with a variety of attachments that provide inputs to the home unit or receive outputs from the home unit. For example, a source identification unit such as a frequency detector attachment may be in communication with a television to sense a local oscillator frequency of the television tuner. In this manner, the frequency detector attachment may be used to determine to which channel the television is currently tuned based on a detected frequency. Additional source identification devices, such as on-screen readers and light-emitting-diode (LED) display readers, may be provided, for example, to determine if the television is operating (i.e., is turned ON) and/or the channel to which the television is tuned. A people counter may be located in the viewing space of the television and in communication with the home unit, thereby enabling the home unit to detect the identities and/or number of persons currently viewing programs displayed on the television. 
     The home unit usually processes the inputs (e.g., channel tuning information, viewer identities, etc.) from the attachments to produce viewing records. Viewing records may be generated on a periodic basis (e.g., at fixed time intervals) or on an a-periodic basis (e.g., in response to one or more predetermined events, such as a full memory, or a change in an input, such as a change in the identities of the persons viewing the television, a change in the channel tuning information (i.e., a channel change)), etc. Each viewing record typically contains channel information, such as a channel number and/or station identification (ID), and a time (e.g., a date and time-of-day) at which the channel was displayed. In cases in which the program content being displayed is associated with a local audio/video content delivery device, such as a digital versatile disk (DVD) player (also known as a digital video disk player), a digital video recorder (DVR), a video cassette recorder (VCR), etc., the viewing records may include content identification (i.e., program identification) information as well as information relating to the time and manner in which the associated content was displayed. Viewing records may also contain additional information, such as the number of viewers present at the viewing time. 
     The home unit typically collects a quantity of viewing records and periodically (e.g., daily) transmits the collected viewing records to a central office or data processing facility for further processing or analysis. The central data processing facility receives viewing records from home units located in some or all of the statistically selected households and analyzes the viewing records to ascertain the viewing behaviors of households in a geographic area or market of interest, a particular household and/or a particular group of households selected from all participating households. Additionally, the central data processing facility may generate metering statistics and other parameters indicative of viewing behavior associated with some or all of the participating households. This data may be extrapolated to reflect the viewing behaviors of markets and/or regions modeled by the statistically selected households. 
     To generate viewing behavior information from viewing records, the central office or data processing facility may compare reference data, such as a list of programs (e.g., a schedule of television programming or a television guide), to the viewing records. In this manner, the central office can infer which program was displayed by cross-referencing the time and channel information in a viewing record to the program associated with that same time and channel in the program schedule. Such a cross-referencing process can be carried out for each of the viewing records received by the central office, thereby enabling the central office to reconstruct which programs were displayed by the selected households and the times at which the programs were displayed. Of course, the aforementioned cross-referencing process is unnecessary in systems in which the identity of the program is obtained by the home unit and contained in the viewing record. 
     The rapid development and deployment of a wide variety of audio/video content delivery and distribution platforms has dramatically complicated the home unit task of providing viewing records or information to the central data collection facility. For instance, while the above-mentioned frequency detector device can be used to detect channel information at a site where network television broadcasts are being displayed (because, under normal operation conditions, the local oscillator frequency corresponds to a known network channel), such a device typically cannot be used with digital broadcast systems. In particular, digital broadcast systems (e.g., satellite-based digital television systems, digital cable systems, etc.) typically include a digital receiver or set-top box at each subscriber site. The digital receiver or set-top box demodulates a multi-program data stream, parses the multi-program data stream into individual audio and/or video data packets, and selectively processes those data packets to generate an audio/video signal for a desired program. The audio and/or video output signals generated by the set-top box can be directly coupled to an audio/video input of an output device (e.g., a television, a video monitor, etc.). As a result, the local oscillator frequency of the output device tuner, if any, does not necessarily identify the channel or program currently being displayed. 
     To allow generation of meaningful viewing records in cases wherein, for example, the channel tuned by a monitored information presenting device is not readily identifiable or may not uniquely correspond to a displayed program, metering techniques based on the use of ancillary codes and/or content signatures may be employed. Metering techniques that rely on ancillary codes often encode and embed identifying information (e.g., a broadcast/network channel number, a program identification code, a broadcast time stamp, a source identifier to identify a network and/or station providing and/or broadcasting the content, etc.) in the broadcast signal such that the code is not noticed by the viewer. For example, a well-known technique used in television broadcasting involves embedding the ancillary codes in the non-viewable vertical blanking interval (VBI) of the video signal. Another example involves embedding inaudible codes in portions of the audio signal accompanying the broadcast program. This latter technique is especially advantageous because the embedded code may be reproduced by, for example, the television speaker and non-intrusively monitored by an external sensor, such as a microphone. 
     In general, signature-based program identification techniques use one or more characteristics of the currently displayed (but not yet identified) audio/video content to generate a substantially unique proxy or signature (e.g., a series of digital values, a waveform, etc.) for that content. The signature information for the content being displayed may be compared to a set of reference signatures corresponding to a known set of programs. When a substantial match is found, the currently displayed program content can be identified with a relatively high probability. 
     Generation of accurate monitoring information is becoming increasingly challenging due to the ongoing trend of incorporating multiple audio/visual content sources into a single household viewing area. For example, a typical home entertainment system may include a cable television or broadcast satellite set-top box with an integrated or separate DVR, a DVD player, a DVD recorder, a VCR, a video game console, etc. To generate accurate monitoring information, the audio/video content source and any associated content identification information must be accurately determined. However, monitoring each possible audio/video content source separately may result in an overly complex and/or cumbersome monitoring system. Also, it is desirable that the monitoring be performed in a way that does not require any after-market modification of the various possible audio/video content sources. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example home entertainment system monitored by an example multi-engine meter. 
         FIG. 2  is a block diagram of an example multi-engine meter that may be used in the example of  FIG. 1 . 
         FIG. 3  is a block diagram of an example set of audio engines that may be used to implement the example multi-engine meter of  FIG. 2 . 
         FIG. 4  is a block diagram of an example set of video engines that may be used to implement the example multi-engine meter of  FIG. 2 . 
         FIG. 5  is a block diagram of an example set of metadata engines that may be used to implement the example multi-engine meter of  FIG. 2 . 
         FIG. 6  is a block diagram of an example decision processor that may be used to implement the example multi-engine meter of  FIG. 2 . 
         FIGS. 7A-7D  collectively form a flowchart representative of example machine readable instructions that may be executed to implement the example decision processor of  FIG. 6 . 
         FIG. 8  is a flowchart representative of example machine readable instructions that may be executed to implement the example volume and mute detector of  FIG. 3 . 
         FIG. 9  is a flowchart representative of example machine readable instructions that may be executed to implement the example compression detector of  FIG. 3 . 
         FIG. 10  is a flowchart representative of example machine readable instructions that may be executed to implement the example jingle detector of  FIG. 3 . 
         FIG. 11  is a flowchart representative of example machine readable instructions that may be executed to implement the example spectral shape processor of  FIG. 3 . 
         FIG. 12  is a flowchart representative of example machine readable instructions that may be executed to implement the example scene change and blank frame detector of  FIG. 4 . 
         FIG. 13  is a flowchart representative of example machine readable instructions that may be executed to implement the example macroblock detector of  FIG. 4 . 
         FIG. 14  is a flowchart representative of example machine readable instructions that may be executed to implement the example template matcher of  FIG. 4 . 
         FIG. 15  is a block diagram of an example computer that may execute the example machine readable instructions of  FIGS. 7A-7D ,  8 - 13  and/or  14  to implement the example multi-engine meter of  FIG. 2 . 
         FIGS. 16A-16F  illustrate example decision metrics that may be used by the example decision processor of  FIG. 6 . 
     
    
    
     DETAILED DESCRIPTION 
     A block diagram of an example home entertainment system  100  with content monitoring capability is illustrated in  FIG. 1 . The example home entertainment system  100  includes a plurality of audio/visual (A/V) content sources  102  that may include any or all of a game console  104 , a set-top box (STB)  106 , a digital video disk (DVD) player  108 , a video cassette recorder (VCR)  110 , a personal video recorder (PVR), a digital video recorder (DVR)  112 , etc. The A/V content sources  102  are coupled to the inputs of an A/V switch  114  to route the outputs from a selected one of the A/V content sources  102  to the inputs of a television  116  or other information presentation device. Additionally, a signal splitter  118  routes the inputs being provided to the television  116  to a multi-engine meter  120  to facilitate monitoring of the A/V content provided to and presented by the television  116 . The components of the home entertainment system  100  may be connected in any well-known manner including that shown in  FIG. 1 . 
     The game console  104  may be any device capable of playing a video game. An example game console  104  is a standard dedicated game console, such as Microsoft&#39;s XBOX, Nintendo&#39;s GameCube, Sony&#39;s PlayStation, etc. Another example game console  104  is a portable dedicated gaming device, such as Nintendo&#39;s GameBoy SP or Game Boy DS, or Sony&#39;s PSP. Other example games consoles  104  include a personal digital assistant (PDA), a personal computer, a DVD player, a DVR, a PVR, a cellular/mobile phone, and the like. 
     The STB  106  may be any set-top box, such as a cable television converter, a direct broadcast satellite (DBS) decoder, an over-the-air (OTA) digital television (DTV) receiver, a VCR, etc. The set-top box  106  receives a plurality of broadcast channels from a broadcast source (not shown). Typically, the STB  106  selects one of the plurality of broadcast channels based on a user input, and outputs one or more signals received via the selected broadcast channel. In the case of an analog signal, the STB  106  tunes to a particular channel to obtain programming delivered on that channel. For a digital signal, the STB  106  may tune to a channel and decode certain packets of data to obtain programming delivered on the selected channel. For example, the STB  106  may tune to a major channel and then extract a program carried on a minor channel within the major channel via the decoding process mentioned above. 
     The DVD player  108  may be provided, for example, to output A/V content stored in a digital format on a DVD and/or audio content stored in a digital format on a compact disk (CD). The VCR  110  may be provided, for example, to output pre-recorded A/V content stored on a video cassette and/or to record A/V content provided by another of the A/V content sources  102  for later presentation via the television  116 . The PVR/DVR  112  may be provided to support time-shifted presentation of the A/V content provided by, for example, the STB  106 . The PVR/DVR  112  typically supports a variety of features, including presenting live A/V content, delaying the presentation of live A/V content, fast-forwarding and rewinding A/V content, pausing the presentation of A/V content, recording A/V content for later presentation while watching a live broadcast of other A/V content, etc. A PVR is typically a DVR that has been configured to be automatically adaptive to or otherwise automatically responsive to the viewing preferences of a particular user or group of users within a particular household. For example, many DVRs provide a telephone line connection that enables the DVR to communicate with a central service facility that receives viewer preference information from the DVR and which sends configuration information to the DVR based on those viewer preferences. The configuration information is used by the DVR to automatically configure the DVR to record video programs consistent with the preferences of the viewer or viewers associated with that DVR. TiVo™ is one well-known service that provides PVR functionality to an otherwise standard or conventional DVR. 
