Abstract: A system processed a representation of a media content item using a trained machine learning model to identify whether the media content item is a modified media content item. The trained machine learning model outputs an indication of one or more modifications of the media content item. Responsive to determining that the one or more modifications satisfy a criterion, one or more operations are performed on the media content item in view of the one or more modifications to generate an updated media content item. The updated media content item is compared to known media content items to identify known material contained therein.

Abstract: Embodiments cover identifying an unidentified media content item as a cover of a known media content item using lyrical contents. In an example, a processing device receives an unidentified media content item and determines lyrical content associated with the unidentified media content item. The processing device then determines a lyrical similarity between the lyrical content associated with the unidentified media content item and additional lyrical content associated with a known media content item of a plurality of known media content items. The processing device then identifies the unidentified media content item as a cover of the known media content item based at least in part on the lyrical similarity, resulting in an identified cover-media content item.

Abstract: A method and system classify media content items and then identify a subset of the classified media content items. In embodiments, audio features of a plurality of media content items are processed by one or more machine learning model to classify each of the media content items as containing music or not containing music. Those media content items not containing music are filtered out, digital fingerprints of those media content items that contain music are generated, and the digital fingerprints are used to identify those media content items that contain music.

Abstract: An unidentified media content item may be received by a processing device. A set of features of the unidentified media content item may be determined. Metadata associated with the unidentified media content item may be determined. A first similarity between the metadata associated with the unidentified media content item and additional metadata associated with a known media content item from a media content repository may be determined. A second similarity between the set of features of the unidentified media content item and an additional set of features associated with the known media content item may be determined. The unidentified media content item may be identified as a cover of the known media content item based on the first similarity and the second similarity by the processing device.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A system receives a media content item and analyzes the media content item to identify copyrighted material contained therein. Analyzing the media content item includes determining an initial interval for selecting segments of the media content item to be analyzed, generating a first digital fingerprint of a first segment of the media content item, comparing the first digital fingerprint to at least a first subset of a plurality of digital fingerprints of a first plurality of known works to determine whether the first digital fingerprint matches one of the plurality of digital fingerprints of the first plurality of known works, and determining an updated interval for selecting the segments of the media content item to be analyzed based on whether or not a match was found between the first digital fingerprint and one of the plurality of digital fingerprints of the first plurality of known works.

Abstract: Embodiments cover identifying an unidentified media content item as a cover of a known media content item using lyrical contents. In an example, a processing device receives an unidentified media content item and determines lyrical content associated with the unidentified media content item. The processing device then determines a lyrical similarity between the lyrical content associated with the unidentified media content item and additional lyrical content associated with a known media content item of a plurality of known media content items. The processing device then identifies the unidentified media content item as a cover of the known media content item based at least in part on the lyrical similarity, resulting in an identified cover-media content item.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A media stream cue point creator receives a media stream comprising at least one of an audio portion or a video portion, wherein the media stream lacks at least one indicator of a content segment within the media stream. The media stream cue point creator identifies a first segment of the plurality of segments of the media stream, wherein the segment corresponds to a segment in a segment reference data store, and creates a cue point associated with the first segment, wherein the cue point identifies at least one of a beginning or an end of the first content segment in the media stream.

Abstract: An unidentified media content item may be received by a processing device. A set of features of the unidentified media content item may be determined. Metadata associated with the unidentified media content item may be determined. A first similarity between the metadata associated with the unidentified media content item and additional metadata associated with a known media content item from a media content repository may be determined. A second similarity between the set of features of the unidentified media content item and an additional set of features associated with the known media content item may be determined. The unidentified media content item may be identified as a cover of the known media content item based on the first similarity and the second similarity by the processing device.

Abstract: A media item may be received by a computing device. One or more works may be identified in the media item. One or more ratios may be determined based on the length of the media item and/or lengths of one or more identified works in the media item. An output may be determined based on the one or more ratios.

Abstract: A media stream cue point creator receives a media stream comprising at least one of an audio portion or a video portion, wherein the media stream lacks at least one indicator of a content segment within the media stream. The media stream cue point creator identifies a first segment of the plurality of segments of the media stream, wherein the segment corresponds to a segment in a segment reference data store, and creates a cue point associated with the first segment, wherein the cue point identifies at least one of a beginning or an end of the first content segment in the media stream.

Abstract: A media content item may be received by a first processing device. A set of features of the media content item may be determined. The set of features determined from the media content item may be analyzed using a media classification profile comprising a first model for a first class of media content items and a second model for a second class of media content items. Whether the media content item belongs to the first class of media content items or the second class of the media content items may be determined based on a result of the analysis. Responsive to determining that the media content item belongs to the first class, either a portion of the media content item or a digital fingerprint of the media content item may be sent to a second processing device for further processing.

Abstract: A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.

Abstract: A media stream cue point creator receives a media stream comprising at least one of an audio portion or a video portion, wherein the media stream lacks at least one indicator of a content segment within the media stream. The media stream cue point creator identifies a first segment of the plurality of segments of the media stream, wherein the segment corresponds to a segment in a segment reference data store, and creates a cue point associated with the first segment, wherein the cue point identifies at least one of a beginning or an end of the first content segment in the media stream.

Abstract: A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.

Abstract: A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.

Abstract: A processor receives a first list comprising a plurality of events from a portion of digital data of an unknown work and one or more metrics between each pair of adjacent events from the plurality of events. The processor compares the first list to a second list comprising events and metrics between events for a known work to determine a first quantity of hits and a second quantity of misses. The processor determines whether the first list matches the second list based on the first quantity of hits and the second quantity of misses. The processor determines that the unknown work is a copy of the known work responsive to determining that the first list matches the second list.

Abstract: A computing system selects a portion of data of an unknown work and detects each event in the portion of data of the unknown work. An event is a perceptual occurrence in a work successively positioned in time. The system determines an event metric between each successive event in the portion of data in the unknown work and generates a list of event metrics between the events for the unknown work. The system compares the list of event metrics for the unknown work to a list of event metrics for a known work and determines the unknown work is a copy of the known work responsive to a match between the list of event metrics of the unknown work and the list of event metrics for the known work.