Brian A. Whitman - Somerville MA, US Barry Vercoe - Natick MA, US
Assignee:
Massachusetts Institute of Technology - Cambridge MA
International Classification:
A63H 5/00
US Classification:
84609, 84600, 84603, 84615, 84616
Abstract:
There are disclosed methods and apparatus for understanding music. A classifier machine may be trained for each of a plurality of selected terms using a first plurality of music samples. The classifier machines may then be tested using a second plurality of music samples. The results from testing the classifier machines may then be used to select a plurality of semantic basis function from the selected terms. A semantic basis classifier machine may then be trained for each semantic basis function.
Automatically Acquiring Acoustic And Cultural Information About Music
There are disclosed methods, apparatus, and storage media for building a music metadata library. Acoustic metadata for one or more music tracks may be automatically acquired from a plurality of client computing devices. Cultural metadata for the one or more music tracks may be automatically acquired by searching Internet-accessible content. The acquired acoustic and cultural metadata may be stored in the music metadata library.
Determining The Similarity Of Music Using Cultural And Acoustic Information
Brian Whitman - Somerville MA, US Tristan Jehan - Somerville MA, US
Assignee:
The Echo Nest Corporation - Somerville MA
International Classification:
G06F 7/00 G06F 17/30
US Classification:
707749, 707802, 707916
Abstract:
There is disclosed a method and storage media for determining the similarity between a first music collection and a second music collection. The similarity may be determined by comparing metadata associated with the first music collection and metadata associated with the second music collection. A plurality of comparison algorithms may be used including at least one of a comparison algorithm based on acoustic metadata and a comparison algorithm based on cultural metadata. The result of the plurality of comparison algorithms may be combined.
Automatically Acquiring Acoustic Information About Music
Methods, apparatus, and storage media to build a music metadata library from music collections stored on a plurality of client computing devices. A music server may receive, from a first client computing device of the plurality of client computing device, a unique identifier for a track stored in the first client computing device. The music server may determine if the track is a known track corresponding to acoustic metadata that is already contained in the music metadata library. When the track is not a known track, the music server may send a message indicating the track is a new track to the first client computing device. The music server may then receive acoustic metadata for the track from the first client computing device and store the received acoustic metadata in the music metadata library.
Brian Whitman - Cambridge MA, US Andrew Nesbit - London, GB Daniel Ellis - New York NY, US
Assignee:
The Echo Nest Corporation - Summerville MA
International Classification:
A63H 5/00 G04B 13/00 G10H 7/00
US Classification:
84609
Abstract:
A method for fingerprinting an unknown music sample is disclosed. A plurality of known tracks may be segmented into reference samples. A reference fingerprint including a plurality of codes may be generated for each reference sample. An inverted index including, for each possible code value, a list of reference samples having reference fingerprints that contain the respective code value may be generated. An unknown fingerprint including a plurality of codes may be generated from the unknown music sample. A code match histogram may list candidate reference samples and associated scores, each score indicating a number of codes from the unknown fingerprint that match codes in the reference fingerprint. Time difference histograms may be generated for two or more reference samples having the highest scores. A determination may be made whether or not a single reference sample matches the unknown music sample based on a comparison of the time difference histograms.
Demographic And Media Preference Prediction Using Media Content Data Analysis
Methods, systems and computer program products are provided for predicting data. A name or title is obtained from a taste profile. There is an index into a data set based on the name or title, and a set of terms and corresponding term weights associated with the name or title are retrieved. A sparse vector is constructed based on the set of terms and term weights. The sparse vector is input to a training model including target data. The target data includes a subset of test data which has a correspondence to a predetermined target metric of data. A respective binary value and confidence level is output for each term, corresponding to an association between the term and the target metric.
Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain.
Methods, systems and computer program products are provided for cross-media recommendation by store a plurality of taste profiles corresponding to a first domain and a plurality of media item vectors corresponding to a second domain. An evaluation taste profile in the first domain is applied to a plurality of models that have been generated based on relationship among the plurality of taste profiles and the plurality of media item vectors, and obtain a plurality of resulting codes corresponding to at least one of the plurality of media item vectors in the second domain.
Bliss Brothers Dairy Inc Attleboro, MA 2005 to 2012 Delivery Driver/Wholesale SupervisorSygma Network Inc Westborough, MA 2004 to 2005 Delivery DriverBlackstone Valley Security Providence, RI 2001 to 2003 Security OfficerAmerican Medical Response E Providence, RI 2000 to 2001 Chair Car DriverUnited States Marine Corps Jacksonville, NC 1996 to 2000 Administrative Clerk E-4 Corporal
Name / Title
Company / Classification
Phones & Addresses
Brian Whitman Treasurer
THE ECHO NEST CORPORATION Custom Computer Programing
48 Grv St SUITE 206, Somerville, MA 02144 39 Skehan St, Somerville, MA 02143 6176280233
Cambridge, MA New York, NY Hillsdale, NJ San Francisco, CA
Work:
The Echo Nest - Co-Founder & CTO (2005) NEC Research Institute - Research Scientist (2000-2001) MIT Media Lab - Research Assistant (2001-2005)
Education:
Massachusetts Institute of Technology - Media Arts & Sciences, Columbia University - Computer Science, Worcester Polytechnic Institute - English Literature
Brian Whitman
Work:
Nuance Communications (2004)
Education:
Rutgers University
Brian Whitman
Brian Whitman
Brian Whitman
Brian Whitman
Brian Whitman
Brian Whitman
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Alan Ritchson & Brian Whitman - Adam Carolla ...
00:00 Alan Ritchson Adam welcomes Alan Ritchson to the pod right at th...
Duration:
1h 51m 59s
Whitman makes Adam Carolla cry
Duration:
2m 50s
Brian Whitman addresses his exit, after 8 1/2...
Duration:
14m 15s
Symphony #1 in D
this. this is my magnum opus.
Duration:
33m 56s
Full Session: Rick Dees & Dr. Viscott (Adam C...
Duration:
7m 59s
Changing Music One Listener at a Time: Brian ...
Brian teaches computers to listen to music. He was a frustrated electr...
The firm is thrilled to be joining the streaming music company. "Since founding The Echo Nest, Brian Whitman and Tristan Jehan have created a company completely and beautifully obsessed with understanding the world of music to help fans discover more music," said The Echo Nest CEO Jim Lucchese.
When I approached liberal activist John Podesta about appearing on KRLAs Heidi Harris Show along with me and my co-hosts Heidi Harris (conservative) and Brian Whitman (liberal), his handler quickly intervened. Later, the handler stopped by to clarify: did the show have two liberals and one conser
Date: Sep 09, 2012
Category: U.S.
Source: Google
The Echo Nest Raises Over $17 Million in New Financing to Bring Big Data to Music
preferences and psychographic attributes. As one example of the many applications of this, The Echo Nest demonstrated a predictive correlation between musical activity and political affiliation. More detail is available in a blog post published today by the Echo Nest's CTO Brian Whitman (http://notes.variogr.