Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: A machine may form all or part of a network-based system configured to provide media service to one or more user devices. The machine may be configured to define a station library within a larger collection of media files. In particular, the machine may access metadata that describes a seed that forms the basis on which the station library is to be defined. The machine may determine a genre composition for the station library based on the metadata. The machine may generate a list of media files from the metadata based on a relevance of each media file to the station library. The machine may determine the relevance of each media file based on a similarity of the media file to the genre composition of the station library as well as a comparison of metadata describing the media file to the accessed metadata that describes the seed.

Abstract: 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.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: A machine may form all or part of a network-based system configured to provide media service to one or more user devices. The machine may be configured to define a station library within a larger collection of media files. In particular, the machine may access metadata that describes the media files included in the collection and access a seed that forms the basis on which the station library is to be defined. The machine may generate a list of media files from the metadata and based on the seed and enable a human editor to modify the machine-generated station set according to a human-contributed input to the station list. The machine may then modify the station set based on the submitted input and configure a media service to provide one or more user devices with a datastream that includes media files selected from the modified station list.

Abstract: In an embodiment, a method and apparatus for generating a presentation is provided. The method considers characteristics of audio works and visual works when constructing the presentation. In some embodiments, the presentation may be automatically constructed.

Abstract: A user interface generator is configured to access a media file that stores acoustic data representative of sounds. The user interface generator determines a mood category of the media file, based on a mood vector calculated from the acoustic data. The mood category characterizes the media file as being evocative of a mood described by the mood category. The user interface generator generates a user interface that depicts a grid or map (e.g., a “mood grid” or a “mood map”) of multiple zones. One of the zones may occupy a position in the grid or map that corresponds to the mood category. The user interface may then be presented by the user interface generator (e.g., to a user). In the presented user interface, the zone that corresponds to the mood category may be operable (e.g., by the user) to perform one or more actions pertinent to the mood category.

Abstract: In an embodiment, a method is provided for determining a mood profile of media data. In this method, mood is determined across multiple elements of mood for the media data to create a mood profile associated with the media data. In some embodiments, the mood profile is then used to determine congruencies between one or more pieces of media data.

Abstract: A user interface generator is configured to access a media file that stores acoustic data representative of sounds. The user interface generator determines a mood category of the media file, based on a mood vector calculated from the acoustic data. The mood category characterizes the media file as being evocative of a mood described by the mood category. The user interface generator generates a user interface that depicts a grid or map (e.g., a “mood grid” or a “mood map”) of multiple zones. One of the zones may occupy a position in the grid or map that corresponds to the mood category. The user interface may then be presented by the user interface generator (e.g., to a user). In the presented user interface, the zone that corresponds to the mood category may be operable (e.g., by the user) to perform one or more actions pertinent to the mood category.

Abstract: In an embodiment, a method is provided for determining a mood profile of media data. In this method, mood is determined across multiple elements of mood for the media data to create a mood profile associated with the media data. In some embodiments, the mood profile is then used to determine congruencies between one or more pieces of media data.

Abstract: 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.

Abstract: A user interface generator is configured to access a media file that stores acoustic data representative of sounds. The user interface generator determines a mood category of the media file, based on a mood vector calculated from the acoustic data. The mood category characterizes the media file as being evocative of a mood described by the mood category. The user interface generator generates a user interface that depicts a grid or map (e.g., a “mood grid” or a “mood map”) of multiple zones. One of the zones may occupy a position in the grid or map that corresponds to the mood category. The user interface may then be presented by the user interface generator (e.g., to a user). In the presented user interface, the zone that corresponds to the mood category may be operable (e.g., by the user) to perform one or more actions pertinent to the mood category.

Abstract: The prominent tempo of an audio data is determined by detecting a plurality of beat rates of the audio data. One or more audio data characteristics are used to filter through the beat rates to determine the prominent tempo. Systems, methods, and apparatuses to determine the prominent tempo are discussed herein.

Abstract: A method and system for media navigation. A descriptor hierarchy may be accessed. The descriptor hierarchy may include at least one category list. One or more media descriptors may be accessed for a plurality of media items. The plurality of media items may be accessible from a plurality of sources. The one or more media descriptors may be mapped to the at least one category list. The navigation may be processed through a user interface to enable selection of the plurality of media items from the plurality of sources.

Abstract: In an embodiment, a method is provided for determining a mood profile of media data. In this method, mood is determined across multiple elements of mood for the media data to create a mood profile associated with the media data. In some embodiments, the mood profile is then used to determine congruencies between one or more pieces of media data.

Abstract: The prominent tempo of an audio data is determined by detecting a plurality of beat rates of the audio data. One or more audio data characteristics are used to filter through the beat rates to determine the prominent tempo. Systems, methods, and apparatuses to determine the prominent tempo are discussed herein.

Abstract: A method and system for media navigation. A descriptor hierarchy may be accessed. The descriptor hierarchy may include at least one category list. One or more media descriptors may be accessed for a plurality of media items. The plurality of media items may be accessible from a plurality of sources. The one or more media descriptors may be mapped to the at least one category list. The navigation may be processed through a user interface to enable selection of the plurality of media items from the plurality of sources.

Abstract: Media metadata is accessible for a plurality of media items (See FIG. 12). The media metadata includes a number of strings to identify information regarding the media items (See FIG. 12). Phonetic metadata is associated the number of strings of the media metadata (See FIG. 12). Each portion of the phonetic metadata is stored in an original language of the string (See FIG. 12).