     The A/V switch  114  is configured to route a user-selected A/V input to the switch output. As shown in  FIG. 1 , the outputs of each of the plurality of A/V content sources  102  are routed to respective inputs of the A/V switch  114 . The user may then use the A/V switch  114  to select which of the A/V content sources  102  will be coupled to the television  116 . The format of the inputs and output of the A/V switch  114  will depend on the formats of the outputs of the A/V content sources  102  and the inputs of the television  116 . For example, the inputs and outputs of the A/V switch  114  may be composite audio/video, component audio/video, RF, etc. Also, as will be recognized by a person of ordinary skill in the art, the A/V switch  114  may be implemented as a stand-alone device or integrated, for example, into a home entertainment receiver, a television or similar device. 
     An output from the A/V switch  114  is fed to a signal splitter  118 , such as a composite audio/video splitter in the case of a direct composite audio/video connection between the A/V switch  114  and the television  116 , or a single analog y-splitter in the case of an RF coaxial connection between the A/V switch  114  and the television  116 . In the example home entertainment system  100 , the signal splitter  118  produces two signals indicative of the output from the A/V switch  114 . Of course, a person of ordinary skill in the art will readily appreciate that any number of signals may be produced by the signal splitter  118 . 
     In the illustrated example, one of the two signals from the signal splitter  118  is fed to the television  116  and the other signal is delivered to the multi-engine meter  120 . The television  116  may be any type of television or television display device. For example, the television  116  may be a television and/or display device that supports the National Television Standards Committee (NTSC) standard, the Phase Alternating Line (PAL) standard, the Systeme Électronique pour Couleur avec Mémoire (SECAM) standard, a standard developed by the Advanced Television Systems Committee (ATSC), such as high definition television (HDTV), a standard developed by the Digital Video Broadcasting (DVB) Project, a multimedia computer system, etc. 
     The second of the two signals from the signal splitter  118  (i.e., the signal carried by connection  122  in  FIG. 1 ) is coupled to an input of the multi-engine meter  120 . The multi-engine meter  120  is an A/V content monitoring device capable of determining the A/V content source  102  providing the A/V content to the television  116 . Such source identification information may be output via a source identification output  124 . Additionally, the multi-engine meter  120  may be configured to determine content identification information (also known as tuning information) that may be dependent on the content source, such as a video game title, a broadcast program title, a recorded program title, an original broadcast time, a presentation time, a trickmode in use, etc. Such content identification information may be output via a content information output  126 . The multi-engine meter  120  determines the content identification information based on the signal corresponding to the A/V content being output by the A/V switch  114 . 
     To facilitate the determination of source and content identification information, the multi-engine meter  120  may also be provided with one or more sensors  128 . For example, one of the sensors  128  may be configured to detect signals transmitted by a remote control device  130 . As shown in  FIG. 1 , the example home entertainment system  100  also includes a remote control device  130  to transmit control information that may be received by any or all of the A/V content sources  102 , the television  116  and/or the multi-engine meter  120 . One having ordinary skill in the art will recognize that the remote control device  130  may transmit this information using a variety of techniques, including, but not limited to, infrared (IR) transmission, radio frequency transmission, wired/cabled connection, and the like. 
     A block diagram of an example multi-engine meter  200  that may be used to implement the multi-engine meter  120  of  FIG. 1  is illustrated in  FIG. 2 . The example multi-engine meter  200  is configured to process composite A/V inputs that include stereo left and right audio input signals  204  and a video input signal  208 . The stereo audio input signals  204  are sampled by an audio sampler  212  at a suitable sampling rate, e.g., 48 kHz, and converted to a digital monaural audio signal. The resulting digital audio samples are stored in an audio buffer  216 . The video input signal  208  is sampled by a video sampler  220  to form digital video samples that are stored in a video buffer  224 . In the instant example, the video sampler  220  and video buffer  224  are configured to sample the video input  208  at an NTSC frame rate of 29.97 frames/sec with a resolution of 640 by 480 pixels. Additionally, the input color video signal is converted to a black-and-white luminance signal. However, a person having ordinary skill in the art will appreciate that various sampling rates, resolutions and color conversions may also be used. 
     The multi-engine meter  200  includes one or more audio engines  228  to process the digital audio samples stored in the audio buffer  216 . The audio engines  228  are configured to determine characteristics of the input audio signals  204  and/or information included in the input audio signals  204  that may be used to ascertain the A/V content source coupled to the multi-engine meter  200  (e.g., which A/V content source  102  of  FIG. 1  is coupled to the multi-engine meter  120  and, therefore, the television  116 ). Additionally, the audio engines  228  may be configured to determine A/V content identification information based on the input audio signals  204 . Examples of audio engines  228  are discussed in greater detail below in connection with  FIG. 3 . 
     The example multi-engine meter  200  also includes one or more video engines  232  to process the digital video samples stored in the video buffer  224 . Similar to the audio engines  228 , the video engines  232  are configured to determine characteristics of the input video signal  208  and/or information included in the input video signal  208  that may be used to ascertain the A/V content source coupled to the multi-engine meter  200  (e.g., which A/V content source  102  of  FIG. 1  is coupled to the multi-engine meter  120  and, therefore, the television  116 ). Additionally, the video engines  232  may be configured to determine A/V content identification information based on the input video signal  208 . Examples of video engines  232  are discussed in greater detail below in connection with  FIG. 4 . 
     To receive, decode and process metadata that may be embedded in the input audio signals  204  and/or input video signal  208 , the example multi-engine meter  200  includes a metadata extractor  236  and one or more associated metadata engines  240 . The metadata extractor  236  is configured to extract and/or process portions of the input audio signals  204  and/or input video signal  208  that may be used to carry embedded metadata information. The extracted/process signal portions are then processed further by the metadata engines  240  to determine if metadata is present in the signal portions and, if so, to receive/decode such metadata. The resulting metadata may be used to ascertain the A/V content source coupled to the multi-engine meter  200  and/or to determine A/V content information associated with the input signals  204 ,  208 . Examples of metadata engines  240  are discussed in greater detail below in connection with  FIG. 5 . 
     The example multi-engine meter  200  includes a decision processor  244  to process the output information generated by the audio engines  228 , the video engines  232  and the metadata engines  240 . Additionally, the decision processor  244  of the example multi-engine meter  200  is configured to process remote control signals  248  transmitted by a remote control device, such as the remote control device  130  of  FIG. 1 . The remote control signals  248  are received by a remote control detector  252  and provided as input to the decision processor  244  as shown. The decision processor  244  processes the available input information to determine the A/V content source coupled to the multi-engine meter  200  and outputs this information via the source identification (ID) output  256 . Additionally, the decision processor  244  may determine A/V content identification information and output such information via the content information (info) output  260 . An example decision processor  244  is discussed in greater detail below in connection with  FIG. 6 . 
     An example set of audio engines  300  that may be used to implement the audio engines  228  of  FIG. 2  is shown in  FIG. 3 . The audio engines  300  process input audio samples  304  provided, for example, by the audio buffer  216  of  FIG. 2 . The input audio samples  304  correspond to an audio signal being output by an A/V content source (e.g., one of the A/V content sources  102  of  FIG. 1 ) and provided as input to a monitored presentation device, such as the television  116 . An audio engine included in the audio engines  300  is configured to read a number of input audio samples  304  at a rate and frequency that depends on the processing performed by that particular audio engine. Thus, the audio engines  300  may operate autonomously and read the input audio samples  304  and generate corresponding audio engine outputs  308  in an autonomous fashion. 
     The example set of audio engines  300  includes an audio code detector  312 , an audio signature processor  316 , a volume and mute detector  320 , a compression detector  324 , a jingle detector  328  and a spectral shape processor  332 . The example audio code detector  312  is configured to detect and process ancillary audio codes that may be embedded in the audio signal corresponding to the input audio samples  304 . As discussed above, ancillary audio codes may be used to encode and embed identifying information (e.g., a broadcast/network channel number, a program identification code, a broadcast time stamp, a source identifier to identify a network and/or station providing and/or broadcasting the content, etc.) in, for example, non-audible portions of the audio signal accompanying a broadcast program. Methods and apparatus for implementing the audio code detector  312  are known in the art. For example, in U.S. Pat. No. 6,272,176, incorporated herein by reference in its entirety, Srinivasan discloses a broadcast encoding system and method for encoding and decoding information transmitted within an audio signal. This and/or any other appropriate technique may be used to implement the audio code detector  312 . 
     The example audio signature processor  316  is configured to generate and process audio signatures corresponding to the input audio samples  304 . As discussed above, characteristics of the audio portion of presented A/V content may be used to generate a substantially unique proxy or signature (e.g., a series of digital values, a waveform, etc.) for that content. The signature information for the content being presented may be compared to a set of reference signatures corresponding to a known set of content. When a substantial match is found, the currently displayed A/V content can be identified with a relatively high probability. Methods and apparatus for implementing the audio signature processor  316  are known in the art. For example, in U.S. patent application Ser. No. 09/427,970, incorporated herein by reference in its entirety, Srinivasan, et al. disclose audio signature extraction and correlation techniques. As another example, in Patent Cooperation Treaty Application Serial No. U.S. 03/22562,incorporated herein by reference in its entirety, Lee, et al. disclose signature based program identification apparatus and methods for use with a digital broadcast system. These and/or any other appropriate technique may be used to implement the audio signature processor  316 . 
     The example volume and mute detector  320  is configured to determine whether the input audio samples  304  correspond to an audio signal in a volume mute state. Additionally or alternatively, the volume and mute detector  320  may be configured to determine a volume level associated with the input audio samples  304 . Knowledge of whether or not the audio is in a mute state may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine which audio engine outputs  308  to process and/or how to process such outputs. Example machine readable instructions  800  that may be executed to implement the volume and mute detector  320  are discussed in the detailed description of  FIG. 8  below. 
