A customized programming content creation system is disclosed. According to one embodiment, a media content search engine locates and retrieves media segments, and a storage component stores a user location indicator. A processor accesses scheduling information data structures for the media segments, and filters the media segments to generate a set of current local media segments. The filtering is based on a comparison of a user-associated location value with a geography value from the scheduling information data structure, and a comparison of a current time value with an expiration value from the scheduling information data structure. The set of current local media segments includes at least one local weather segment. The media segments are also filtered to identify at least one current news media segment. An integrated video stream is assembled. The stream is then transmitted for presentation on a user device of the user.

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.

Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.

Techniques for projecting household-level viewing events are described herein. Population data may be accessed including classes of a plurality of demographic attributes for households in a market. Representative household units (RHUs) may be generated, and the RHUs may be assigned a class for each of the demographic attributes and a quota based on the demographic attributes of a plurality of panelist households. Each of the panelist households may be assigned to one of the RHUs based on at least one panelist classes matching the classes for respective demographic attributes of the RHU, and the number of matching panelist households assigned to each of the RHU may be based on the quota. Panelist viewing data representing viewing events associated with the panelist household may be accessed. A report may be generated with the classes of the RHUs and the panelist viewing data of the assigned panelist households.

Conventional television audience measurements are made with diaries or by imaging the area in front of a television and trying to identify the people in the images watching television. Unfortunately, diaries are only accurate if the audience members record entries in them, and image-based techniques are intrusive. The present techniques address these problems by using a viewer's wearable device to measure a viewer's proximity to a television. The wearable device emits or receives low-power beacon signals; measurements of the signal strength are used to calculate the viewer's distance to the television. If the viewer is close enough to the television and the television is on, the viewer may be engaged with the content on the television. This system is simple, non-intrusive, and can used to measure engagement with each television in a multi-television household.

Conventional television audience measurements are made with diaries or by imaging the area in front of a television and trying to identify the people in the images watching television. Unfortunately, diaries are only accurate if the audience members record entries in them, and image-based techniques are intrusive. The present techniques address these problems by using a viewer's wearable device to measure a viewer's proximity to a television. The wearable device emits or receives low-power beacon signals; measurements of the signal strength are used to calculate the viewer's distance to the television. If the viewer is close enough to the television and the television is on, the viewer may be engaged with the content on the television. This system is simple, non-intrusive, and can used to measure engagement with each television in a multi-television household.

Disclosed examples include: determining periods of loudness in an audio signal by: determining whether a difference between a first amplitude at a first time and a second amplitude at a second time satisfies a loudness threshold; in response to the difference satisfying the loudness threshold, setting a loudness flag to a loud state corresponding to the second time, the second time occurring after the first time; determining whether a third amplitude at a third time after the second time satisfies a quiet threshold; in response to the third amplitude satisfying the quiet threshold, setting the loudness flag to a quiet state corresponding to the third time; and determining periods of loudness for the audio signal based on the states of the loudness flag at the second and third times; and indicating the audio signal as spillover based on the periods of loudness.

Disclosed examples include: determining periods of loudness in an audio signal by: determining whether a difference between a first amplitude at a first time and a second amplitude at a second time satisfies a loudness threshold; in response to the difference satisfying the loudness threshold, setting a loudness flag to a loud state corresponding to the second time, the second time occurring after the first time; determining whether a third amplitude at a third time after the second time satisfies a quiet threshold; in response to the third amplitude satisfying the quiet threshold, setting the loudness flag to a quiet state corresponding to the third time; and determining periods of loudness for the audio signal based on the states of the loudness flag at the second and third times; and indicating the audio signal as spillover based on the periods of loudness.

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.