Abstract: Methods, apparatus, systems and articles of manufacture to measure engagement of media consumers based on acoustic environment are disclosed. Example apparatus disclosed herein are to identify media device audio data and ambient environment audio data from sensed audio data collected from an environment, and determine classification data for the media device audio data and the ambient environment audio data. Disclosed example apparatus are also to process the classification data with a machine learning model to calculate an engagement metric. Disclosed example apparatus are further to determine whether at least one individual is engaged with media in the environment based on the engagement metric.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to detect a presence status. An example apparatus includes media identification circuitry to generate first signatures representative of first audio data associated with a monitored media device, a comparator to obtain second signatures from a portable meter, the second signatures representative of second audio data sensed by the portable meter and compare the first signatures and the second signatures to determine a comparison result, presence detection circuitry to determine a presence status of a user based on the comparison result, the user associated with the portable meter, and network communication circuitry to transmit the presence status to a data processor to perform audience measurement based on the presence status.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to detect a presence status. An example apparatus includes media identification circuitry to generate first signatures representative of first audio data associated with a monitored media device, a comparator to obtain second signatures from a portable meter, the second signatures representative of second audio data sensed by the portable meter and compare the first signatures and the second signatures to determine a comparison result, presence detection circuitry to determine a presence status of a user based on the comparison result, the user associated with the portable meter, and network communication circuitry to transmit the presence status to a data processor to perform audience measurement based on the presence status.

Abstract: Methods, apparatus, systems, and articles of manufacture to measure facial attention are disclosed. An example apparatus includes at least one memory, machine readable instructions, and processor circuitry to at least one of instantiate or execute the machine readable instructions to identify facial landmarks from input image data, the facial landmarks corresponding to coordinates of landmarks of a face detected in the input image data, determine a first distance between a first facial landmark of the facial landmarks and a second facial landmark of the facial landmarks, determine a second distance between the first facial landmark and a third facial landmark of the facial landmarks, and compare a quotient of the first distance and the second distance to a threshold to determine an attentiveness metric.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to detect a presence status. An example apparatus includes media identification circuitry to generate first signatures representative of first audio data associated with a monitored media device, a comparator to obtain second signatures from a portable meter, the second signatures representative of second audio data sensed by the portable meter and compare the first signatures and the second signatures to determine a comparison result, presence detection circuitry to determine a presence status of a user based on the comparison result, the user associated with the portable meter, and network communication circuitry to transmit the presence status to a data processor to perform audience measurement based on the presence status.

Abstract: Methods, apparatus, systems and articles of manufacture to measure engagement of media consumers based on acoustic environment are disclosed. Example apparatus disclosed herein are to identify media device audio data and ambient environment audio data from sensed audio data collected from an environment, and determine classification data for the media device audio data and the ambient environment audio data. Disclosed example apparatus are also to process the classification data with a machine learning model to calculate an engagement metric. Disclosed example apparatus are further to determine whether at least one individual is engaged with media in the environment based on the engagement metric.