     The example compression detector  324  is configured to determine whether the input audio samples  304  correspond to an audio signal that has been subject to compression. Additionally or alternatively, the compression detector  324  is configured to determine which type of compression has been performed on a compressed audio signal. For example, DVDs and digital television systems typically use AC3 compression to store/transmit digital audio, whereas some DVRs/PVRs may use MPEG audio compression. Thus, knowledge of whether the audio has been compressed and, if so, the type of compression employed may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input audio samples  304 . Example machine readable instructions  900  that may be executed to implement the compression detector  324  are discussed in the detailed description of  FIG. 9  below. 
     The example jingle detector  328  is configured to determine whether the input audio samples  304  correspond to an audio jingle generated by an A/V content source when, for example, a user causes the A/V content source to display a menu, such as a power-on menu, a channel/program select menu, etc. Knowledge of whether the input audio samples  304  correspond to an audio jingle may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine which A/V content source generated the audio jingle and, therefore, is the source of the corresponding input audio samples  304 . Known techniques for generating and comparing audio signatures, such as those described above in connection with the example audio signature processor  316 , may be adapted to determine whether the input audio samples  304  correspond to a reference audio jingle. Example machine readable instructions  1000  that may be executed to implement the jingle detector  328  are discussed in the detailed description of  FIG. 10  below. 
     The example spectral shape processor  332  is configured to determine whether the input audio samples  304  correspond to an audio signal that possesses a particular spectral shape. For example, audio signals in an analog cable television transmission system may exhibit increased energy in a frequency band at or near 15.75 kHz due to video signal leakage. Thus, knowledge of whether the audio has a particular spectral shape may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input audio samples  304 . Example machine readable instructions  1100  that may be executed to implement the spectral shape processor  332  are discussed in the detailed description of  FIG. 11  below. 
     As shown in the example of  FIG. 3 , the results of each audio engine  312 - 332  may be scaled/prioritized by a set of respective weights  336 - 356 . For example, the weights  336 - 356  may explicitly scale the audio engine results based on the amount of information, amount of confidence, etc. that a respective result may contribute to the processing performed by a decision processor, such as the decision processor  224  of  FIG. 2 . Additionally or alternatively, and in the instant example, the weights  336 - 356  may be implicit and based, for example, on a stage in which a particular audio engine result is used in a decision process performed by the decision processor, the priority given a particular audio engine result by the decision processor, etc. 
     An example set of video engines  400  that may be used to implement the video engines  232  of  FIG. 2  is shown in  FIG. 4 . The video engines  400  process input video samples  404  provided, for example, by the video buffer  224  of  FIG. 2 . The input video samples  404  correspond to a video signal being output by an A/V content source (e.g., one of the A/V content sources  102  of  FIG. 1 ) and provided as input to a monitored presentation device, such as the television  116 . A video engine included in the video engines  400  is configured to read a number of input video samples  404  at a rate and frequency that depends on the processing performed by that particular video engine. Thus, the video engines  400  may operate autonomously and read the input video samples  404  and generate corresponding video engine outputs  408  in an autonomous fashion. 
     The example set of video engines  400  includes a text detector  412 , a blurriness detector  416 , a scene change and blank frame detector  420 , a macroblock detector  424  and a template matcher  428 . The example text detector  412  is configured to determine whether portions/areas of the video corresponding to the input video samples  404  include text associated, for example, with a known display, such as a menu displayed by a particular A/V content source based on invocation of a selected operating mode. Thus, knowledge of whether the input video samples  404  correspond to video displaying particular text may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input video samples  404 . Methods and apparatus for implementing the text detector  412  are known in the art. For example, in Patent Cooperation Treaty Application Serial No. U.S. 04/012272, incorporated herein by reference in its entirety, Nelson, et al. disclose methods and apparatus for detecting a television channel change event that are based on determining whether selected portions of a video display include numeric digits corresponding to a displayed channel number. This and/or any other appropriate technique may be used to implement the text detector  412 . 
     The example blurriness detector  416  is configured to determine whether portions/areas of the video corresponding to the input video samples  404  are blurry or exhibit blurriness characteristics. For example, blurriness may be introduced into video/images as a result of compression associated with a particular A/V content source. Thus, knowledge of whether the input video samples  404  correspond to video exhibiting blurriness may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input video samples  404 . Methods and apparatus for implementing the blurriness detector  416  are known in the art. For example, in “Digital Image Restoration,” IEEE Signal Processing Magazine, March 1997, pp. 24-41, incorporated herein by reference in its entirety, Banham and Katsaggelos describe various techniques to identify blur in an image. These and/or any other appropriate technique may be used to implement the blurriness detector  416 . 
     The example scene change and blank frame detector  420  is configured to determine whether a set of sequential frames corresponding to the input video samples  404  exhibit, for example, a scene change, a paused frame, one or more blank frames, etc. Such information may be used to determine, for example, whether a trick mode (e.g., pause) has been performed by the A/V content source providing the input video samples  404 . Additionally, the number of blank frames detected over a predetermined interval (e.g., such as two minutes) may be used to determine whether the A/V content corresponds, for example, to a commercial pod and, thus, indicate whether the A/V content source is a broadcast source. Thus, knowledge of whether the input video samples  404  correspond to a scene change, a paused frame, a blank frame, etc. may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input video samples  404 . Example machine readable instructions  1200  that may be executed to implement the scene change and blank frame detector  420  are discussed in the detailed description of  FIG. 12  below. 
     The example macroblock detector  424  is configured to determine whether the video corresponding to the input video samples  404  exhibits macroblock characteristics corresponding to MPEG video compression. Additionally, the macroblock detector  424  may determine whether the video signal exhibits nearly perfect color blending indicative of a video game being played via a game console, such as the game console  104  of  FIG. 4 . Knowledge of whether the input video samples  404  exhibit macroblock characteristics or nearly perfect color blending may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input video samples  404 . Example machine readable instructions  1300  that may be executed to implement the macroblock detector  424  are discussed in the detailed description of  FIG. 13  below. 
     The example template matcher  428  is configured to determine whether the video corresponding to the input video samples  404  matches a known/stored template corresponding, for example, to a menu screen being output by a particular A/V content source. Knowledge of whether the input video samples  404  correspond to a known/stored template may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input video samples  404 . Known techniques for generating and comparing video signatures, such as those described in, for example, U.S. Pat. No. 6,633,651, entitled “Method and Apparatus for Recognizing Video Sequences” and U.S. Pat. No. 6,577,346, entitled “Recognizing a Pattern in a Video Segment to Identify the Video Segment”, both of which are incorporated herein by reference in their entireties, may be adapted to determine whether the input video samples  404  correspond to a reference template. Example machine readable instructions  1400  that may be executed to implement the template matcher  428  are discussed in the detailed description of  FIG. 14  below. 
     As shown in the example of  FIG. 4 , the results of each video engine  412 - 428  may be scaled/prioritized by a set of respective weights  432 - 448 . For example, the weights  432 - 448  may explicitly scale the video engine results based on the amount of information, amount of confidence, etc. that a respective result may contribute to the processing performed by a decision processor, such as the decision processor  224  of  FIG. 2 . Additionally or alternatively, and in the instant example, the weights  442 - 448  may be implicit and based, for example, on a stage in which a particular video engine result is used in a decision process performed by the decision processor, the priority given a particular video engine output by the decision processor, etc. 
     An example set of metadata engines  500  that may be used to implement the metadata engines  240  of  FIG. 2  is shown in  FIG. 5 . The metadata engines  500  process input metadata  504  provided, for example, by the metadata extractor  236  of  FIG. 2 . The input metadata  504  corresponds to an audio and/or video signal being output by an A/V content source (e.g., one of the A/V content sources  102  of  FIG. 1 ) and provided as input to a monitored presentation device, such as the television  116 . A metadata engine included in the metadata engines  500  is configured to read input metadata  504  at a rate and frequency that depends on the processing performed by that particular metadata engine. Thus, the metadata engines  500  may operate autonomously and read the input metadata  504  and generate corresponding metadata engine outputs  508  in an autonomous fashion. 
     The example set of metadata engines  500  includes an Automated Measurement of Lineup (AMOL) processor  512 , a closed caption processor  516  and a teletext processor  520 . 
     The example AMOL processor  512  is configured to determine whether the input metadata  504  corresponds to AMOL codes and to process such codes if present. AMOL codes may be embedded, for example, in broadcast television transmissions to permit identification of transmitted content, the source of the transmitted content, etc. More specifically, AMOL codes may be included in non-viewable portions of a broadcast television signal (e.g., line  20  of the vertical blanking interval (VBI)) and/or in viewable portions of the broadcast television signal (e.g., line  22  of the active video portion of the video signal). Additionally, AMOL codes may be encrypted. Typically, AMOL codes transmitted, for example, in line  20  of the VBI are not recoverable after digital compression because digital video signals do not use the VBI and, therefore, the compression algorithm may discard/corrupt such information. AMOL codes transmitted, for example, in line  22  may be recoverable after digital compression because such codes are transmitted in the active video portion of the video signal. 
     Thus, processed AMOL codes may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input metadata  504  and additional content identification information. Methods and apparatus for implementing the AMOL processor  512  are known in the art. For example, in U.S. Pat. Nos. 5,425,100 and 5,526,427, incorporated herein by reference in their entirety, Thomas, et al. disclose universal broadcast code and multi-level encoded signal monitoring systems that may be used to process AMOL codes. These and/or any other appropriate technique may be used to implement the AMOL processor  512 . 
     The example closed caption processor  516  is configured to determine whether the input metadata  504  corresponds to closed caption information and to process such information, if present. Closed caption information (such as text) may be included in non-viewable portions of a broadcast television signal (e.g., line  21  of the VBI). Processed closed caption information may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input metadata  504  and additional content identification information. Methods and apparatus for implementing the closed caption processor  516  are known in the art. For example, in U.S. Pat. No. 4,857,999, incorporated herein by reference in its entirety, Welsh describes a video monitoring system that processes closed caption information. This and/or any other appropriate techniques may be used to implement the closed caption processor  516 . 
     The example teletext processor  520  is configured to determine whether the input metadata  504  corresponds to teletext information and to process such information, if present. As with closed caption information, teletext information may be included in non-viewable portions of a broadcast television signal. Processed teletext information may be used, for example, by a decision processor, such as the decision processor  224  of  FIG. 2 , to determine the A/V content source corresponding to the input metadata  504  and additional content identification information. Methods and apparatus for implementing the teletext processor  520  are known in the art. For example, techniques used to process closed caption information may be adapted to process teletext. As such, any appropriate technique may be used to implement the teletext processor  520 . 
