Abstract: A tunable signal sampling system includes a computing platform having a hardware processor and a memory storing a software code that when executed receives a communications signal, identifies data partitions included in the communications signal, performs, using a first predetermined metric, a first set of comparisons each comparing a different sequential pair of the data partitions with each other, and selects, based on the first set of comparisons, a subset of the data partitions as candidate sample partitions of the communications signal. The software code also determines multiple default sample partitions of the communications signal, performs, using a second predetermined metric, a second set of comparisons each comparing a different one of the default sample partitions with a respective one of the candidate sample partitions, and extracts, using a predetermined weighting factor applied to the results of the second set of comparisons, a sample of the communications signal.

Abstract: An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory.

Abstract: A metadata aggregation system includes a computing platform having a hardware processor and a memory storing a software code including a trained entity matching predictive model trained using training data obtained from a reference database. The hardware processor executes the software code to obtain metadata inputs from multiple sources, conform the metadata inputs to a common format, match, using the trained entity matching predictive model, at least some of the conformed metadata inputs to the same entity, and determine, using the trained entity matching predictive model, a confidence score for each match. The software code further sends a request to one or more human editor(s) for confirmation of each match having a confidence score greater than a first threshold and less than a second threshold, and updates the reference database, in response to receiving a confirmation that at least one match is a confirmed match, to include the confirmed match.

Abstract: A video content matching system includes a computing platform having a hardware processor and a memory storing a software code. When executed, the software code obtains a reference digital profile of a reference video segment, obtains a target digital profile of target video content, and compares the reference and target digital profiles to detect a candidate video segment of the target video content for matching to the reference video segment. The software code also frame aligns reference video frames of the reference video segment with corresponding candidate video frames of the candidate video segment to provide frame aligned video frame pairs, pixel aligns the frame aligned video frame pairs to produce frame and pixel aligned video frame pairs, and identifies, using the frame and pixel aligned video frame pairs, the candidate video segment as a matching video segment or a non-matching video segment for the reference video segment.

Abstract: An automated hardware resource optimization system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor is configured to execute the software code to identify computing hardware for hosting a neural network based application, determine, based on a first performance parameter of the computing hardware, a batch size for performing data processing using the neural network based application, and tune, using a data batch having the determined batch size, a second performance parameter of the computing hardware to enable substantially continuous loading of its hardware processor memory.

Abstract: According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

Abstract: According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

Abstract: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Abstract: Techniques for machine learning optimization are provided. A video comprising a plurality of segments is received, and a first segment of the plurality of segments is processed with a machine learning (ML) model to generate a plurality of tags, where each of the plurality of tags indicates presence of an element in the first segment. A respective accuracy value is determined for each respective tag of the plurality of tags, where the respective accuracy value is based at least in part on a maturity score for the ML model. The first segment is classified as accurate, based on determining that an aggregate accuracy of tags corresponding to the first segment exceeds a predefined threshold. Upon classifying the first segment as accurate, the first segment is bypassed during a review process.

Abstract: There is provided a system including a computing platform having a hardware processor and a memory, and a metadata extraction and management unit stored in the memory. The hardware processor is configured to execute the metadata extraction and management unit to extract a plurality of metadata types from a media asset sequentially and in accordance with a prioritized order of extraction based on metadata type, aggregate the plurality of metadata types to produce an aggregated metadata describing the media asset, use the aggregated metadata to include at least one database entry in a graphical database, wherein the at least one database entry describes the media asset, display a user interface for a user to view tags of metadata associated with the media asset, and correcting presence of one of the tags of metadata associated with the media asset, in response to an input from the user via the user interface.

Abstract: According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

Abstract: According to one implementation, an automated audio mapping system includes a computing platform having a hardware processor and a system memory storing an audio mapping software code including an artificial neural network (ANN) trained to identify multiple different audio content types. The hardware processor is configured to execute the audio mapping software code to receive content including multiple audio tracks, and to identify, without using the ANN, a first music track and a second music track of the multiple audio tracks. The hardware processor is further configured to execute the audio mapping software code to identify, using the ANN, the audio content type of each of the multiple audio tracks except the first music track and the second music track, and to output a mapped content file including the multiple audio tracks each assigned to a respective one predetermined audio channel based on its identified audio content type.

Abstract: According to one implementation, a cloud-based system for performing cloud-based image rendering for video stream enrichment includes a video forwarding unit and a video enrichment unit. The video forwarding unit is configured to detect one or more non-interactive video player(s) linked to the video forwarding unit over a communication network, forward a video stream to the non-interactive video player(s), and forward the video stream to the video enrichment unit. The video enrichment unit is configured to receive the video stream, detect one or more interactive video player(s) linked to the video enrichment unit over the communication network, identify a video enhancement corresponding to one or more customizable video segment(s) in the video stream, insert a rendered video enhancement into the one or more customizable video segment(s) to produce an enriched video stream, and distribute the enriched video stream to one or more of the interactive video player(s).

Abstract: According to one implementation, a cloud-based system for performing cloud-based image rendering for video stream enrichment includes a video forwarding unit and a video enrichment unit. The video forwarding unit is configured to detect one or more non-interactive video player(s) linked to the video forwarding unit over a communication network, forward a video stream to the non-interactive video player(s), and forward the video stream to the video enrichment unit. The video enrichment unit is configured to receive the video stream, detect one or more interactive video player(s) linked to the video enrichment unit over the communication network, identify a video enhancement corresponding to one or more customizable video segment(s) in the video stream, insert a rendered video enhancement into the one or more customizable video segment(s) to produce an enriched video stream, and distribute the enriched video stream to one or more of the interactive video player(s).

Abstract: According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

Abstract: A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.

Abstract: A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content to feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.

Abstract: A content promotion system includes a computing platform having a hardware processor and a system memory storing a conversational agent software code. The hardware processor executes the conversational agent software code to receive user identification data, obtain user profile data including a content consumption history of a user associated with the user identification data, and identify a first predetermined phrase for use in interacting with the user based on the user profile data. In addition, the conversational agent software code initiates a dialog with the user based on the first predetermined phrase, detects a response or non-response to the dialog, updates the user profile data based on the response or non-response, resulting in updated user profile data, identifies a second predetermined phrase for use in interacting with the user based on the updated user profile data, and continues the dialog with the user based on the second predetermined phrase.

Abstract: A set of software applications configured to perform interframe and/or intraframe encoding operations based on data communicated between a graphics application and a graphics processor. The graphics application transmits a 3D model to the graphics processor to be rendered into a 2D frame of video data. The graphics application also transmits graphics commands to the graphics processor indicating specific transformations to be applied to the 3D model as well as textures that should be mapped onto portions of the 3D model. Based on these transformations, an interframe module can determine blocks of pixels that repeat across sequential frames. Based on the mapped textures, an intraframe module can determine blocks of pixels that repeat within an individual frame. A codec encodes the frames of video data into compressed form based on blocks of pixels that repeat across frames or within frames.

Abstract: A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.