Gracenote, Inc.

Roku, Inc.

The Nielsen Comapny (us), LLC

Other

Fraunhofer-gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Sony Corporation

Markus Kurt Cremer

Peter Dunker

Shashank C Merchant

Robert Coover

Steven D Scherf

Peter C DiMaria

Wilson Harron

Kurt Roman Thielen

Aneesh Vartakavi

Cameron Aubrey Summers

Dewey Ho Lee

Rishabh Sharma

Zafar Rafii

Dale T Roberts

Suresh Jeyachandran

Vadim Brenner

Ching-Wei Chen

Michael Gubman

Phillip Popp

Todd J Hodges

Paul Emmanuel Quinn

Konstantinos Antonios Dimitriou

Sherman Ling Fung Li

David Henry Pearce

Randall E Cook

Bongjun Kim

Roger Tsai

Michael Jeffrey

Jeffrey William Scott

Donald F Gordon

Joseph Renner

Irene Zhu

Jaehyung Lee

Seunghyeong Lee

Andy Mai

Chungwon Seo

Dong-In Lee

Jeff Benson

Craig Kawahara

Kyogu Lee

Gregoire Tronel

Joshua Ernest Morris

Oscar Celma Herrada

Andreas Schmidt

Bob Coover

Barnabas Mink

Tanji Koshio

Kei Tsuji

Michael Becker

Alexander Pavlovich Topchy

David Howell Wright

Jeremey M Davis

Prem Seetharaman

Jonathan Chazen

Sharmishtha Gupta

Michael Yeehua Chien

Ho-Hsiang Wu

Warren Mansfield

Nicola Marcantonio

Cherisse Ponraj

Chris Curtis

Matthew McCallum

Jason Timothy Cramer

Claas Derboven

Sebastian Streich

Stephen F White

Marc Theeuwes

Jason Cramer

Donald J Gordon

Michael W Mantle

Sherman Li

Kostantinos Dimitriou

A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.

Responding to remote media classification queries using classifier models and context parameters

Methods, apparatus, systems and articles of manufacture are disclosed to determine audio quality. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal. Example apparatus disclosed herein also include an EQ analyzer to access a plurality of equalization settings determined by the neural network based on the query; and compare the equalization settings to an equalization threshold to determine if the audio signal is to be removed from subsequent processing.

Methods and apparatus to determine audio quality

A clustering machine can cluster descriptive vectors in a balanced manner. The clustering machine calculates distances between pairs of descriptive vectors and generates clusters of vectors arranged in a hierarchy. The clustering machine determines centroid vectors of the clusters, such that each cluster is represented by its corresponding centroid vector. The clustering machine calculates a sum of inter-cluster vector distances between pairs of centroid vectors, as well as a sum of intra-cluster vector distances between pairs of vectors in the clusters. The clustering machine calculates multiple scores of the hierarchy by varying a scalar and calculating a separate score for each scalar. The calculation of each score is based on the two sums previously calculated for the hierarchy. The clustering machine may select or otherwise identify a balanced subset of the hierarchy by finding an extremum in the calculated scores.

Selecting balanced clusters of descriptive vectors

Methods, apparatus, systems and articles of manufacture are disclosed for a broadcast profiling system. An example apparatus includes a memory storing instructions, and a processor configured to execute the instructions stored in the memory to compare a preference included in a user profile with a portion of a content station profile to determine whether the preference included in the user profile satisfies a threshold difference from the portion of the content station profile, in response to the threshold difference being satisfied, generate a station recommendation for a user associated with the user profile, and transmit an instruction to a device associated with the user, the instruction including the station recommendation, the instruction configured to cause a radio pre-set to be adjusted.

Broadcast profiling system

Techniques of content unification are disclosed. In some example embodiments, a computer-implemented method comprises: determining clusters based a comparison of a plurality of audio content using a first matching criteria, each cluster of the plurality of clusters comprising at least two audio content from the plurality of audio content; for each cluster of the plurality of clusters, determining a representative audio content for the cluster from the at least two audio content of the cluster; loading the corresponding representative audio content of each cluster into an index; matching the query audio content to one of the representative audio contents using a first matching criteria; determining the corresponding cluster of the matched representative audio content; and identifying a match between the query audio content and at least one of the audio content of the cluster of the matched representative audio content based on a comparison using a second matching criteria.