     As shown in the example of  FIG. 5 , the results of each metadata engine  512 - 520  may be scaled/prioritized by a set of respective weights  524 - 532 . For example, the weights  524 - 532  may explicitly scale the metadata engine results based on the amount of information, amount of confidence, etc. that a respective result may contribute to the processing performed by a decision processor, such as the decision processor  224  of  FIG. 2 . Additionally or alternatively, and in the instant example, the weights  524 - 532  may be implicit and based, for example, on a stage in which a particular metadata engine result is used in a decision process performed by the decision processor, the priority given a particular metadata engine output by the decision processor, etc. 
     Persons of ordinary skill in the art will appreciate that additional or alternative metadata processors may be included in the set of metadata engines  500  depending on the type of metadata provided by the metadata input  504 . Such an additional or alternative metadata processor may be configured, for example, to process content identification information included in a digital bitstream providing the monitored A/V content. The content identification information could be, for example, a Versatile International Standard Audiovisual Number (VISAN) or any other type of identifier which may be used to identify the monitored A/V content. 
     A block diagram of an example decision processor  600  that may be used to implement the decision processor  244  of  FIG. 2  is illustrated in  FIG. 6 . The example decision processor  600  receives one or more audio engine results  604  from one or more audio engines (e.g., the audio engines  300  of  FIG. 3 ), one or more video engine results  608  from one or more video engines (e.g., the video engines  400  of  FIG. 4 ) and one or more metadata engine results  612  from one or more metadata engines (e.g., the metadata engines  500  of  FIG. 5 ). The audio engine results  604  are stored in respective audio metric registers  616 - 620 . The video engine results  608  are stored in respective video metric registers  624 - 628 . The metadata engine results  612  are stored in respective metadata metric registers  632 - 636 . The audio metric registers  616 - 620 , the video metric registers  624 - 628  and the metadata metric registers  632 - 636  may be implemented as hardware registers, memory locations, etc. or any combination thereof. Because the various audio engine results  604 , video engine results  608  and metadata engine results  612  are generated autonomously, the audio metric registers  616 - 620 , the video metric registers  624 - 628  and the metadata metric registers  632 - 636  may be updated autonomously when their respective results become available. 
     The example decision processor  600  includes an audio metric sampler  640 , a video metric sampler  644  and a metadata metric sampler  648  to sample (e.g., read the respective results from the hardware register, memory location, etc.), respectively, the audio metric registers  616 - 620 , the video metric registers  624 - 628  and the metadata metric registers  632 - 636 . The sampling operation may be performed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. The audio metric sampler  640 , the video metric sampler  644  and the metadata metric sampler  648  provide the sampled results to a metering engine metric evaluator  652 . The metering engine metric evaluator  652  uses the available audio engine results, video engine results and metadata engine results to determine the A/V content source corresponding to the monitored A/V content. The metering engine metric evaluator  652  outputs the detected A/V content source via the source ID output  656 . The metering engine metric evaluator  652  may also determine additional content identification information corresponding to the monitored A/V content. Such content identification information may be output via the content info output  660 . Example machine readable instructions  700  that may be executed to implement the metering engine metric evaluator  652  are discussed in the detailed description of  FIGS. 7A-7D  below. 
       FIGS. 16A-16F  illustrate example decision metrics which may be used by the example metering engine metric evaluator  652  to determine, for example, the A/V content source corresponding to the monitored A/V content and/or whether the A/V content source corresponding to the monitored A/V content has been placed into a special operating mode.  FIG. 16A  lists decision metrics that may be used to determine whether the A/V content source is a live analog television source (Analog TV Live) or an analog video-on-demand (VOD) source (Analog VOD). A first decision metric indicative of a live analog television source detection is the presence of AMOL codes in line  20  of the VBI of the broadcast television signal (e.g., provided by the AMOL processor  512  of  FIG. 5 ) coupled with the absence of a detected timeshift of the A/V content presentation. As discussed above, the presence of AMOL codes in line  20  of the VBI indicates that the A/V content source is an analog television source because AMOL codes do not survive the compression associated with a digital television source. However, if AMOL codes in line  20  of the VBI are not detected (e.g., by the AMOL processor  512 ), a live analog television source may also be detected by a second decision metric which includes detecting the presence of an audio signal corresponding to the monitored A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), the presence of cable spectral shaping of the detected audio signal (e.g., detected by the spectral shape processor  332 ) and the absence of a timeshift. As discussed above, the presence of cable spectral shaping indicates that the detected audio signal was subjected to an analog cable transmission system and, thus, the A/V content source is an analog television source. 
     Similarly,  FIG. 16A  lists two decision metrics that may be used to detect an analog VOD source. The first analog VOD decision metric employs the presence of AMOL codes in line  20  of the VBI (e.g., provided by the AMOL processor  512 ) to detect an analog television source, coupled with the presence of a timeshift to indicate that the source is not live but rather an analog VOD source. If AMOL codes in line  20  of the VBI are not present (e.g., as determined by the AMOL processor  512 ), the second analog VOD decision metric may be evaluated and includes detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), detecting the presence of cable spectral shaping indicative of an analog television source (e.g., detected by the spectral shape processor  332 ) and detecting a timeshift indicative of a VOD presentation. A timeshift of the A/V content presentation may be detected using various techniques, such as, for example, comparing a broadcast timestamp included in the AMOL information with a real-time clock included in the multi-engine meter  200 , comparing a timestamp included in audio codes embedded in the detected audio signal with the real time clock included in multi-engine meter  200 , etc. 
       FIG. 16B  lists two decision metrics corresponding to a third possible analog source, namely, a video cassette recorder (VCR) playback. The first VCR playback decision metric combines the presence of AMOL codes in line  20  of the VBI (e.g., provided by the AMOL processor  512 ) indicative of an analog television source, the presence of a timeshift indicative of an analog television source that is not live and the absence of spectral shaping indicative of a cable television transmission system (e.g., as determined by the spectral shape processor  332 ) to indicate that the source is a local VCR and not a cable television transmission system. If AMOL codes in line  20  of the VBI are not detected (e.g., by the AMOL processor  512 ), then the second VCR playback decision metric may be evaluated to detect a VCR playback and includes detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), detecting the absence of spectral shaping indicative of a cable television transmission system (e.g., as determined by the spectral shape processor  332 ) and detecting the absence of any characteristics associated with a digital television transmission, such as video macroblocking (e.g., as determined by the macroblock detector  424  of  FIG. 4 ), AC3 audio compression (e.g., as determined by the compression detector  324 ) or MPEG audio compression (e.g., as determined by the compression detector  324 ), as discussed above. By a process of elimination, the second VCR playback decision metric determines that the A/V content source corresponds to a local analog source and, thus, a VCR playback. 
       FIG. 16B  also lists a decision metric that may be used to detect a digital A/V content source corresponding to a digital versatile disk (DVD) playback. The DVD playback decision metric combines the absence of AMOL codes in line  20  of the VBI (e.g., as determined by the AMOL processor  512 ) indicative of an analog television source with detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), detecting video macroblocking (e.g., by the macroblock detector  424 ) indicative of a digital video presentation and detecting AC3 audio compression (e.g., by the compression detector  324 ) indicative of a digital audio presentation. AC3 audio compression is used to store audio content on a DVD and video macroblocking is more readily evident in a DVD video presentation than in a digital television presentation (as discussed in more detail below). Thus, the presence of AC3 audio compression and video macroblocking may be used to determine whether the A/V content source corresponds to a DVD playback. 
       FIG. 16C  lists decision metrics that may be used to detect digital television sources corresponding to a live broadcast (Digital TV Live) or a playback through a digital video recorder or similar device (Digital TV DVR Playback). These metrics combine the absence of AMOL codes in line  20  of the VBI (e.g., as determined by the AMOL processor  512 ) indicative of an analog television source with detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), determining the absence of significant video macroblocking (e.g., as determined by the macroblock detector  424 ) and detecting AC3 audio compression (e.g., by the compression detector  324 ) indicative of a digital audio presentation. Live digital television is distinguishable from a DVR playblack by, respectively, the absence or presence of a detected timeshift. As in the case of a DVD playback, digital television employs AC3 audio compression. However, in the case of digital television, as compared to a DVD playback, significant video macroblocking is usually not evident due to anti-macroblocking filters present in the digital television transmission system, less compression in the digital television video signal compared to the DVD video signal, transmission noise in the digital television signal not present in a DVD playback, etc. Thus, the presence of AC3 audio compression and absence of significant video macroblocking may be used to distinguish a digital television source from a DVD playback. 
       FIG. 16D  lists decision metrics that may be used to detect DVR sources employing MPEG audio compression and providing either a live broadcast (MPEG DVR Live) or a delayed playback of previously recorded A/V content (MPEG DVR Playback). These metrics combine the absence of AMOL codes in line  20  of the VBI (e.g., as determined by the AMOL processor  512 ) indicative of an analog television source with detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ), determining the absence of significant video macroblocking (e.g., as determined by the macroblock detector  424 ) and detecting MPEG audio compression (e.g., by the compression detector  324 ) indicative of an MPEG DVR audio presentation. A live MPEG DVR presentation is distinguishable from an MPEG DVR playblack by, respectively, the absence or presence of a detected timeshift. The input to an MPEG DVR is typically a digital TV broadcast and, therefore, the resulting MPEG DVR video signal will usually exhibit no significant macroblocking because the digital television video signal exhibits no significant macroblocking due to the reasons discussed above. Thus, the presence of MPEG audio compression and absence of significant video macroblocking may be used to detect an MPEG DVR source. 
       FIG. 16E  lists a decision metric that may be used to detect a video game source. The video game decision metric combines the absence of AMOL codes in line  20  of the VBI (e.g., as determined by the AMOL processor  512 ) indicative of an analog television source with detecting the presence of the audio signal corresponding to the A/V content presentation (e.g., corresponding to detecting a “no audio mute” condition as determined, for example, by the volume and mute detector  320  of  FIG. 3 ) and a video macroblocking result of zero (e.g., determined by the macroblock detector  424 ) representative of perfect color blending. Perfect color blending is indicative of a video game presentation, as discussed above, and, thus, may be used to detect a video game source. 
       FIG. 16E  also lists decision metrics that may be used to detect A/V content source special operating modes corresponding to a blank frame state or an audio mute state. The blank frame metric is based on detecting the presence of a blank video frame (e.g., by the scene change and blank frame detector  420  of  FIG. 4 ). The audio mute metric is based on detecting the absence of an audio signal corresponding to an A/V content presentation (e.g., as determined by the volume and mute detector  320 ). The audio mute metric may also examine whether closed caption or teletext data is present (e.g., as determined by the closed caption processor  516  and telextext processor  520  of  FIG. 5 , respectively) to verify that A/V content presentation corresponds only to an audio mute state and not another special operating mode as discussed below. 
