An example apparatus to monitor media that is presented via a mobile device includes: an application monitor to determine when media presentation software is being executed by the mobile device; and a metering information collector to collect at least one of a signature or a code of the media presented by the media presentation software based on the media presentation software being executed by the mobile device.

Systems and methods are provided for determining a media cost equivalent to attribute to the appearance of a sponsor's logo or other object in a media item, such as an image or video. This determination may be based on factors such as clarity, prominence, visibility, size, or placement of a sponsor logo or other object related to the sponsor within at least one media item. One or more machine learning models may be utilized to automatically identify objects within, and otherwise analyze, image data in order to determine sponsor value.

Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

Techniques for regional viewership predictions of broadcast events such as live broadcast professional sporting events. The techniques can make the predictions without a direct response variable such as regional viewership data for training a prediction model. Instead, in one technique, demand information for a good or service is used. From the demand information, a derivative demand for the good or service relative to a normal demand is determined. A regression framework is used to learn relationships between the derivative demand for the good or service and features of past live broadcast sporting events. This results in a matrix of feature weights. A non-parametric mixture framework is then used to find a set of feature weights that can be applied to features of future broadcast events to generate regional viewership predictions for the events.

Methods and apparatus to calibrate media ratings based on return path data are disclosed. An apparatus includes a processor and memory including instructions that, when executed, cause the processor to: determine an initial rating for the media provided in a first geographic area based on return path data (RPD) tuning information obtained from RPD devices in subscriber households in the first geographic area; determine a first panelist rating for the media provided in a second geographic area based on first panel tuning information obtained from first metering devices in a first subset of panelist households in the second geographic area; determine a nonsubscriber calibration factor based on the first panelist rating; and determine a final rating for the media in the first geographic area by modifying the initial rating based on the nonsubscriber calibration factor.

Methods, apparatus, systems and articles of manufacture to determine audio source impact on an audience of media are disclosed. A disclosed example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to: divide audio of monitored media into successive audio segments; perform speaker identification on the audio segments; generate confidence values for speakers identified in the audio segments; generate speaker identification data for ones of the speakers having respective confidence values that satisfy a threshold; and analyze the speaker identification data to determine a speaker impact on audience ratings data.

Example methods and apparatus to detect audio engineering problems using identification of isolated watermarks are disclosed herein. Disclosed example methods include identifying, by executing an instruction with a processor, isolated watermarks in a first set of detected watermarks obtained from first media monitored at a first site. Disclosed example methods further include determining, by executing an instruction with the processor, a problem has been detected with the first media associated with the first set of detected watermarks in response to a first number of isolated watermarks identified in the first set of detected watermarks satisfying a first threshold.

Example methods and apparatus to detect audio engineering problems using identification of isolated watermarks are disclosed herein. Disclosed example methods include identifying, by executing an instruction with a processor, isolated watermarks in a first set of detected watermarks obtained from first media monitored at a first site. Disclosed example methods further include determining, by executing an instruction with the processor, a problem has been detected with the first media associated with the first set of detected watermarks in response to a first number of isolated watermarks identified in the first set of detected watermarks satisfying a first threshold.

Aspects of the subject disclosure may include, for example, capturing first monitoring information associated with an audience that is viewing a presentation, determining an actual audience engagement level by performing pattern recognition on the first monitoring information, comparing the actual audience engagement level with an expected audience engagement level to generate an engagement score for the presentation, and transmitting, to a device, a haptic feedback according to the engagement score during the presenting of the presentation. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, capturing first monitoring information associated with an audience that is viewing a presentation, determining an actual audience engagement level by performing pattern recognition on the first monitoring information, comparing the actual audience engagement level with an expected audience engagement level to generate an engagement score for the presentation, and transmitting, to a device, a haptic feedback according to the engagement score during the presenting of the presentation. Other embodiments are disclosed.