Gracenote, Inc.

Nicola Marcantonio

Markus Kurt Cremer

Dale T Roberts

Bob Coover

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.

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San Francisco, CA, United States of America

Nicola Marcantonio