       FIG. 16F  lists decision metrics that may be used to detect additional special operating modes corresponding to a menu display and a pause state. The menu display metric is based on detecting a paused video display (e.g., by the scene change and blank frame detector  420 ) and matching the A/V content presentation to a template, jingle and/or text corresponding to the menu display (e.g., as determined by the template matcher  428  of  FIG. 4 , the jingle detector  328  of  FIG. 3  and the text detector  412  of  FIG. 4 , respectively). Optionally, the menu display metric may also examine whether the audio signal corresponding to the A/V content presentation is absent (e.g., as determined by the volume and mute detector  320 ) and/or closed caption or teletext data is absent (e.g., as determined by the closed caption processor  516  and telextext processor  520 , respectively) to further validate that the current display does not correspond to a normal A/V content presentation. The pause metric is based on detecting the absence of the audio signal corresponding to the A/V content presentation (e.g., as determined by the volume and mute detector  320 ), the absence of closed caption or teletext data (e.g., as determined by the closed caption processor  516  and telextext processor  520 , respectively), a paused video display (e.g., by the scene change and blank frame detector  420 ) and the absence of a template and/or text match (e.g., as determined by the template matcher  428  and the text detector  412 , respectively) corresponding to a menu display. 
     Finally,  FIG. 16  F also lists a metric that may be used to determine whether the A/V content source is operating in some other trick mode, such as, for example, a rewind state, a fast-forward state, etc. The trick mode metric is based on detecting the absence of the audio signal corresponding to the A/V content presentation (e.g., as determined by the volume and mute detector  320 ), the absence of closed caption or teletext data (e.g., as determined by the closed caption processor  516  and telextext processor  520 , respectively), and also detecting the absence of a paused video display or a blank frame (e.g., as determined by the scene change and blank frame detector  420 ). The absence of the audio signal and the closed caption or teletext data indicates that the active display does not correspond to a normal A/V content presentation. However, because the video display does not correspond to a pause state (indicative of a paused frame or a menu display) or a blank frame, the active display is deemed to correspond to some other trick mode operation of the A/V content source. 
     Flowcharts representative of example machine readable instructions that may be executed to implement the metering engine metric evaluator  652  of  FIG. 6  and at least some of the audio engines  300  of  FIG. 3  and the video engines  400  of  FIG. 4  are shown in  FIGS. 7A-7D  through  FIG. 14 . In these examples, the machine readable instructions represented by each flowchart may comprise one or more programs for execution by: (a) a processor, such as the processor  1512  shown in the example computer  1500  discussed below in connection with  FIG. 15 , (b) a controller, and/or (c) any other suitable device. The one or more programs may be embodied in software stored on a tangible medium such as, for example, a flash memory, a CD-ROM, a floppy disk, a hard drive, a DVD, or a memory associated with the processor  1512 , but persons of ordinary skill in the art will readily appreciate that the entire program or programs and/or portions thereof could alternatively be executed by a device other than the processor  1512  and/or embodied in firmware or dedicated hardware in a well-known manner (e.g., implemented by an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable logic device (FPLD), discrete logic, etc.). For example, any or all of the metering engine metric evaluator  652 , the audio engines  300  and/or the video engines  400  (as well as the metadata engines  500  of  FIG. 5 ) could be implemented by any combination of software, hardware, and/or firmware. Also, some or all of the machine readable instructions represented by the flowchart of  FIGS. 7A-7D  through  FIG. 14  may be implemented manually. Further, although the example machine readable instructions are described with reference to the flowcharts illustrated in  FIGS. 7A-7D  through  FIG. 14 , persons of ordinary skill in the art will readily appreciate that many other techniques for implementing the example methods and apparatus described herein may alternatively be used. For example, with reference to the flowcharts illustrated in  FIGS. 7A-7D  through  FIG. 14 , the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, combined and/or subdivided into multiple blocks. 
     Example machine readable instructions  700  that may be executed to implement the metering engine metric evaluator  652  of  FIG. 6  are shown in  FIGS. 7A-7D . While the example machine readable instructions  700  are based on the decision metrics shown in  FIGS. 16A-16F  and targeted for a monitored television conforming to the NTSC standard, the machine readable instructions may be readily modified to support any type of display/information presentation device. The example machine readable instructions  700  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. The machine readable instructions  700  begin execution at block  701  of  FIG. 7A  at which the metering engine metric evaluator  652  samples the available audio, video and metadata metrics/results obtained, for example, from the audio engines  300 , the video engines  400  and the metadata engines  500 . Control then proceeds to sub-process  702 , discussed in greater detail below in connection with  FIG. 7B , at which the metering engine metric evaluator  652  determines the A/V content source providing the monitored A/V content presentation. After sub-process  702  completes, control then proceeds to sub-process  703 , discussed in greater detail below in connection with  FIG. 7C , at which the metering engine metric evaluator  652  determines content identification information (e.g., tuning data) corresponding to the monitored A/V content presentation provided by the A/V content source identified by sub-process  701 . Next, after sub-process  703  completes, control then proceeds to sub-process  704 , discussed in greater detail below in connection with  FIG. 7D , at which the metering engine metric evaluator  652  detects any special operating modes of the A/V content source identified by sub-process  701 . Finally, after sub-process  704  completes, control proceeds to block  705  at which the metering engine metric evaluator  652  reports the identified A/V content source, the content identification information (e.g., tuning data) and/or any special operating modes of the A/V content source via the outputs  656  and  660  to, for example, a central facility for generation of audience measurement statistics. The example process  700  then ends. 
     An example sub-process  702  to determine which A/V content source is providing a monitored A/V content presentation is shown in  FIG. 7B  and is based on the example decision metrics listed in  FIGS. 16A-16F . The example process  702  begins at decision node  706  at which the metering engine metric evaluator  652  determines whether the video metrics sampled at block  701  of  FIG. 7A  indicate that AMOL information is present in line  20  of an NTSC television signal as processed, for example, by the AMOL processor  512 . If AMOL information in line  20  is present (decision node  706 ) then control proceeds to decision node  707  at which the metering engine metric evaluator  652  detects whether the A/V content is being presented with a timeshift, for example, based on comparing a broadcast timestamp included in the AMOL information with the current processing time. The current processing time may be determined, for example, based on a real-time clock function executing in or a real-time clock apparatus coupled with the metering engine metric evaluator  652 , the multi-engine meter  200  or a similar device. If a timeshift is not detected (decision node  707 ), then control proceeds to block  708  and, based on the presence of AMOL information in line  20  and according to the first analog TV live metric of  FIG. 16A , the metering engine metric evaluator  652  determines that the A/V content source is an analog television broadcast (e.g., terrestrial, cable, etc.). The example sub-process  702  then ends. 
     If, however, a timeshift is detected (decision node  707 ), then control proceeds to decision node  710  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that the monitored audio exhibits spectral shaping consistent with a broadcast analog cable television system. Such a metric may be provided, for example, by the spectral shape processor  332 . If the metering engine metric evaluator  652  determines that cable spectral shaping is present (decision node  710 ), then according to the first analog VOD metric of  FIG. 16A , control proceeds to block  712  and, based on the presence of AMOL information in line  20 , the analog cable spectral shaping and the detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is an analog video-on-demand (VOD) presentation. The example sub-process  702  then ends. If, however, cable spectral shaping is not detected (decision node  710 ), then according to the first VCR playback metric of  FIG. 16B , control proceeds to block  714  and, based on the presence of AMOL information in line  20 , the detected timeshift and lack of cable spectral shaping, the metering engine metric evaluator  652  determines that the A/V content source is a VCR playback. The example sub-process  702  then ends. 
     Returning to decision node  706 , if, however, AMOL information is not present in line  20 , then control proceeds to decision node  718  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that an audio mute state has been detected, for example, by the volume and mute detector  320 . If an audio mute state has not been detected (decision node  718 ) and, thus, an audio signal corresponding to the monitored A/V content is present, control proceeds to decision node  722  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that the monitored audio exhibits spectral shaping consistent with a broadcast analog cable television system. If cable spectral shaping is present (decision node  722 ), then control proceeds to decision node  724  at which the metering engine metric evaluator  652  detects whether the A/V content is being presented with a timeshift. The metering engine metric evaluator  652  may determine whether a timeshift is present based on, for example, comparing a broadcast timestamp included in audio codes embedded in the audio signal with the current processing time. If a timeshift is not detected (decision node  724 ), then according to the second analog TV live metric of  FIG. 16A , control proceeds to block  726  and, based on the presence of the audio signal having cable spectral shaping, the metering engine metric evaluator  652  determines that the A/V content source is an analog television broadcast. The example sub-process  702  then ends. If, however, a timeshift is detected (decision node  724 ), then according to the second analog VOD live metric of  FIG. 16A , control proceeds to block  728  and, based on the presence of audio codes, the analog cable spectral shaping and the detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is an analog VOD transmission. The example sub-process  702  then ends. 
     Returning to decision node  722 , if, however, analog cable spectral shaping is not present, then control proceeds to decision node  730  at which the metering engine metric evaluator  652  determines whether the video metrics indicate that macroblocks have been detected, for example, by the macroblock detector  424 . If macroblocks have been detected (decision node  730 ), then control proceeds to decision node  732  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that the audio signal has been subjected to AC3 compression, for example, as detected by the compression detector  324 . If AC3 compression is detected (decision node  732 ), then according to the DVD playback metric of  FIG. 16B , control proceeds to block  734  and, based on the absence of analog cable spectral shaping and the presence of macroblocks and AC3 compression, the metering engine metric evaluator  652  determines that the A/V content source is a DVD playback. The example sub-process  702  then ends. 
     If, however, AC3 compression is not detected (decision node  732 ), then the metering engine metric evaluator  652  determines there is insufficient information to determine the A/V content source directly from the audio, video and metadata metrics sampled at block  701  of  FIG. 7A . Thus, control proceeds to block  736  at which the metering engine metric evaluator  652  uses previously stored heuristic information to determine the A/V content source. Using stored heuristics to determine the A/V content source is discussed in greater detail below. After processing at block  736  completes, the example sub-process  702  then ends. 
