Gracenote, Inc.

Jason Timothy Cramer

Markus Kurt Cremer

Cameron Aubrey Summers

Phillip Popp

Aneesh Vartakavi

Carmen Yaiza Rancel Gil

Anjana Gopakumar

Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.

Methods and apparatus to generate recommendations based on attribute vectors

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

A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that the query corresponds to a specified 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, a query can be analyzed for various characteristics, such as a noise profile, before it is input to the network-based classifier. If the query has greater than a specified threshold noise characteristic, then a successful classification can be unlikely and a classification process based on the query can be terminated before computational resources are expended. Query signals that meet or exceed a threshold condition can be provided to the network-based classifier for media classification. In an example embodiment, a remote device or a central media classifier circuit can determine a noise profile for a query.

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Brooklyn, NY, United States of America

Jason Timothy Cramer