Abstract: The invention allows to generate music as a real time continuation of an input sequence of music data, through a continuation phase comprising the steps of:
Abstract: An electronic device comprising a processing unit arranged to determine an estimation signal (y(k)) based on an input signal (x(k)) and based on a non-stationary reference signal (s0(k)).
Abstract: The method serves to automatically identify in a set of data sequences at least one specific type of information contained in each data sequence of the set, wherein the type of information has an unknown presentation in the data sequences.
Abstract: An electronic device comprising a processing unit arranged to determine an estimation signal (y(k)) based on an input signal (x(k)) and based on a non-stationary reference signal (s0(k)).
Abstract: The invention serves to store at least one sequence of information (2) formed of a succession of information items, such as music titles, in which an artistic or rational link is considered to exist between at least some pairs of adjacent items in the succession. The apparatus comprises an input (I1) for receiving the sequence of information (2), and a storage unit (32) for storing it.
Abstract: A method and a system for generating sequencing information representing a sequence of items selected in a database. Similarity relation techniques are applied between the items.
Type:
Grant
Filed:
September 27, 2001
Date of Patent:
October 31, 2006
Assignee:
SONY France S.A.
Inventors:
Francois Pachet, Daniel Cazaly, Pierre Roy
Abstract: The invention enables to generate a general function (4) which can operate on an input signal (Sx) to extract from the latter a value (DVex) of a global characteristic value expressing a feature (De) of the information conveyed by that signal.
Abstract: Methods, systems and computer program products are provided for testing a lead sheet for plagiarism. A test lead sheet receiving having a plurality of passages is received at receiving a plagiarism detector. A set of annotations describing a level of plagiarism of a plurality of elements (e.g., chord sequence, subsequences, melodic fragments (i.e., notes), rhythm, harmony, etc.) of the test lead sheet in relation to the preexisting lead sheets are generated and output via an output device.
Abstract: Methods, systems and computer program products are provided for testing a lead sheet for plagiarism. A test lead sheet receiving having a plurality of passages is received at receiving a plagiarism detector. A set of annotations describing a level of plagiarism of a plurality of elements (e.g., chord sequence, subsequences, melodic fragments (i.e., notes), rhythm, harmony, etc.) of the test lead sheet in relation to the preexisting lead sheets are generated and output via an output device.
Abstract: Meta-data (tags) for an audiovisual file can be generated by prompting a user to input certain tags (meta-data) descriptive of the audiovisual file, to serve as an initial estimate of the tags, and then revising the initial estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged.
Type:
Application
Filed:
November 20, 2009
Publication date:
May 20, 2010
Applicant:
SONY FRANCE S.A.
Inventors:
Jean-Julien Aucouturier, Pierre Roy, Francois Pachet
Abstract: Meta-data (tags) for an audiovisual file can be generated by prompting a user to input certain tags (meta-data) descriptive of the audiovisual file, to serve as an initial estimate of the tags, and then revising the initial estimate (notably to expand it and/or render it more precise) based on the assumption that the relationships which hold between the different tags for a set of manually-tagged training examples will also hold for the tags of the input file now being tagged.
Type:
Grant
Filed:
November 20, 2009
Date of Patent:
November 27, 2012
Assignee:
Sony France S.A.
Inventors:
Jean-Julien Aucouturier, Pierre Roy, Francois Pachet
Abstract: Methods, systems and computer program products are provided for identifying an audio stem. Audio stems (t1, . . . , tN) are stored on a stem database and songs (S1, . . . , SP) made with at least a subset of the plurality of the audio stems (t1, . . . , tN) are stored on a song database. At least partially composed song (S*) having a predetermined number of pre-selected stems (k) are received. In turn, a probability vector (or relevance value or ranking) is produced for each stem (t1, . . . , tN) to be complementary to the at least partially composed song (S*).
Type:
Application
Filed:
September 19, 2019
Publication date:
March 25, 2021
Applicant:
Spotify AB
Inventors:
François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan, Juan José Bosch Vicente
Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.
Type:
Application
Filed:
September 19, 2019
Publication date:
March 25, 2021
Applicant:
Spotify AB
Inventors:
Juan José Bosch Vicente, François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan
Abstract: A content-management interface is implemented using superposition of graphical “widget” elements onto an on-screen display of the real-time image of a user. The widgets are motion-sensitive control elements which are responsive to user movement that produces motion of the user's displayed image at an on-screen location associated with that widget. The user can operate the widget virtually, for example by moving his hand over a widget, or pushing a widget along. Meta-data descriptive of the content to be managed is mapped onto widgets, enabling the user to interact with the meta-data, notably by changing it or by selecting content with reference to it.
Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.
Type:
Grant
Filed:
March 16, 2021
Date of Patent:
January 31, 2023
Assignee:
Spotify AB
Inventors:
Juan José Bosch Vicente, François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan
Abstract: Methods, systems and computer program products are provided for identifying an audio stem. Audio stems (t1, . . . , tN) are stored on a stem database and songs (S1, . . . , SP) made with at least a subset of the plurality of the audio stems (t1, . . . , tN) are stored on a song database. At least partially composed song (S*) having a predetermined number of pre-selected stems (k) are received. In turn, a probability vector (or relevance value or ranking) is produced for each stem (t1, . . . , tN) to be complementary to the at least partially composed song (S*).
Type:
Grant
Filed:
September 19, 2019
Date of Patent:
February 1, 2022
Assignee:
Spotify AB
Inventors:
François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan, Juan José Bosch Vicente
Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.
Type:
Application
Filed:
December 28, 2022
Publication date:
July 13, 2023
Applicant:
Spotify AB
Inventors:
Juan José Bosch Vicente, François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan
Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.
Type:
Application
Filed:
March 16, 2021
Publication date:
October 7, 2021
Applicant:
Spotify AB
Inventors:
Juan José Bosch Vicente, François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan
Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.
Type:
Grant
Filed:
September 19, 2019
Date of Patent:
May 4, 2021
Assignee:
Spotify AB
Inventors:
Juan José Bosch Vicente, François Pachet, Pierre Roy, Mathieu Ramona, Tristan Jehan