     If, however, macroblocks have not been detected (decision node  730 ), then control proceeds to decision node  737  at which the metering engine metric evaluator  652  determines whether the video metrics indicate that the macroblocking index output by, for example, the macroblock detector  424  is equal to zero, indicative of perfect color matching. If the macroblock index is not equal to zero, control proceeds to decision node  738  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that AC3 compression has been detected. If AC3 compression has not been detected (decision node  738 ), then control proceeds to decision node  740  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that the audio signal has been subjected to MPEG audio compression, for example, as detected by the compression detector  324 . If MPEG audio compression has been detected (decision node  740 ), then control proceeds to decision node  742  at which the metering engine metric evaluator  652  detects whether a timeshift is present, for example, by comparing timestamp information included in the MPEG audio compression data with the current processing time. If a timeshift is not detected (decision node  742 ), then according to the MPEG DVR live metric of  FIG. 16D , control proceeds to block  744  and, based on the presence of MPEG audio compression, the absence of macroblocks and no detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is an MPEG-type DVR outputting a “live” broadcast program. If, however, a timeshift is detected (decision node  742 ), then according to the MPEG DVR playback metric of  FIG. 16D , control proceeds to block  746  and, based on the absence of macroblocks, the presence of MPEG audio compression and the detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is an MPEG-type DVR playing back previously recorded A/V content. If, however, MPEG audio compression has not been detected (decision node  740 ), then according to the second VCR playback metric of  FIG. 16B , control proceeds to block  748  and, due to the absence of macroblocks, audio compression and AMOL information, the metering engine metric evaluator  652  determines that the A/V content source is a VCR playing back pre-recorded A/V content. After processing at either block  744 , block  746  or block  748  completes, the example sub-process  702  then ends. 
     Returning to decision node  738 , if, however, AC3 compression has been detected, control proceeds to decision node  750  at which the metering engine metric evaluator  652  detects whether a timeshift is present, for example, by comparing timestamp information included in the AC3 audio compression data with the current processing time. If a timeshift is detected (decision node  750 ), then according to the digital TV playback metric of  FIG. 16C , control proceeds to block  752  and, based on the absence of macroblocks and the presence of AC3 audio compression and the detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is, for example, a cable television DVR outputting previously recorded A/V content (block  752 ). If, however, a timeshift is not detected (decision node  742 ), then according to the digital TV live metric of  FIG. 16C , control proceeds to block  754  and, based on the presence of AC3 audio compression and the absence of macroblocks and no detected timeshift, the metering engine metric evaluator  652  determines that the A/V content source is a digital cable broadcast (possibly passed through an associated DVR) outputting “live” A/V content. After processing at blocks  752  or  754  completes, the example sub-process  702  then ends. 
     If, however, at decision node  737  the metering engine metric evaluator  652  determined that the macroblock index output by the macroblock detector  424  was equal to zero, control proceeds to block  756 . At block  756 , and according to the video game decision metric of  FIG. 16E , the metering engine metric evaluator  652  determines that the A/V content source is a video game based on the perfect color matching represented by a macroblock index equal to zero. The example sub-process  702  then ends. 
     Returning to decision node  718 , if the metering engine metric evaluator  652  determines that an audio mute state has been detected, for example, by the volume and mute detector  320 , the metering engine metric evaluator  652  may determine that there is insufficient information to determine the A/V content source due to the lack of audio or AMOL information provided by the audio, video and metadata metrics sampled at block  701  of  FIG. 7A . Thus, control proceeds to block  760  at which the metering engine metric evaluator  652  uses previously stored heuristic information to determine the A/V content source. Using stored heuristics to determine the A/V content source is discussed in greater detail below. After processing at block  760  completes, the example sub-process  702  then ends 
     An example sub-process  703  to determine content identification information (e.g., tuning data) corresponding to the content presentation provided by the A/V content source identified, for example, by sub-process  701  of  FIG. 7B  is shown in  FIG. 7C . The content identification information may include, for example, a content/program name, a broadcast time, a broadcast station ID/channel number, etc. The example sub-process  703  begins at decision node  762  at which, for example, the metering engine metric evaluator  652  of  FIG. 6  determines whether the video metrics sampled at block  701  of  FIG. 7A  indicate that AMOL information is present in line  20  of an NTSC television signal as processed, for example, by the AMOL processor  512 . If AMOL information in line  20  is present (decision node  762 ) control then proceeds to block  764  at which the metering engine metric evaluator  652  determines content identification information from detected AMOL information in line  20  based on any appropriate technique, such as those described above in connection with the AMOL processor  512 . The example sub-process  703  then ends. 
     If, however, AMOL information is not present in line  20  (decision node  762 ), control proceeds to decision node  766  at which the metering engine metric evaluator  652  determines whether the video metrics indicate that AMOL information is present in line  22 . If AMOL information in line  22  is present (decision node  766 ) then control proceeds to block  768  at which the metering engine metric evaluator  652  determines content identification information from detected AMOL information in line  22  based on any appropriate technique, such as those described above in connection with the AMOL processor  512 . The example sub-process  703  then ends. 
     If, however, AMOL information is not present in line  22  (decision node  766 ), control proceeds to decision node  770  at which the metering engine metric evaluator  652  determines whether the audio metrics indicate that audio codes are present, for example, as processed by the audio code detector  312  of  FIG. 3 . If audio codes are present (decision node  770 ), then control proceeds to block  772  at which the metering engine metric evaluator  652  determines program identification information from the available audio codes based on any appropriate technique, such as those described above in connection with the audio code detector  312 . The example sub-process  703  then ends. 
     If, however, audio codes are not present (decision node  770 ), control proceeds to block  774  at which the metering engine metric evaluator  652  may determine program identification information by comparing, for example, an audio signature corresponding to the monitored A/V content presentation, and generated by the audio signature processor  316  of  FIG. 3 , to a set of known reference signatures. Additionally or alternatively, the metering engine metric evaluator  652  may output the audio signature corresponding to the monitored A/V content for comparison to a set of known reference signatures at, for example, a central processing facility. Any known technique for generating and comparing signatures may be employed at block  774  to ascertain the desired content identification information, such as those described above in connection with the audio signature processor  316 . In any case, after processing at block  774  completes, the example sub-process  703  then ends. 
     An example sub-process  704  to detect any special operating modes of the A/V content source identified, for example, by sub-process  701  of  FIG. 7B  is shown in  FIG. 7D  and is based on the decision metrics listed in  FIGS. 16A-16F . The special operating modes detected by sub-process  704  include a blank frame mode, an audio mute mode, a pause mode, a menu display mode, a device OFF mode, and a catch-all trick mode indication. The catch-all trick mode indication is used to indicate that the identified A/V content source may be engaged in any number of special trick modes of operation including, for example, a rewind mode, a fast-forward mode, etc. The example sub-process  704  begins at decision node  776  at which the metering engine metric evaluator  652  determines whether the video metrics sampled at block  701  of  FIG. 7A  indicate that the monitored A/V content presentation corresponds to a blank frame as detected, for example, by the scene change and blank frame detector  420  of  FIG. 4 . If a blank frame is not detected (decision node  776 ), control proceeds to decision node  778  at which the metering engine metric evaluator  652  determines whether an audio mute state has been detected, for example, by the volume and mute detector  320 . 
     If an audio mute state is detected (decision node  778 ), then control proceeds to decision node  780  at which the metering engine metric evaluator  652  determines whether the metadata metrics indicate that closed caption or teletext information is present as processed, for example, by the closed caption processor  516  or teletext processor  520 , respectively, of  FIG. 5 . If closed caption or teletext information is not present (decision node  780 ), control then proceeds to decision node  782  at which the metering engine metric evaluator  652  determines whether the video metrics indicate that a pause state has been detected, for example, by the scene change and blank frame detector  420 . If the pause state is not detected (decision node  782 ), then according to the trick mode metric of  FIG. 16F , control proceeds to block  784  and, based on the absence of audio, closed caption information and the pause state, the metering engine metric evaluator  652  determines that the most recently identified A/V content source is operating in a trick mode (because the absence of any audio and a pause in the video indicates an abrupt transition in the presentation of the A/V content). The example sub-process  704  then ends 
     If, however, a pause state is detected (decision node  782 ), control the proceeds to decision node  786  at which the metering engine metric evaluator  652  determines whether the video metrics indicate that the paused video frame matches a known template, for example, as determined by the template matcher  428 , or contains predetermined text, for example, as determined by the text detector  412  of  FIG. 4 . If a template or text match is not detected (decision node  786 ), then according to the pause metric of  FIG. 16F , control proceeds to block  788  and, based on the presence of the pause state and absence of the template or text match, the metering engine metric evaluator  652  determines that the most recently identified A/V content source has entered a pause mode of operation. If, however, a template or text match is detected (decision node  786 ), then according to the menu display decision metric of  FIG. 16F , control proceeds to block  790  and the metering engine metric evaluator  652  determines that the corresponding A/V content source is displaying a menu corresponding to the matched reference template or predetermined text. After processing at block  788  or  790  completes, the example sub-process  704  then ends. 
     Returning to decision node  780 , if, however, closed caption or teletext information is present, then according to audio mute decision metric of  FIG. 16E , control proceeds to block  792  and, based on the presence of closed caption information and the audio mute state, the metering engine metric evaluator  652  determines that the most recently identified A/V content source has entered an audio mute mode of operation. The example sub-process  704  then ends. If, however, at decision node  778  the metering engine metric evaluator  652  determines that the audio is not muted and, thus, an audio signal corresponding to the monitored A/V content presentation is present, then due to the absence of a blank frame determined at decision node  776 , control may proceed to block  794  at which the metering engine metric evaluator  652  may determine that the most recently identified A/V content source is operating in a normal presentation mode. The example sub-process  704  then ends. 
     Returning to decision node  776 , if a blank frame is detected, for example, by the scene change and blank frame detector  420 , control proceeds to decision node  796  at which the metering engine metric evaluator  652  determines whether an audio mute state has been detected, for example, by the volume and mute detector  320 . If an audio mute state is not detected (decision node  796 ), then according to the blank frame decision metric of  FIG. 16E , control proceeds to block  798  and the metering engine metric evaluator  652  determines that the most recently identified A/V content source is displaying a blank frame. However, if an audio mute state is detected (decision node  796 ), then based on the lack of a detected audio signal coupled with a blank video frame, control may proceed to block  799  at which the metering engine metric evaluator  652  may determine that a presentation transition has occurred (e.g., corresponding to a transition between a program and a commercial). If the audio mute and blank frame states are of significant duration, then at block  799  the metering engine metric evaluator  652  may determine that the most recently identified A/V content source has been placed in an OFF state. In any case, after processing at blocks  798  or  799  completes, the example sub-process  704  then ends. 
