Google Inc.

Rovi Technologies Corporation

Uvic Industry Partnerships Inc.

Moodlogic, Inc.

George Tzanetakis

Matthew Nirvan Sharifi

Dominik Roblek

Gheorghe Postelnicu

Jay Yagnik

Sergey Ioffe

Rehan M Khan

Annie Chen

Robert Van Rooyen

Robotic drummers include voice coil actuators that are coupled to linear-to-rotary motion convertors to produce drumstick rotations so as to strike a drum head. Such rotations can be triggered via a microprocessor using stored performance data, by a user with a mouse, trackpad, joystick, or other user input device. Performances are enhanced by driving the VCA with drive signals have random variations associated with strike timing, amplitude, location, and speed. Multiple strikes are provided by reducing, eliminating, or reversing drumstick rotation with a corresponding drive signal upon detection of drumstick contact with the drum head.

Robotic drummer

This disclosure relates to transformation invariant media matching. A fingerprinting component can generate a transformation invariant identifier for media content by adaptively encoding the relative ordering of interest points in media content. The interest points can be grouped into subsets, and stretch invariant descriptors can be generated for the subsets based on ratios of coordinates of interest points included in the subsets. The stretch invariant descriptors can be aggregated into a transformation invariant identifier. An identification component compares the identifier against a set of identifiers for known media content, and the media content can be matched or identified as a function of the comparison.

Transformation invariant media matching

Systems and methods for noise based interest point density pruning are disclosed herein. The systems include determining an amount of noise in an audio sample and adjusting the amount of interest points within an audio sample fingerprint based on the amount of noise. Samples containing high amounts of noise correspondingly generate fingerprints with more interest points. The disclosed systems and methods allow reference fingerprints to be reduced in size while increasing the size of sample fingerprints. The benefits in scalability do not compromise the accuracy of an audio matching system using noise based interest point density pruning.

Noise based interest point density pruning

Systems and methods for generating unique pitch-resistant descriptors for audio clips are provided. In one or more embodiments, a descriptor for an audio clip is generated as a function of relative magnitudes between interest points within the audio clip's time-frequency representation. A number of techniques for leveraging the relative magnitudes to generate descriptors are considered. These techniques include ordering of interest points as a function of ascending or descending magnitude, creation of binary vectors based on magnitude comparisons between pairs of points, and calculation of quantized magnitude ratios between pairs of points. Descriptors generated based on relative magnitudes according to the techniques disclosed herein are relatively invariant to common transformations to the original audio clip, such as pitch shifting, time stretching, global volume changes, equalization, and/or dynamic range compression.

Magnitude ratio descriptors for pitch-resistant audio matching

Systems and methods for audio matching are disclosed herein. In one embodiment, a system includes both interest point mixing and fingerprint mixing by using multiple interest point detection methods in parallel. Since multiple interest point detection methods are used in parallel, accuracy of audio matching is improved across a wide variety of audio signals. In addition the scalability of the disclosed audio matching system is increased by matching the fingerprint of an audio sample with a fingerprint of a reference sample versus matching an entire spectrogram. Accordingly, a more accurate and more general solution to audio matching can be accomplished.

Ensemble interest point detection for audio matching

System and methods for characterizing interest points within a fingerprint are disclosed herein. The systems include generating a set of interest points and an anchor point related to an audio sample. A quantized absolute frequency of an anchor point can be calculated and used to calculate a set of quantized ratios. A fingerprint can then be generated based upon the set of quantized ratios and used in comparison to reference fingerprints to identify the audio sample. The disclosed systems and methods provide for an audio matching system robust to pitch-shift distortion by using quantized ratios within fingerprints rather than solely using absolute frequencies of interest points. Thus, the disclosed system and methods result in more accurate audio identification.

Frequency ratio fingerprint characterization for audio matching

System and methods for intelligently pruning interest points are disclosed herein. The systems include generating a plurality of distorted audio samples and associated distorted interest points based upon a clean audio sample. Interest points that are common to sets of distorted interest points are retained with interest points not robust to distortion discarded. The disclosed systems and methods therefore can provide for a scalable audio matching solution by eliminating interest points in reference sample fingerprints. The set of pruned interest points are robust to distortion and the benefits of both scalability and accuracy can be had.

Intelligent interest point pruning for audio matching

A method and system for analyzing audio files is provided. Plural audio file feature vector values based on an audio file's content are determined and the audio file feature vectors are stored in a database that also stores other pre-computed audio file features. The process determines if the audio files feature vectors match the stored audio file vectors. The process also associates a plurality of known attributes to the audio file.

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Victoria, Canada

George Tzanetakis