Disclosed is a method of associating, at a secondary device, secondary media content with primary media content being output at a primary device. The method includes receiving, at the secondary device, first information based upon the primary content being output at the primary device, wherein the first information includes at least one of an audio and a visual signal, determining at the secondary device second information corresponding to the first information, receiving at the secondary device one or more portions of secondary media content that have been made available by a third device, determining at the secondary device whether one or more of the portions of the secondary media content match one or more portions of the second information, and taking at least one further action upon determining that there is a match.

Systems and methods for a media guidance application that adjusts output parameters of media assets delivered to output devices based on user preferences of users near the output devices. For example, the media guidance application may adjust the volume to be higher at a speaker near a first user who enjoys a particular media asset and lower at a speaker near a second user who dislikes the media asset.

Systems and methods for a media guidance application that adjusts output parameters of media assets delivered to output devices based on user preferences of users near the output devices. For example, the media guidance application may adjust the volume to be higher at a speaker near a first user who enjoys a particular media asset and lower at a speaker near a second user who dislikes the media asset.

Methods and apparatus to improve the accuracy of crediting media exposure through detecting reverberation indicative of spillover are disclosed. An example apparatus includes a reverberation analyzer to identify a quantity of short durations of loudness in an audio signal of media presented by a media presentation device and calculate a ratio of the quantity of the short durations of loudness to a quantity of durations of loudness in the audio signal of the media, the quantity of the durations of loudness including the quantity of short durations of loudness. The example apparatus also includes a processor and memory in circuit with the processor, the memory including instructions that, when executed by the processor, cause the processor to mark the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold.

Methods and apparatus to improve the accuracy of crediting media exposure through detecting reverberation indicative of spillover are disclosed. An example apparatus includes a reverberation analyzer to identify a quantity of short durations of loudness in an audio signal of media presented by a media presentation device and calculate a ratio of the quantity of the short durations of loudness to a quantity of durations of loudness in the audio signal of the media, the quantity of the durations of loudness including the quantity of short durations of loudness. The example apparatus also includes a processor and memory in circuit with the processor, the memory including instructions that, when executed by the processor, cause the processor to mark the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold.

Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber. Other embodiments are disclosed.

A computer-implemented method includes: identifying, by a computing device, one or more participants associated with a live streaming video, wherein the one or more participants are co-located; monitoring, by the computing device, behavior of each of the one or more participants, wherein the monitoring comprises monitoring sensor data associated with the one or more participants; automatically determining, by the computing device, respective reactions of each of the one or more participants based on the monitoring the behavior; and providing, by the computing device, respective visual representations of the respective reactions of each of the one or more participants for display within a user interface that is presenting the live streaming video.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement neural network processing of set-top box return path data to estimate household demographics are disclosed. Example demographic estimation systems disclosed herein include a feature generator to generate features from return path data reported from set-top boxes associated with return path data households. Disclosed example demographic estimation systems also include a neural network to process the features generated from the return path data to predict demographic classification probabilities for the return path data households, the neural network to be trained based on panel data reported from meters that monitor media devices associated with panelist household. Disclosed example demographic estimation systems further include a demographic assignment engine to assign one or more demographic categories to respective ones of the return path data households based on the predicted demographic classification probabilities.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement neural network processing of set-top box return path data to estimate household demographics are disclosed. Example demographic estimation systems disclosed herein include a feature generator to generate features from return path data reported from set-top boxes associated with return path data households. Disclosed example demographic estimation systems also include a neural network to process the features generated from the return path data to predict demographic classification probabilities for the return path data households, the neural network to be trained based on panel data reported from meters that monitor media devices associated with panelist household. Disclosed example demographic estimation systems further include a demographic assignment engine to assign one or more demographic categories to respective ones of the return path data households based on the predicted demographic classification probabilities.

In one embodiment, a computer-implemented method of creating customized programming content for a user of a video content system includes accessing a user interest profile for the user, the user interest profile comprising a ranked list of a plurality of interest categories; locating at least one video segment corresponding to each of the interest categories of the user interest profile; calculating the correlation between the user interest profile and data describing each of the located video segment, and ranking the video segments based on the correlation; assembling the video segments into a customized video programming stream based on the ranking; and displaying the customized video programming stream to the user.