     Additionally, although not shown in  FIGS. 7A-7D , a multi-engine meter employing the example process  700  or any similar process may use other detected information to ascertain the A/V content source and/or associated content identification information. For example, the multi-engine meter  200  of  FIG. 2  includes a remote control detector  252  to detect and process signals received from a remote control device. The received remote control signals may be decoded and processed to determine, for example, which of a set of possible A/V content sources is being controlled by a user, the operating state of such A/V content source, etc. 
     In certain circumstances, for example, when sufficient metric information is not available, the example machine readable instructions  700  may use stored heuristics to determine the A/V content source, content identification information, etc. For example, a multi-engine meter executing the machine readable instructions  700 , or a similar process, may store statistical information regarding previous A/V content source selection, content identification, etc. The information may be categorized, for example, by time of day, sequence of selection, etc. Then, as shown in  FIG. 7B , in certain circumstances the machine readable instructions  700  may employ a set of heuristic rules to determine the A/V content source based on the stored statistical information. 
     Also, as discussed above, the audio metrics, video metrics and metadata metrics may be updated autonomously and, thus, a particular metric or set of metrics may not be available when the machine readable instructions  700  read the metrics at block  701 . Thus, the machine readable instructions  700  may employ one or more timeout timers to cause one or more audio, video or metadata metrics to be reset to a known state. This mechanism prevents metric information from becoming stale if not updated within an expected/reasonable amount of time. 
     Example machine readable instructions  800  that may be executed to implement the volume and mute detector  320  of  FIG. 3  are shown in  FIG. 8 . The machine readable instructions  800  begin execution at block  804  at which the volume and mute detector  320  reads samples from an audio buffer such as, for example, the audio buffer  216  of  FIG. 2 . For example, the volume and mute detector  320  may read a set of  512  audio samples from the audio buffer  216 . Additionally, the machine readable instructions  800  may be scheduled to execute each time a new set of 512 audio samples has been stored in the audio buffer  216 . After the audio samples have been read, the volume and mute detector  320  then counts the number of zero crossings occurring in the set of samples read from the audio buffer (block  808 ). As is known, a zero crossing is said to occur when a transition from a previous sample to a next sample would require passing through zero. In the case of an audio mute state, the audio samples will typically correspond to quantization noise and, therefore, will tend to fluctuate about zero. Thus, the volume and mute detector  320  determines whether the number of zero crossings exceeds a predetermined threshold indicative of audio mute fluctuation (block  812 ). If the number of zero crossings exceeds the threshold (block  812 ), then the volume and mute detector  320  reports that the monitored audio signal corresponds to an audio mute state (block  816 ). The example process  800  then ends. 
     If, however, the number of zero crossings does not exceed the threshold (block  812 ), then the volume and mute detector  320  determines the energy of the audio samples (block  820 ). The volume and mute detector  320  then compares the audio energy to a predetermined threshold indicative of an audio mute state (block  824 ). If the audio energy is less than this threshold (block  824 ), then the volume and mute detector  320  reports an audio mute state (block  816 ) and the example process  800  ends. If, however, the audio energy is not less than the threshold (block  824 ), then the volume and mute detector  320  reports the volume level of the audio samples, for example, based on quantizing the audio energy to correspond to a set of predetermined volume levels (block  828 ). The example process  800  then ends. 
     Example machine readable instructions  900  that may be executed to implement the compression detector  324  of  FIG. 3  are shown in  FIG. 9 . The machine readable instructions  900  begin execution at block  904  at which the compression detector  324  reads samples from an audio buffer such as, for example, the audio buffer  216  of  FIG. 2 . For example, the compression detector  324  may read a set of  256  audio samples generated by sampling the audio input signals  204  at a rate of 48 kHz, as discussed above, from the audio buffer  216 . Additionally, the machine readable instructions  900  may be scheduled to execute each time a new set of  256  audio samples has been stored in the audio buffer  216 . After the audio samples have been read, the compression detector  324  then computes a modified discrete cosine transform (MDCT) of the audio samples and may quantize the coefficients to correspond, for example, to the quantization used in AC3 audio compression (block  908 ). For example, the compression detector  324  may compute an MDCT having a length of 256 corresponding to 256 MDCT coefficients by processing 512 audio samples with an overlap of 256 samples (e.g., corresponding to 256 “old” samples read during a previous execution of the process  900  and 256 “new” samples read from the audio buffer  216  during the current execution of the process  900 ). Then, for a one second window of audio samples, the compression detector  324  determines the number of MDCT coefficients having a substantially zero value at frequencies greater than a predetermined threshold frequency (block  912 ). The predetermined threshold frequency corresponds to the audio passband associated with AC3 audio compression. Therefore, if the audio samples correspond to an audio signal that has already been subjected to AC3 compression, MDCT coefficients corresponding to frequencies greater than the passband threshold will be substantially equal to zero. In the example described herein, the predetermined threshold frequency corresponds approximately to MDCT coefficient bin  220 . Thus, the compression detector  324  determines whether the number of zero MDCT coefficients in the example frequency region corresponding to the MDCT coefficient bins  220  through 256 is less than 4000 (block  916 ). If the number of zero MDCT coefficients is less than 4000, then the audio signal has not been subjected to compression and the compression detector  324  reports that the monitored A/V content corresponds to a broadcast analog transmission or a VCR playback (block  920 ). The example process  900  then ends. 
     If, however, the number of zero MDCT coefficients is not less than 4000 (block  916 ), then the compression detector  324  determines whether the number of MDCT coefficients in the examined frequency region corresponding to the MDCT coefficient bins  220  through  256  is greater than 6000 (block  924 ). If the number of zero MDCT coefficients exceeds 6000 (block  924 ), then the compression detector  324  determines that the audio signal is substantially equal to zero at these frequencies and, thus, that the audio signal has been subjected to AC3 compression (block  928 ). The example process  900  then ends. However, if the number of zero MDCT coefficients does not exceed 6000 (block  924 ), then the compression detector  324  compares the MDCT coefficients to a stored template corresponding to the frequency response of the subband filters used in MPEG audio compression (block  932 ). If the MDCT coefficients match the template (block  936 ), then the compression detector  324  reports that the audio signal has been subjected to MPEG audio compression (block  940 ). However, if the MDCT coefficients do not match the template (block  936 ), then compression detector  324  reports that the audio signal has been subjected to AC3 compression (block  928 ). The example process  900  then ends. 
     Example machine readable instructions  1000  that may be executed to implement the jingle detector  328  of  FIG. 3  are shown in  FIG. 10 . The machine readable instructions  1000  begin execution at block  1004  at which the jingle detector  328  reads samples from an audio buffer such as, for example, the audio buffer  216  of  FIG. 2 . For example, the jingle detector  328  may read a set of  512  audio samples from the audio buffer  216 . Additionally, the machine readable instructions  1000  may be scheduled to execute each time a new set of  512  audio samples has been stored in the audio buffer  216 . After the audio samples have been read, the jingle detector  328  then compares the audio samples to a set of stored reference templates corresponding to known audio jingles for various possible A/V content sources (block  1008 ). As discussed above, the comparison may be performed using, for example, any known technique for comparing audio signatures. If the audio samples match a template corresponding to a game console jingle (block  1012 ), the jingle detector  328  reports that the A/V content source is a game console (block  1016 ) and the example process  1000  ends. If, however, the audio samples match a template corresponding to an STB jingle (block  1020 ), the jingle detector  328  reports that the A/V content source is an STB (block  1024 ) and the example process  1000  ends. 
     If, however, the audio samples match a template corresponding to a DVD player jingle (block  1028 ), the jingle detector  328  reports that the A/V content source is a DVD player (block  1032 ) and the example process  1000  ends. If, however, the audio samples match a template corresponding to a VCR jingle (block  1036 ), the jingle detector  328  reports that the A/V content source is a VCR (block  1040 ) and the example process  1000  ends. If, however, the audio samples match a template corresponding to a PVR/DVR jingle (block  1044 ), the jingle detector  328  reports that the A/V content source is a PVR/DVR player (block  1048 ) and the example process  1000  ends. However, if the audio samples do not match any of the stored reference templates, then the jingle detector  328  reports that the A/V content source is indeterminate (block  1052 ) and the example process  1000  then ends. 
     Example machine readable instructions  1100  that may be executed to implement the spectral shape processor  332  of  FIG. 3  are shown in  FIG. 11 . The machine readable instructions  1100  begin execution at block  1104  at which the spectral shape processor  332  reads samples from an audio buffer such as, for example, the audio buffer  216  of  FIG. 2 . For example, the spectral shape processor  332  may read a set of  512  audio samples from the audio buffer  216 . Additionally, the machine readable instructions  1100  may be scheduled to execute each time a new set of  512  audio samples has been stored in the audio buffer  216 . After the audio samples have been read, the process  1100  may then proceed along one or both of the following paths. In the case of the first processing path, the spectral shape processor  332  applies a notch filter centered at 15.75 kHz to the audio samples (block  1108 ). The spectral shape processor  332  then determines whether the output of the notch filter exceeds a predetermined threshold (block  1112 ). The predetermined threshold corresponds to spectral leakage expected with an analog cable television system. If the notch filter output exceeds the threshold (block  1112 ), then the spectral shape processor  332  reports that the A/V content source is an analog cable television broadcast (block  1116 ). If, however, the notch filter output does not exceed the threshold (block  1112 ), them the spectral shape processor  332  reports that the A/V content source is indeterminate (block  1120 ). The example process  1100  then ends. 
     In the case of the second processing path, the spectral shape processor  332  computes a frequency spectrum (e.g., based on a fast Fourier transform or FFT) corresponding to the audio samples (block  1124 ). The spectral shape processor  332  then compares the audio frequency spectrum to a template corresponding to the expected frequency response of an analog cable system (block  1128 ). If the audio frequency spectrum matches the template (block  1132 ), then the spectral shape processor  332  reports that the A/V content source is an analog cable television broadcast (block  1136 ). If the audio frequency spectrum does not match the template (block  1132 ), then the spectral shape processor  332  reports that the A/V content source is indeterminate (block  1140 ). The example process  1100  then ends. 
