Abstract: A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

Abstract: A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

Abstract: A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

Abstract: A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

Abstract: A system and method for making categorized music tracks available to end user applications. The tracks may be categorized based on computer-derived rhythm, texture and pitch (RTP) scores for tracks derived from high-level acoustic attributes, which is based on low level data extracted from the tracks. RTP scores are stored in a universal database common to all of the music publishers so that the same track, once RTP scored, does not need to be re-RTP scored by other music publishers. End user applications access an API server to import collections of tracks published by publishers, to create playlists and initiate music streaming. Each end user application is sponsored by a single music publisher so that only tracks capable of being streamed by the music publisher are available to the sponsored end user application.

Abstract: A method for categorizing streamed music based on a sample set of RTP scores for predetermined tracks. High-level acoustic attributes for tracks are determined by an analyzed extraction of low-level data from the tracks. The high-level acoustic attributes are used to develop computer-derived RTP scores for the tracks based on the sample set, which includes RTPs score for a plurality of possible combinations of a rhythm score (R), a texture score (T), and a pitch score (P) respectively from a R range, a T range, and a P range. At least some of the RTP scores correspond to human-determined RTP scores for predetermined tracks among a plurality of predetermined tracks. Each RTP score corresponds to a category among a plurality of categories. The computer-derived RTP scores are used to determine a category for each track among the plurality of categories. Playlists of the tracks are based on one or more of the categories.

Abstract: A method for categorizing streamed music based on a sample set of RTP scores for predetermined tracks. High-level acoustic attributes for tracks are determined by an analyzed extraction of low-level data from the tracks. The high-level acoustic attributes are used to develop computer-derived RTP scores for the tracks based on the sample set, which includes RTPs score for a plurality of possible combinations of a rhythm score (R), a texture score (T), and a pitch score (P) respectively from a R range, a T range, and a P range. At least some of the RTP scores correspond to human-determined RTP scores for predetermined tracks among a plurality of predetermined tracks. Each RTP score corresponds to a category among a plurality of categories. The computer-derived RTP scores are used to determine a category for each track among the plurality of categories. Playlists of the tracks are based on one or more of the categories.

Abstract: A method for categorizing music based on a sample set of RTP scores (rhythm, texture and pitch) for predetermined pieces of music. Some RTP scores correspond to human-determined RTP scores. Each RTP score corresponds to a category among categories. Unless an unknown piece of music was previously RTP scored based on a unique identification, low-level data is extracted from the unknown piece and analyzed to identify RTP scores based on the sample set. The identified RTP scores are then used to categorize each piece of unknown music and playlists may be created based on the categories. Each RTP score corresponds to an intensity level within the corresponding category, which may also be used in creating playlists. The low-level data may be converted to mel-frequency cepstrum coefficient (MFCC) data that is input into a trained neural network to identify the RTP scores.

Abstract: A method for categorizing music based on a sample set of RTP scores (rhythm, texture and pitch) for predetermined pieces of music. Some RTP scores correspond to human-determined RTP scores. Each RTP score corresponds to a category among categories. Unless an unknown piece of music was previously RTP scored based on a unique identification, low-level data is extracted from the unknown piece and analyzed to identify RTP scores based on the sample set. The identified RTP scores are then used to categorize each piece of unknown music and playlists may be created based on the categories. Each RTP score corresponds to an intensity level within the corresponding category, which may also be used in creating playlists. The low-level data may be converted to mel-frequency cepstrum coefficient (MFCC) data that is input into a trained neural network to identify the RTP scores.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

Abstract: A computer-based system and method for providing users of software/video games with a wrap-around gaming experience. A first game is played by a user in an initial form to gather experience. A second game is then played by the user to gather further experience. If the user gathers enough experience while playing the second game, the user can return to the first game and unlock features and experience that did not appear in its initial form, basically transforming the first game into a third game. A character and its attributes can also be exported from a first game into a second game with or without modifying the second video game.