Methods, systems, and apparatuses for audience metric determination are described herein. An audience segment may be targeted for delivery of content. A clustering algorithm may be used to categorize a quantity of users or devices into subsets based on a propensity to consume, present or output a particular type of content, and a quantity of time to output the particular type of content. A weight may be assigned to each subset based on its relevance to other subsets, such as based on data variance, e.g., on a distance to a midpoint of a specific subset of the subsets. An index parameter may be determined for the datasets, e.g., based on each weight for each subset, and data may be generated that reflects a ranking of content delivery spots for delivery of content to the audience segment.

Methods, apparatus, and systems to collect audience measurement data are disclosed. An example system includes at least one non-transitory machine readable storage medium including instructions which, when executed, cause a machine to at least: generate behavior data developed during a first time period based on first media data and user data corresponding to one or more users of a household, the user data to include demographic information for the one or more users associated with the household, identify second media data during a second time period different than the first time period, the second media data identified without identification of the one or more users of the household, and associate the demographic information to the second media data based on the behavior data generated during the first time period associated with the one or more users.

Methods, apparatus, and systems to collect audience measurement data are disclosed. An example system includes at least one non-transitory machine readable storage medium including instructions which, when executed, cause a machine to at least: generate behavior data developed during a first time period based on first media data and user data corresponding to one or more users of a household, the user data to include demographic information for the one or more users associated with the household, identify second media data during a second time period different than the first time period, the second media data identified without identification of the one or more users of the household, and associate the demographic information to the second media data based on the behavior data generated during the first time period associated with the one or more users.

Methods, apparatus, systems, and articles of manufacture to monitor media are disclosed. An example apparatus includes first means for performing a first mapping of a first media identifier and timestamp to a second media identifier in a look-up table (LUT), the first media identifier and timestamp based on media obtained from a server, the first media identifier to identify the media, means for determining a third media identifier and a second timestamp based on media monitoring information (MMI) obtained from the server, the MMI obtained from the server in response to an access of the media identified by the third media identifier by a media device, second means for performing a second mapping of the third media identifier and the second timestamp to the first media identifier and timestamp in the LUT, and means for crediting access of the media to the media device based on the second mapping.

Methods, apparatus, systems, and articles of manufacture to monitor media are disclosed. An example apparatus includes first means for performing a first mapping of a first media identifier and timestamp to a second media identifier in a look-up table (LUT), the first media identifier and timestamp based on media obtained from a server, the first media identifier to identify the media, means for determining a third media identifier and a second timestamp based on media monitoring information (MMI) obtained from the server, the MMI obtained from the server in response to an access of the media identified by the third media identifier by a media device, second means for performing a second mapping of the third media identifier and the second timestamp to the first media identifier and timestamp in the LUT, and means for crediting access of the media to the media device based on the second mapping.

Methods, apparatus, systems and articles of manufacture are disclosed selectively generating and storing hashed reference signatures. An example method disclosed herein determining whether watermark coverage of a first media segment satisfies a dropout constraint, the first media segment corresponding to a first monitoring time interval of a media source feed and, when the watermark coverage of the first media segment does not satisfy the dropout constraint hashing first media signatures associated with the first media segment to generate corresponding first hashed signatures and generating first reference data for the first media segment, the first reference data including the first hashed signatures and the first media signatures. The example method further includes, when the watermark coverage of the first media segment satisfies the dropout constraint, generating second reference data for the first media segment, the second reference data including the first media signatures.

Example methods, apparatus, systems and articles of manufacture to implement an addressable measurement framework are disclosed. Example apparatus disclosed herein perform a common homes analysis of provider data and panel data to determine a coverage footprint associated with the provider data, the provider data including at least one of return path data reported by a plurality of set-top boxes or automatic content recognition data reported by a plurality of smart media devices, and the panel data reported by media device meters. Disclosed example apparatus also weight a portion of the provider data based on the common homes analysis, weight a portion of the panel data based on the common homes analysis, and calculate an addressable advertisement rating based on the weighted portion of the provider data and the weighted portion of the panel data.

Methods, apparatus and articles of manufacture to identify sources of network streaming services are disclosed. An example apparatus includes a coding format identifier to identify, from a received first audio signal representing a decompressed second audio signal, an audio compression configuration used to compress a third audio signal to form the second audio signal, and a source identifier to identify a source of the second audio signal based on the identified audio compression configuration.

Methods, apparatus and articles of manufacture to identify sources of network streaming services are disclosed. An example apparatus includes a coding format identifier to identify, from a received first audio signal representing a decompressed second audio signal, an audio compression configuration used to compress a third audio signal to form the second audio signal, and a source identifier to identify a source of the second audio signal based on the identified audio compression configuration.

Disclosed examples determine a duplicated audience size representative of panelists exposed to television media and digital media; determine a panel duplication reach based on the duplicated audience size and a panelist population; determine a did-not-view reach based on a television audience size, a digital audience size, the duplicated audience size, and the panelist population; obtain an overlap multiplier as a ratio of (1) a product of the panel duplication reach and the did-not-view reach and (2) a product of a television panel reach and a digital panel reach; determine a duplication factor for a media item based on a television audience reach, a digital audience reach, and the overlap multiplier; and determine a total audience for the media item based on the television audience reach, the digital audience reach, and the duplication factor.