Gracenote, Inc.

Bob Coover

Markus Kurt Cremer

Dale T Roberts

Nicola Marcantonio

Michael Gubman

Andy Mai

Jeff Benson

Craig Kawahara

Jinyu Han

Methods and apparatus for audio identification during a performance are disclosed herein. An example apparatus includes at least one memory and at least one processor to transform a segment of audio into a log-frequency spectrogram based on a constant Q transform using a logarithmic frequency resolution, transform the log-frequency spectrogram into a binary image, each pixel of the binary image corresponding to a time frame and frequency channel pair, each frequency channel representing a corresponding quarter tone frequency channel in a range from C3-C8, generate a matrix product of the binary image and a plurality of reference fingerprints, normalize the matrix product to form a similarity matrix, select an alignment of a line in the similarity matrix that intersects one or more bins in the similarity matrix with the largest calculated Hamming similarities, and select a reference fingerprint based on the alignment.

Audio identification during performance

Methods and apparatus for audio identification during a performance are disclosed herein. An example method includes transforming a segment of audio into a log-frequency spectrogram based on a transform using a logarithmic frequency resolution. The log-frequency spectrogram is transformed into a binary image, each pixel of the binary image corresponding to a time frame and frequency channel pair. A matrix product of the binary image and a plurality of reference fingerprints is computed. Based on a number of frequency ranges represented in the binary image, the matrix product to form a similarity matrix is computed. Selecting an alignment of a line in the similarity matrix that intersects one or more bins in the similarity matrix with the largest calculated Hamming similarities. A reference fingerprint is selected based on the alignment.

As a user is being presented with interactive media by a presenting device, a separate monitoring device may be used to monitor the presentation of the interactive media and detect an event that occurs therein. Such a monitoring device may be configured and positioned to access media content from the presentation of the interactive media. For example, the monitoring device may be configured and positioned to record video content with a camera and record audio content with a microphone. Having accessed this media content, the monitoring device may generate an identifier, such as a fingerprint or watermark, of the media content and compare the generated identifier with a reference identifier that is generated from the source of the media content. Based on the generated identifier matching the reference identifier, the monitoring device may detect that an event has occurred within the interactive media presentation and present a corresponding notification.

Detecting an event within interactive media including spatialized multi-channel audio content

A machine may be configured to generate one or more audio fingerprints of one or more segments of audio data. The machine may access audio data to be fingerprinted and divide the audio data into segments. For any given segment, the machine may generate a spectral representation from the segment; generate a vector from the spectral representation; generate an ordered set of permutations of the vector; generate an ordered set of numbers from the permutations of the vector; and generate a fingerprint of the segment of the audio data, which may be considered a sub-fingerprint of the audio data. In addition, the machine or a separate device may be configured to determine a likelihood that candidate audio data matches reference audio data.

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Orinda, CA, United States of America

Bob Coover