Abstract: Methods and systems generate deep metadata associated with media content. The deep metadata may be used, for example, by media content recommendation systems. In one embodiment, a database includes a plurality of media files that are each associated with respective data models and metadata sets. A new media file is categorized by microgenre and automatically analyzed to generate a new data model. The new data model is compared to the database to determine a particular data model stored therein that satisfies a similarity threshold. In one embodiment, the comparison is limited to data models that are associated with the same microgenre as that of the new media file. A set of metadata associated with the particular data model stored in the database is then assigned to the new media file. In an example embodiment, the new media file is an audio file and the database is a music database.

Abstract: A method of real-time customization of an audio stream includes retrieving a set of parameters related to a current state of a device, determining a pattern, tempo, background loop, pitch, and number of foreground notes for the audio stream based upon the set of parameters, and creating an audio stream based upon the determination.

Abstract: Methods and systems verify and control the quality of metadata associated with a media data file. The metadata may be used, for example, by media content recommendation systems. In one embodiment, a first attribute is selected from metadata associated with a media data file and an attribute dependency corresponding to the first attribute is determined. The metadata may be searched to determine whether it includes a second attribute that satisfies the dependency. If the dependency is not satisfied, the metadata is flagged for reevaluation. The metadata may also be flagged for reevaluation, for example, if the metadata includes inconsistent attributes, if the total number of assigned attributes within the metadata does not exceed a predetermined value, or if an expected attribute dimension is missing or does not include an expected number of defined attributes. In certain embodiments, user selections are controlled to provide consistent attribute combinations.

Abstract: Methods and systems generate deep metadata associated with media content. The deep metadata may be used, for example, by media content recommendation systems. In one embodiment, a database includes a plurality of media files that are each associated with respective data models and metadata sets. A new media file is categorized by microgenre and automatically analyzed to generate a new data model. The new data model is compared to the database to determine a particular data model stored therein that satisfies a similarity threshold. In one embodiment, the comparison is limited to data models that are associated with the same microgenre as that of the new media file. A set of metadata associated with the particular data model stored in the database is then assigned to the new media file. In an example embodiment, the new media file is an audio file and the database is a music database.

Abstract: Methods and systems verify and control the quality of metadata associated with a media data file. The metadata may be used, for example, by media content recommendation systems. In one embodiment, a first attribute is selected from metadata associated with a media data file and an attribute dependency corresponding to the first attribute is determined. The metadata may be searched to determine whether it includes a second attribute that satisfies the dependency. If the dependency is not satisfied, the metadata is flagged for reevaluation. The metadata may also be flagged for reevaluation, for example, if the metadata includes inconsistent attributes, if the total number of assigned attributes within the metadata does not exceed a predetermined value, or if an expected attribute dimension is missing or does not include an expected number of defined attributes. In certain embodiments, user selections are controlled to provide consistent attribute combinations.