Abstract: A method implemented by a computing system comprises generating, by the computing system, a fingerprint comprising a plurality of bin samples associated with audio content. Each bin sample is specified within a frame of the fingerprint and is associated with one of a plurality of non-overlapping frequency ranges and a value indicative of a magnitude of energy associated with a corresponding frequency range. The computing system removes, from the fingerprint, a plurality of bin samples associated with a frequency sweep in the audio content.

Abstract: Techniques of providing an interactive programming guide with a personalized lineup are disclosed. In some embodiments, a profile is accessed, and a personalized lineup is determined based on the profile. The personalized lineup may include a corresponding media content identification assigned to each one of a plurality of sequential time slots, where each media content identification identifies media content for the corresponding time slot. A first interactive programming guide may be caused to be displayed on a first media content device associated with the profile, where the first interactive programming guide includes the personalized lineup.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to adjust device control information. The example apparatus comprises a light drive waveform generator to obtain metadata corresponding to media and generate device control information based on the metadata, the device control information to inform a lighting device to enable consecutive light pulses; an effect engine to apply an attack parameter and a decay parameter to consecutive light pulses corresponding to the device control information, the attack parameter and the decay parameter based on the metadata to affect a shape of the consecutive light pulses; and a color timeline generator to generate color information based on the metadata, the color information to inform the lighting device to change a color state.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve media identification. An example apparatus includes a hash handler to generate a first set of reference matches by performing hash functions on a subset of media data associated with media to generate hashed media data based on a first bucket size, a candidate determiner to identify a second set of reference matches that include ones of the first set, the second set including ones having first quantities of hits that did not satisfy a threshold, determine second quantities of hits for ones of the second set by matching ones to the hash tables based on a second bucket size, and identify one or more candidate matches based on at least one of (1) ones of the first set or (2) ones of the second set, and a report generator to generate a report including a media identification.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for dynamic volume adjustment via audio classification.

Abstract: A clustering machine can cluster descriptive vectors in a balanced manner. The clustering machine calculates distances between pairs of descriptive vectors and generates clusters of vectors arranged in a hierarchy. The clustering machine determines centroid vectors of the clusters, such that each cluster is represented by its corresponding centroid vector. The clustering machine calculates a sum of inter-cluster vector distances between pairs of centroid vectors, as well as a sum of intra-cluster vector distances between pairs of vectors in the clusters. The clustering machine calculates multiple scores of the hierarchy by varying a scalar and calculating a separate score for each scalar. The calculation of each score is based on the two sums previously calculated for the hierarchy. The clustering machine may select or otherwise identify a balanced subset of the hierarchy by finding an extremum in the calculated scores.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve detection of audio signatures.

Abstract: Example methods and systems for indexing fingerprints are described. Fingerprints may be made up of sub-fingerprints, each of which corresponds to a frame of the media, which is a smaller unit of time than the fingerprint. In some example embodiments, multiple passes are performed. For example, a first pass may be performed that compares the sub-fingerprints of the query fingerprint with every thirty-second sub-fingerprint of the reference material to identify likely matches. In this example, a second pass is performed that compares the sub-fingerprints of the query fingerprint with every fourth sub-fingerprint of the likely matches to provide a greater degree of confidence. A third pass may be performed that uses every sub-fingerprint of the most likely matches, to help distinguish between similar references or to identify with greater precision the timing of the match. Each of these passes is amenable to parallelization.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to fingerprint an audio signal via exponential normalization. An example apparatus includes an audio segmenter to divide an audio signal into a plurality of audio segments including a first audio segment and a second audio segment, the first audio segment including a first time-frequency bin, the second audio segment including a second time-frequency bin, a mean calculator to determine a first exponential mean value associated with the first time frequency bin based on a first magnitude of the audio signal associated with the first time frequency bin and a second exponential mean value associated with the second time frequency bin based on a second magnitude of the audio signal associated with the second time frequency bin and the first exponential mean value.

Abstract: A computing system automatically detects, within a digital video frame, a video frame region that depicts a textual expression of a scoreboard. The computing system (a) engages in an edge-detection process to detect edges of at least scoreboard image elements depicted by the digital video frame, with at least some of these edges being of the textual expression and defining alphanumeric shapes; (b) applies pattern-recognition to identify the alphanumeric shapes; (c) establishes a plurality of minimum bounding rectangles each bounding a respective one of the identified alphanumeric shapes; (d) establishes, based on at least two of the minimum bounding rectangles, a composite shape that encompasses the identified alphanumeric shapes that were bounded by the at least two minimum bounding rectangles; and (e) based on the composite shape occupying a particular region, deems the particular region to be the video frame region that depicts the textual expression.