Multiple stage indexing of audio content

A user of a network-based system may correspond to a user profile that describes the user. The user profile may describe the user using one or more descriptors of items that correspond to the user (e.g., items owned by the user, items liked by the user, or items rated by the user). In some situations, such a user profile may be characterized as a 'taste profile' that describes an array or distribution of one or more tastes, preferences, or habits of the user. Accordingly, the user profile machine within the network-based system may generate the user profile by accessing descriptors of items that correspond to the user, clustering one or more of the descriptors, and generating the user profile based on one or more clusters of the descriptors.

User profile based on clustering tiered descriptors

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

In one aspect, an example method includes (i) presenting, by a playback device, first media content from a first source; (ii) encountering, by the playback device, a trigger to switch from presenting the first media content from the first source to presenting second media content from a second source; (iii) determining, by the playback device, that the playback device is presenting the first media content from the first source in a muted state; and (iv) responsive to encountering the trigger, and based on the determining that the playback device is presenting the first media content from the first source in a muted state, presenting, by the playback device, the second media content from the second source in the muted state.

Detection of mute and compensation therefor during media replacement event

Apparatus, methods, and systems that operate to provide interactive streaming content identification and processing are disclosed. An example apparatus includes a classifier to determine an audio characteristic value representative of an audio characteristic in audio; a transition detector to detect a transition between a first category and a second category by comparing the audio characteristic value to a threshold value among a set of threshold values, the set of threshold values corresponding to the first category and the second category; and a context manager to control a device to switch from a first fingerprinting algorithm to a second fingerprinting algorithm different than the first fingerprinting algorithm, responsive to the detected transition between the first category and the second category.

Machine-control of a device based on machine-detected transitions

Methods, apparatus and articles of manufacture to identify sources of network streaming services are disclosed. An example method includes receiving a first audio signal that represents a decompressed second audio signal, identifying, from the first audio signal, a parameter of an audio compression configuration used to form the decompressed second audio signal, and identifying a source of the decompressed second audio signal based on the identified audio compression configuration.

Methods, apparatus, and articles of manufacture to identify sources of network streaming services

Example methods and systems for modifying the playback of content using pre-processed profile information are described. Example instructions, when executed, cause at least one processor to access a media stream that includes media and a profile of equalization parameters, the media stream provided to a device via a network, the profile of equalization parameters included in the media stream selected based on a comparison of a reference fingerprint to a query fingerprint generated based on the media, the profile of equalization parameters including an equalization parameter for the media; and modify playback of the media based on the equalization parameter specified in the accessed profile.

Modifying playback of content using pre-processed profile information

Methods, apparatus, systems and articles of manufacture are disclosed to adjust device control information. The example apparatus comprises a light drive waveform generator to obtain metadata corresponding to media and generate device control information based on the metadata, the device control information to inform a lighting device to enable consecutive light pulses; an effect engine to apply an attack parameter and a decay parameter to consecutive light pulses corresponding to the device control information, the attack parameter and the decay parameter based on the metadata to affect a shape of the consecutive light pulses; and a color timeline generator to generate color information based on the metadata, the color information to inform the lighting device to change a color state.

Methods and apparatus to control light pulsing effects based on media content

Methods, apparatus and articles of manufacture to identify sources of network streaming services are disclosed. An example apparatus includes a coding format identifier to identify, from a received first audio signal representing a decompressed second audio signal, an audio compression configuration used to compress a third audio signal to form the second audio signal, and a source identifier to identify a source of the second audio signal based on the identified audio compression configuration.

Methods, apparatus and articles of manufacture to identify sources of network streaming services

In an example implementation, a method is described. The implementation accesses first and second media clips. The implementation also matches a first fingerprint of the first media clip with a second fingerprint of the second media clip and determines an overlap of the first media clip with the second media clip. The implementation also, based on the overlap, merges the first and second media clips into a group of overlapping media clips, transmits, to a client device, data identifying the group of overlapping media clips and specifying a synchronization of the first media clip with the second media clip, and generates for display on a display device of the client computing device, a graphical user interface that identifies the group of overlapping media clips, specifies the synchronization of the first media clip with the second media clip, and allows access to, and manipulation of, the first and second media clips.

Synthesizing a presentation from multiple media clips

A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Machine-led mood change

Growing community of inventors

Orinda, CA, United States of America

Markus Kurt Cremer