Gracenote, Inc.

Prem Seetharaman

Zafar Rafii

Markus Kurt Cremer

Robert Coover

Example systems and methods are audio identification based on data structure are disclosed. An example apparatus includes memory, and one or more processors to execute instructions to execute a constant Q transform on query time slices of query audio, binarize the constant Q transformed query time slices, execute a two-dimensional Fourier transform on query time windows within the binarized and constant Q transformed query time slices to generate two-dimensional Fourier transforms of the query time windows, sequentially order the two-dimensional Fourier transforms in a query data structure, and identify the query audio as a cover rendition of reference audio based on a comparison between the query data structure and a reference data structure associated with the reference audio.

Audio identification based on data structure

Example systems and methods for automated cover song identification are disclosed. An example apparatus includes at least one memory, machine-readable instructions, and one or more processors to execute the machine-readable instructions to at least execute a constant Q transform on time slices of first audio data to output constant Q transformed time slices, binarize the constant Q transformed time slices to output binarized and constant Q transformed time slices, execute a two-dimensional Fourier transform on time windows within the binarized and constant Q transformed time slices to output two-dimensional Fourier transforms of the time windows, generate a reference data structure based on a sequential order of the two-dimensional Fourier transforms, store the reference data structure in a database, and identify a query data structure associated with query audio data as a cover rendition of the audio data based on a comparison of the query and reference data structures.

Automated cover song identification

Example systems and methods for automated cover song identification are disclosed. An example apparatus includes memory, and one or more processors to execute instructions to identify query audio from a content source based on a search query using rights metadata associated with the query audio, execute a constant Q transform on query time slices of the query audio, binarize the constant Q transformed query time slices, execute a two-dimensional Fourier transform on query time windows within the binarized and constant Q transformed query time slices to generate two-dimensional Fourier transforms of the query time windows, generate a query data structure based on a sequential order of the two-dimensional Fourier transforms, select a subset including reference audio of a reference database based on the rights metadata, and identify the query audio as a cover rendition of the reference audio based on a comparison between the query and reference data structures.

Example systems and methods represent audio using a sequence of two-dimensional (2D) Fourier transforms (2DFTs), and such a sequence may be used by a specially configured machine to perform audio identification, such as for automated cover song identification. Such systems and methods are robust to timbral changes, time skews, and pitch skews that occur in cover songs found in content repositories. The systems and methods allow copyright holders to search the content repositories for unlicensed cover song.

Example systems and methods represent audio using a sequence of two-dimensional (2D) Fourier transforms (2DFTs), and such a sequence may be used by a specially configured machine to perform audio identification, such as for cover song identification. Such systems and methods are robust to timbral changes, time skews, and pitch skews. In particular, a special data structure provides a time-series representation of audio, and this time-series representation is robust to key changes, timbral changes, and small local tempo deviations. Accordingly, the systems and methods described herein analyze cross-similarity between these time-series representations. In some example embodiments, such systems and methods extract features from an audio fingerprint and calculate a distance measure that is robust and invariant to changes in musical structure.

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Chicago, IL, United States of America

Prem Seetharaman