Abstract: A method and system for computer-based generation of podcast metadata, to facilitate operations such as searching for and recommending podcasts based on the generated metadata. In an example method, a computing system obtains a text representation of a podcast episode and obtains person data defining a list of person names such as celebrity names. The computing system then correlates the person data with the text representation, to find a match between a listed person name a text string in the text representation. Further, the computing system predicts a named-entity span in the text representation and determines that the predicted named-entity span matches a location of the text string in the text representation of the podcast episode, and based on this determination, the computing system generates and outputs metadata that associates the person name with the podcast episode.

Abstract: In one aspect, an example method includes (i) receiving a first group of video content items; (ii) identifying from among the first group of video content items, a second group of video content items having a threshold extent of similarity with each other; (iii) determining a quality score for each video content item of the second group; (iv) identifying from among the second group of video content items, a third group of video content items each having a quality score that exceeds a quality score threshold; and (v) based on the identifying of the third group, transmitting at least a portion of at least one video content item of the identified third group to a digital video-effect (DVE) system, wherein the system is configured for using the at least the portion of the at least one video content item of the identified third group to generate a video content item.

Abstract: Techniques of content unification are disclosed. In some example embodiments, a computer-implemented method comprises: determining clusters based a comparison of a plurality of audio content using a first matching criteria, each cluster of the plurality of clusters comprising at least two audio content from the plurality of audio content; for each cluster of the plurality of clusters, determining a representative audio content for the cluster from the at least two audio content of the cluster; loading the corresponding representative audio content of each cluster into an index; matching the query audio content to one of the representative audio contents using a first matching criteria; determining the corresponding cluster of the matched representative audio content; and identifying a match between the query audio content and at least one of the audio content of the cluster of the matched representative audio content based on a comparison using a second matching criteria.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to select reference sub-fingerprints for comparison to query sub-fingerprints based on a determination that a query sub-fingerprint is a match with a reference sub-fingerprint, generate a count vector that stores total counts of matches between the query sub-fingerprints and different subsets of the reference sub-fingerprints, each of the different subsets being aligned to the query sub-fingerprints at a different offset from a reference point, each of the different offsets being mapped by the count vector to a different total count, calculate a maximum count among the total counts, a median of the total counts, and a difference between the maximum count and the median of the total counts, and classify the reference sub-fingerprints as a match with the query sub-fingerprints based on the difference between the maximum count in the count vector and the median.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to determine audio quality. Example apparatus disclosed herein include an equalization (EQ) model query generator to generate a query to a neural network, the query including a representation of a sample of an audio signal. Example apparatus disclosed herein also include an EQ analyzer to access a plurality of equalization settings determined by the neural network based on the query; and compare the equalization settings to an equalization threshold to determine if the audio signal is to be removed from subsequent processing.

Abstract: Methods, apparatus and articles of manufacture to identify sources of network streaming services are disclosed. An example method includes receiving a first audio signal that represents a decompressed second audio signal, identifying, from the first audio signal, a parameter of an audio compression configuration used to form the decompressed second audio signal, and identifying a source of the decompressed second audio signal based on the identified audio compression configuration.

Abstract: Methods and systems for automated video segmentation are disclosed. A sequence of video frames having video segments of contextually-related sub-sequences may be received. Each frame may be labeled according to segment and segment class. A video graph may be constructed in which each node corresponds to a different frame, and each edge connects a different pair of nodes, and is associated with a time between video frames and a similarity metric of the connected frames. An artificial neural network (ANN) may be trained to predict both labels for the nodes and clusters of the nodes corresponding to predicted membership among the segments, using the video graph as input to the ANN, and ground-truth clusters of ground-truth labeled nodes. The ANN may be further trained to predict segment classes of the predicted clusters, using the segment classes as ground truths. The trained ANN may be configured for application runtime video sequences.

Abstract: Example systems and methods may selection of video frames using a machine learning (ML) predictor program are disclosed. The ML predictor program may generate predicted cropping boundaries for any given input image. Training raw images associated with respective sets of training master images indicative of cropping characteristics for the training raw image may be input to the ML predictor, and the ML predictor program trained to predict cropping boundaries for raw image based on expected cropping boundaries associated training master images. At runtime, the trained ML predictor program may be applied to runtime raw images in order to generate respective sets of runtime cropping boundaries corresponding to different cropped versions of the runtime raw image. The runtime raw images may be stored with information indicative of the respective sets of runtime boundaries.

Abstract: In one aspect, an example method to be performed by a computing device includes (a) receiving a request to use a camera of the computing device; (b) in response to receiving the request, (i) using a microphone of the computing device to capture audio content and (ii) using the camera of the computing device to capture an image; (c) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; and (d) outputting an indication of the identified reference audio content while displaying the captured image.

Abstract: In one aspect, an example method to be performed by a vehicle-based media system includes (a) receiving audio content; (b) causing one or more speakers to output the received audio content; (c) using a microphone of the vehicle-based media system to capture the output audio content; (d) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; (e) identifying visual content based at least on the identified reference audio content; and (f) outputting, via a user interface of the vehicle-based media system, the identified visual content.