Gracenote, Inc.

Alexander Berrian

Robert Coover

Matthew James Wilkinson

Zafar Rafii

Todd J Hodges

Methods, apparatus, systems, and articles of manufacture are disclosed to fingerprint an audio signal via exponential normalization. An example apparatus includes an audio segmenter to divide an audio signal into a plurality of audio segments including a first audio segment and a second audio segment, the first audio segment including a first time-frequency bin, the second audio segment including a second time-frequency bin, a mean calculator to determine a first exponential mean value associated with the first time frequency bin based on a first magnitude of the audio signal associated with the first time frequency bin and a second exponential mean value associated with the second time frequency bin based on a second magnitude of the audio signal associated with the second time frequency bin and the first exponential mean value. The example apparatus further includes a bin normalizer to normalize the first time-frequency bin based on the second exponential mean value and a fingerprint generator to generate a fingerprint of the audio signal based on the normalized first time-frequency bins.

Methods and apparatus to fingerprint an audio signal via exponential normalization

A method implemented by a computing system comprises generating, by the computing system, a fingerprint comprising a plurality of bin samples associated with audio content. Each bin sample is specified within a frame of the fingerprint and is associated with one of a plurality of non-overlapping frequency ranges and a value indicative of a magnitude of energy associated with a corresponding frequency range. The computing system removes, from the fingerprint, a plurality of bin samples associated with a frequency sweep in the audio content.

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Emeryville, CA, United States of America

Alexander Berrian