US 12,354,623 B2
Engagement measurement of media consumers based on the acoustic environment
Meryem Berrada, Clearwater, FL (US); and John Stavropoulos, Edison, NJ (US)
Assigned to The Nielsen Company (US), New York, NY (US)
Filed by The Nielsen Company (US), LLC, New York, NY (US)
Filed on Jul. 10, 2023, as Appl. No. 18/349,796.
Application 18/349,796 is a continuation of application No. PCT/US2022/011652, filed on Jan. 7, 2022.
Application 18/349,796 is a continuation of application No. 17/571,261, filed on Jan. 7, 2022.
Claims priority of provisional application 63/135,389, filed on Jan. 8, 2021.
Prior Publication US 2024/0079026 A1, Mar. 7, 2024
Int. Cl. G10L 25/00 (2013.01); G10L 15/06 (2013.01); G10L 15/08 (2006.01); G10L 15/22 (2006.01); G10L 25/51 (2013.01)
CPC G10L 25/51 (2013.01) [G10L 15/063 (2013.01); G10L 15/08 (2013.01); G10L 15/22 (2013.01); G10L 2015/088 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computing system comprising:
a processor; and
a memory storing instructions that, upon execution by the processor, cause the computing system to perform operations comprising:
identifying media device audio data and ambient environment audio data from sensed audio data collected from a media exposure environment where a consumer is exposed to media through a media device;
determining classification data for the media device audio data and the ambient environment audio data;
processing the classification data with a machine learning model to calculate an engagement metric, wherein the machine learning model is trained based on a combination of (i) second sensed audio data collected by a media device meter and (ii) panelist survey data that is time aligned with the second sensed audio data; and
determining whether the consumer is paying attention to media presentation through the media device in the media exposure environment based on the engagement metric.