The Nielsen Comapny (us), LLC

Arbitron Inc.

Alan R Neuhauser

Jack C Crystal

Wendell D Lynch

John Steven Stavropoulos

Vladimir Kuznetsov

Anand Jain

David Gish

James M Jensen

Jack K Zhang

Eugene L Flanagan

Daniel W Pugh

Sadhana Gupta

Douglas J Visnius

Taymoor Arshi

William K Krug

William John McKenna

Jason Bolles

John G Kelly

Thomas W White

Ronald S Kolessar

Michael Tenbrock

David Patrick Forr

Joan Fitzgerald

Mark Richardson

Vijoy K Gopalakrishnan

Joan G Fitzgerald

Eugene L Flanagan Iii

Example apparatus disclosed herein include a watermark detector to detect watermarks in a media signal. Disclosed example apparatus also include a controller to operate the watermark detector to (1) detect a first watermark in the media signal, and (2) cycle between sleep intervals and active intervals based on a repetition rate of the watermarks in the media signal to perform a detection operation for a second watermark at a second location in the media signal relative to a first location of the first watermark in the media signal. In some examples, the controller is to search a buffer of prior detected watermark symbols to detect a third watermark at a third location prior to the second location in the media signal in response to the second watermark not being detected at the second location in the media signal.

Power efficient detection of watermarks in media signals

Example methods, apparatus and articles of manufacture to determine semantic information for audio are disclosed. Example apparatus disclosed herein are to process an audio signal obtained by a media device to determine values of a plurality of features that are characteristic of the audio signal, compare the values of the plurality of features to a first template having corresponding first ranges of the plurality of features to determine a first score, the first template associated with first semantic information, compare the values of the plurality of features to a second template having corresponding second ranges of the plurality of features to determine a second score, the second template associated with second semantic information, and associate the audio signal with at least one of the first semantic information or the second semantic information based on the first score and the second score.

Audio processing techniques for semantic audio recognition and report generation

Apparatuses are disclosed for identifying portable devices carried by the same person. An example apparatus includes at least one memory, instructions on the apparatus, and a processor to execute the instructions to access media exposure data from at least one of a plurality of portable computing devices, access motion strings from each of the plurality of portable computing devices, each of said strings comprising a successive binary representation of motion over a first period of time, compare the motion strings in a processor to determine if at least two motion strings match within a predetermined threshold, and identify the devices that produced matching motion strings.

Multiple meter detection and processing using motion data

Example systems, methods, apparatus and articles of manufacture for wirelessly modifying detection characteristics of portable devices are disclosed herein. Example portable devices include example radio circuitry and example processor circuitry. In disclosed examples, the example processor circuitry is to cause the radio circuitry to scan for radio frequency beacons. The example processor circuitry is also to trigger activation of audio monitoring via a microphone of the portable device in response to establishment of a first connection with a first radio frequency beacon, the audio monitoring to at least one of detect an audio code or generate an audio signature. The example processor circuitry is further to cause the audio monitoring to stop in response to establishment of a second connection with a second radio frequency beacon different from the first radio frequency beacon.

Systems and methods for wirelessly modifying detection characteristics of portable devices

Example methods, apparatus, and articles of manufacture for monitoring use by a user of a portable research device in accordance with at least one predetermined use criterion are disclosed. The disclosed examples include passively gathering data for assessing an identity of a user of the portable research device, processing the passively gathered data to produce assessment data indicating a possibility that the user is not a predetermined correct user of the portable research device, based on the assessment data, displaying a message to the user requesting a response from which the user's identity may be determined, and processing a response to the message to produce data indicating whether the user is the predetermined correct user.

Methods and systems for compliance confirmation and incentives

Systems and methods are disclosed for customizing, distributing and processing audio signature data. Examples disclosed herein include waking a portable device from an inactive state in response to an activation signal. Disclosed examples also include generating a first signature based on audio from a microphone of the portable device during a period of time specified by the activation signal. Disclosed examples further include comparing the first signature with a second signature obtained from the activation signal to determine a match score, and communicating a match result based on the match score to a server via a network.

Distributed audience measurement systems and methods

Methods, apparatus and articles of manufacture for research data gathering are disclosed. An example apparatus disclosed herein is to detect whether the apparatus is powered by an internal power source or an external power source. The example apparatus is also to, in response to detecting the apparatus is powered by the internal power source, perform first processing on a received audio signal to determine audio data to store in storage of the apparatus. The example apparatus is further to, in response to detecting the apparatus is powered by the external power source, perform second processing on the stored audio data to recover the code, the second processing different from the first processing.

Research data gathering

Example articles of manufacture and apparatus for producing supplemental information for audio signature data are disclosed herein. An example apparatus includes memory including computer readable instructions. The example apparatus also includes a processor to execute the instructions to at least obtain first audio signature data associated with a first time period of media, obtain first semantic signature data associated with the first time period of the media and second semantic signature data associated with a second time period of the media, and when second audio signature data associated with the second time period of the media is unavailable, identify the media based on the first audio signature data associated with the first time period of media when the second semantic signature data associated with the second time period matches the first semantic signature data associated with the first time period of the media.

Audio matching with semantic audio recognition and report generation

Example systems, methods, apparatus and articles of manufacture for wirelessly modifying detection characteristics of portable devices are disclosed herein. Example portable devices disclosed here include a microphone and radio frequency circuitry to scan for a radio frequency beacon at a first scanning rate. Disclosed example apparatus also include a processor to trigger activation of audio monitoring, in the portable device, of an audio signal obtained via the microphone. In some disclosed examples, the audio monitoring is to be triggered in response to a first connection being made via the radio frequency circuitry with a first transmitter of the radio frequency beacon. In some disclosed examples, the audio monitoring is to collect research data from the audio signal.

Systems and methods are disclosed for identifying portable devices carried by the same person. An example system includes receiving media exposure data over a data network from at least one of a plurality of portable computing devices, motion strings to be received over the data network respectively from each of the plurality of portable computing devices, each of the strings including a successive binary representation of motion over a first period of time, the first period of time divided into a plurality of sub-periods of time, and comparing the motion strings to determine if at least two motion strings match within a predetermined threshold, and identify the devices that produced matching motion strings as being physically carried by a same person.

Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement power efficient detection of watermarks in media signals are disclosed. Example metering apparatus disclosed herein include detector means for detecting watermarks in a media signal. Disclosed example metering apparatus also include controller means for operating the detector means to (1) detect a first watermark in the media signal, and (2) cycle between sleep intervals and active intervals to detect a second watermark at a second location in the media signal relative to a first location of the first watermark in the media signal after the first watermark has been verified.

Systems and methods are disclosed for identifying portable devices carried by the same person. An example method includes receiving media exposure data from one of a plurality of portable computing devices. Receiving segmented accelerometer data from the plurality of portable computing devices. Forming accelerometer classification data for the plurality of portable computing devices based on features extracted from the segmented accelerometer data. Comparing the accelerometer classification data for the first portable computing device to accelerometer classification data for the plurality of portable computing devices, to determine if the accelerometer classification data for the first portable computing device is sufficiently similar to a second portable computing devices. The example method further includes identifying the first and second portable computing device as being physically carried by a same person when the comparison determines that the accelerometer classification data for the first portable computing device is sufficiently similar to the second computing devices.

Growing community of inventors

Silver Spring, MD, United States of America

Alan R Neuhauser