     Example machine readable instructions  1200  that may be executed to implement the scene change and blank frame detector  420  of  FIG. 4  are shown in  FIG. 12 . The machine readable instructions  1200  begin execution at block  1204  at which the scene change and blank frame detector  420  reads samples from a video buffer such as, for example, the video buffer  224  of  FIG. 2 . For example, the video buffer  224  may store video samples corresponding to an input frame rate of 30 frames/sec with a resolution of 640 by 480 pixels. This results in a buffer size of 640×480×3 bytes, where the factor of 3 corresponds to storing 3 colors (e.g., red, green and blue) per pixel, where each color is represented with 1 byte=8 bits. The machine readable instructions  1200  may be scheduled to execute each time the video buffer  224  is filled, which corresponds to processing each sampled video frame. After the video samples have been read, the scene change and blank frame detector  420  computes histograms of the pixel luminance values corresponding to three regions in a first video frame (block  1208 ). A person of ordinary skill in the art will appreciate that fewer or greater than three regions may be used depending on, for example, the size of the regions and the frequency with which the process  1200  is performed. Next, the scene change and blank frame detector  420  computes histograms of the pixel luminance values corresponding to the same three regions but in a second video frame (block  1216 ). Then, the scene change and blank frame detector  420  computes the distance between the histograms of the first frame and the second frame (block  1216 ). For example, this distance may be computed by computing an absolute difference between corresponding histogram bins in the two frames and then summing the absolute differences. 
     The scene change and blank frame detector  420  then compares the histogram distance to a predetermined threshold corresponding to an expected luminance change associated with a scene change (block  1220 ). If the histogram distance exceeds the threshold (block  1220 ), then the scene change and blank frame detector  420  reports that a scene change has occurred (block  1224 ). Additionally, the scene change and blank frame detector  420  may determine the number of scene changes that occur per unit time (block  1228 ). If, however, the histogram distance does not exceed the threshold (block  1220 ), then the scene change and blank frame detector  420  determines whether the histograms are dominated by a black luminance value (or range of values) (block  1232 ). If black is not dominant (block  1232 ), then the scene change and blank frame detector  420  reports that the current video frame corresponds to a paused state (block  1236 ). However, if black is dominant (block  1232 ), then the scene change and blank frame detector  420  reports that a blank frame has occurred (block  1240 ). Additionally, the scene change and blank frame detector  420  may determine the number of blank frames that occur per unit time ( 1244 ). The number of blank frames per unit time may be used to determine, for example, whether the monitored video corresponds to a transition from broadcast content to the insertion of a commercial. The example process  1200  then ends. 
     An example process  1300  that may be used to implement the macroblock detector  424  of  FIG. 4  is shown in  FIG. 13 . The process  1300  begins at block  1304  at which the macroblock detector  424  reads samples from a video buffer such as, for example, the video buffer  224  of  FIG. 2 . For example, the video buffer  224  may store video samples corresponding to an input frame rate of 30 frames/sec with a resolution of 640 by 480 pixels. This results in a buffer size of 640×480×3 bytes, where the factor of 3 corresponds to storing 3 colors (e.g., red, green and blue) per pixel, where each color is represented with 1 byte=8 bits. The process  1300  may be scheduled to process, for example, every 10 th  sampled video frame. 
     As discussed above, MPEG video compression introduces macroblocks in the video image. For example, macroblocks may be 16 pixels by 16 pixels in size. Macroblocks tend to have different average (DC) luminance values that may be used to detect the presence of macroblocking in a video image. To detect the presence of macroblocking, the macroblock detector  424  computes the interpixel difference in the horizontal and/or vertical directions of a video image (block  1308 ). The macroblock detector  424  then computes the power spectral density (PSD) of the computed interpixel differences (block  1312 ). Next, the macroblock detector  424  median filters the PSD (block  1316 ), computes the difference between the original PSD and the median filtered PSD ( 1320 ) and sums the differences (block  1324 ). Median filtering is known and may be used to smooth transitions in an image. For example, a 3 by 3 median filter replaces a given pixel with the median of the nine pixels adjacent to and including the given pixel. Therefore, due to the different average values of different macroblocks, a video image exhibiting macroblocking will have a large sum of PSD differences as compared to a video image that does not exhibit macroblocking. 
     Thus, the macroblock detector  424  then compares the sum of PSD differences to a predetermined threshold set to detect macroblocking (block  1328 ). If the sum of PSD differences exceeds the threshold (block  1328 ), then the macroblock detector  424  detects macroblocking and reports that the monitored video signal has been subjected to video compression (block  1332 ). If, however, the sum of PSD differences does not exceed the threshold (block  1328 ), the macroblock detector  424  determines whether the sum is substantially equal to zero (block  1336 ). A substantially zero sum is indicative of perfect color matching, typically associated with video game content. Thus, if the sum of PSD differences is substantially zero (block  1336 ), the macroblock detector  424  reports that the A/V content source corresponds to a game console (block  1340 ). Otherwise, the macroblock detector  424  reports that the A/V content source is indeterminate (block  1344 ). The example process  1300  then ends. 
     Example machine readable instructions  1400  that may be executed to implement the template matcher  428  of  FIG. 4  are shown in  FIG. 14 . The machine readable instructions  1400  begin execution at block  1404  at which the template matcher  428  reads samples from a video buffer such as, for example, the video buffer  224  of  FIG. 2 . For example, the video buffer  224  may store video samples corresponding to an input frame rate of 30 frames/sec with a resolution of 640 by 480 pixels. This results in a buffer size of 640×480×3 bytes, where the factor of 3 corresponds to storing 3 colors (e.g., red, green and blue) per pixel, where each color is represented with 1 byte=8 bits. The machine readable instructions  1400  may be structured to process, for example, every 10 th  sampled video frame. After the video samples have been read, the template matcher  428  then compares the video samples to a set of stored reference templates corresponding to known video frames (e.g., menu frames) for various possible A/V content sources (block  1408 ). If the video samples match a template corresponding to a reference game console video frame (block  1412 ), the template matcher  428  reports that the A/V content source is a game console (block  1416 ) and the example process  1400  ends. If, however, the video samples match a template corresponding to a reference STB video frame (block  1420 ), the template matcher  428  reports that the A/V content source is an STB (block  1424 ) and the example process  1400  ends. 
     If, however, the video samples match a template corresponding to a reference DVD player video frame (block  1428 ), the template matcher  428  reports that the A/V content source is a DVD player (block  1432 ) and the example process  1400  ends. If, however, the video samples match a template corresponding to a reference VCR video frame (block  1436 ), the template matcher  428  reports that the A/V content source is a VCR (block  1440 ) and the example process  1400  ends. If, however, the video samples match a template corresponding to a reference PVR/DVR video frame (block  1444 ), the template matcher  428  reports that the A/V content source is a PVR/DVR (block  1448 ) and the example process  1400  ends. However, if the video samples do not match any of the stored reference templates, then the template matcher  428  reports that the A/V content source is indeterminate (block  1452 ) and the example process  1400  then ends. 
       FIG. 15  is a block diagram of an example computer  1500  capable of implementing the apparatus and methods disclosed herein. The computer  1500  can be, for example, a server, a personal computer, a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a personal video recorder, a set top box, or any other type of computing device. 
     The system  1500  of the instant example includes a processor  1512  such as a general purpose programmable processor. The processor  1512  includes a local memory  1514 , and executes coded instructions  1516  present in the local memory  1514  and/or in another memory device. The processor  1512  may execute, among other things, the machine readable instructions represented in  FIGS. 7A-7D  through  FIG. 14 . The processor  1512  may be any type of processing unit, such as one or more microprocessor from the Intel® Centrino® family of microprocessors, the Intel® Pentium® family of microprocessors, the Intel® Itanium® family of microprocessors, and/or the Intel XScale® family of processors. Of course, other processors from other families are also appropriate. 
     The processor  1512  is in communication with a main memory including a volatile memory  1518  and a non-volatile memory  1520  via a bus  1522 . The volatile memory  1518  may be implemented by Static Random Access Memory (SRAM), 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  1520  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  1518 ,  1520  is typically controlled by a memory controller (not shown) in a conventional manner. 
     The computer  1500  also includes a conventional interface circuit  1524 . The interface circuit  1524  may be implemented by any type of well known interface standard, such as an Ethernet interface, a universal serial bus (USB), and/or a third generation input/output (3GIO) interface. 
     One or more input devices  1526  are connected to the interface circuit  1524 . The input device(s)  1526  permit a user to enter data and commands into the processor  1512 . The input device(s) can be implemented by, for example, a keyboard, a mouse, a touchscreen, a track-pad, a trackball, an isopoint and/or a voice recognition system. 
     One or more output devices  1528  are also connected to the interface circuit  1524 . The output devices  1528  can be implemented, for example, by display devices (e.g., a liquid crystal display, a cathode ray tube display (CRT)), by a printer and/or by speakers. The interface circuit  1524 , thus, typically includes a graphics driver card. 
     The interface circuit  1524  also includes a communication device such as a modem or network interface card to facilitate exchange of data with external computers via a network (e.g., an Ethernet connection, a digital subscriber line (DSL), a telephone line, coaxial cable, a cellular telephone system, etc.). 
     The computer  1500  also includes one or more mass storage devices  1530  for storing software and data. Examples of such mass storage devices  1530  include floppy disk drives, hard drive disks, compact disk drives and digital versatile disk (DVD) drives. The mass storage device  1530  may implement the audio metric registers  616 - 620 , the video metric registers  624 - 628  and/or the metadata metric registers  632 - 636 . Alternatively, the volatile memory  1518  may implement the audio metric registers  616 - 620 , the video metric registers  624 - 628  and/or the metadata metric registers  632 - 636 . 
     At least some of the above described example methods and/or apparatus are implemented by one or more software and/or firmware programs running on a computer processor. However, dedicated hardware implementations including, but not limited to, application specific integrated circuits (ASICs), programmable logic arrays (PLAs) and other hardware devices can likewise be constructed to implement some or all of the example methods and/or apparatus described herein, either in whole or in part. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the example methods and/or apparatus described herein. 
     It should also be noted that the example software and/or firmware implementations described herein are optionally stored on a tangible storage medium, such as: a magnetic medium (e.g., a magnetic disk or tape); a magneto-optical or optical medium such as an optical disk; or a solid state medium such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories; or a signal containing computer instructions. A digital file attached to e-mail or other information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, the example software and/or firmware described herein can be stored on a tangible storage medium or distribution medium such as those described above or successor storage media. 
     Additionally, although this patent discloses example systems including software or firmware executed on hardware, it should be noted that such systems are merely illustrative and should not be considered as limiting. For example, it is contemplated that any or all of these hardware and software components could be embodied exclusively in hardware, exclusively in software, exclusively in firmware or in some combination of hardware, firmware and/or software. Accordingly, while the above specification described example systems, methods and articles of manufacture, persons of ordinary skill in the art will readily appreciate that the examples are not the only way to implement such systems, methods and articles of manufacture. Therefore, although certain example methods, apparatus and articles of manufacture have been described 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 appended claims either literally or under the doctrine of equivalents.