Abstract: In one aspect, an example method includes (i) retrieving, from a text index, closed captioning repetition data for a segment of a sequence of media content; (ii) generating features using the closed captioning repetition data; (iii) providing the features as input to a classification model, wherein the classification model is configured to output classification data indicative of a likelihood of the features being characteristic of a program segment; (iv) obtaining the classification data output by the classification model; (v) determining a prediction of whether the segment is a program segment using the classification data; and (vi) storing the prediction for the segment in a database.

Abstract: In one aspect, an example method includes (i) presenting first media content from a first source; (ii) encountering a trigger to switch from presenting the first media content from the first source to presenting second media content from a second source; (iii) determining a first loudness level of the first media content; (iv) determining a second loudness level of the second media content; (v) based on a difference between the first loudness level and the second loudness level, adjusting a loudness level of the second media content so as to generate modified media content having a third loudness level that is different from the second loudness level; and (vi) responsive to encountering the trigger, presenting the modified media content having the third loudness level.

Abstract: In one aspect, an example method includes (i) retrieving, from a text index, closed captioning repetition data for a segment of a sequence of media content; (ii) generating features using the closed captioning repetition data; (iii) providing the features as input to a classification model, wherein the classification model is configured to output classification data indicative of a likelihood of the features being characteristic of a program segment; (iv) obtaining the classification data output by the classification model; (v) determining a prediction of whether the segment is a program segment using the classification data; and (vi) storing the prediction for the segment in a database.

Abstract: Example systems and methods for automated generation of banner images are disclosed. A program identifier associated with a particular media program may be received by a system, and used for accessing a set of iconic digital images and corresponding metadata associated with the particular media program. The system may select a particular iconic digital image for placing a banner of text associated with the particular media program, by applying an analytical model of banner-placement criteria to the iconic digital images. The system may apply another analytical model for banner generation to the particular iconic image to determine (i) dimensions and placement of a bounding box for containing the text, (ii) segmentation of the text for display within the bounding box, and (iii) selection of font, text size, and font color for display of the text. The system may store the particular iconic digital image and banner metadata specifying the banner.

Abstract: In one aspect, a method includes receiving podcast content, generating a transcript of at least a portion of the podcast content, and parsing the podcast content to (i) identify audio segments within the podcast content, (ii) determine classifications for the audio segments, (iii) identify audio segment offsets, and (iv) identify sentence offsets. The method also includes based on the audio segments, the classifications, the audio segment offsets, and the sentence offsets, dividing the generated transcript into text sentences and, from among the text sentences of the divided transcript, selecting a group of text sentences for use in generating an audio summary of the podcast content. The method also includes based on timestamps at which the group of text sentences begin in the podcast content, combining portions of audio in the podcast content that correspond to the group of text sentences to generate an audio file representing the audio summary.

Abstract: In one aspect, an example method includes (i) presenting first media content from a first source; (ii) encountering a trigger to switch from presenting the first media content from the first source to presenting second media content from a second source; (iii) determining a first loudness level of the first media content; (iv) determining a second loudness level of the second media content; (v) based on a difference between the first loudness level and the second loudness level, adjusting a loudness level of the second media content so as to generate modified media content having a third loudness level that is different from the second loudness level; and (vi) responsive to encountering the trigger, presenting the modified media content having the third loudness level.

Abstract: In one aspect, a method includes detecting a fingerprint match between query fingerprint data representing at least one audio segment within podcast content and reference fingerprint data representing known repetitive content within other podcast content, detecting a feature match between a set of audio features across multiple time-windows of the podcast content, and detecting a text match between at least one query text sentences from a transcript of the podcast content and reference text sentences, the reference text sentences comprising text sentences from the known repetitive content within the other podcast content. The method also includes responsive to the detections, generating sets of labels identifying potential repetitive content within the podcast content. The method also includes selecting, from the sets of labels, a consolidated set of labels identifying segments of repetitive content within the podcast content, and responsive to selecting the consolidated set of labels, performing an action.

Abstract: In one aspect, a method includes receiving podcast content, generating a transcript of at least a portion of the podcast content, and parsing the podcast content to (i) identify audio segments within the podcast content, (ii) determine classifications for the audio segments, (iii) identify audio segment offsets, and (iv) identify sentence offsets. The method also includes based on the audio segments, the classifications, the audio segment offsets, and the sentence offsets, dividing the generated transcript into text sentences and, from among the text sentences of the divided transcript, selecting a group of text sentences for use in generating an audio summary of the podcast content. The method also includes based on timestamps at which the group of text sentences begin in the podcast content, combining portions of audio in the podcast content that correspond to the group of text sentences to generate an audio file representing the audio summary.

Abstract: In one aspect, a method includes receiving podcast content, generating a transcript of at least a portion of the podcast content, and parsing the podcast content to (i) identify audio segments within the podcast content, (ii) determine classifications for the audio segments, (iii) identify audio segment offsets, and (iv) identify sentence offsets. The method also includes based on the audio segments, the classifications, the audio segment offsets, and the sentence offsets, dividing the generated transcript into text sentences and, from among the text sentences of the divided transcript, selecting a group of text sentences for use in generating an audio summary of the podcast content. The method also includes based on timestamps at which the group of text sentences begin in the podcast content, combining portions of audio in the podcast content that correspond to the group of text sentences to generate an audio file representing the audio summary.

Abstract: Example systems and methods of 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 a sequence of video image frames to determine for each respective video image frame a respective score corresponding to a highest statistical confidence associated with one or more subsets of cropping boundaries predicted for the respective video image frame. Information indicative of the respective video image frame having the highest score may be stored or recorded.

Abstract: Methods, apparatus and systems are disclosed to generate light control information. An example apparatus comprises a beat tracking network to determine a length of time between a first media onset and a second media onset, a light drive waveform generator to compare the length of time to a time threshold, the time threshold corresponding to a desired time between consecutive light pulses, when the time threshold is not satisfied, increase the length of time, the increased length of time corresponding to light pulse spacing, and generate light control information based on the light pulse spacing, the light control information to cause the consecutive light pulses, and an effect engine to generate intensity information based on a first amplitude of the first media onset and a second amplitude of the second media onset, an amplitude of the consecutive light pulses corresponding to the intensity information.

Abstract: Example systems and methods for automated generation of banner images are disclosed. A program identifier associated with a particular media program may be received by a system, and used for accessing a set of iconic digital images and corresponding metadata associated with the particular media program. The system may select a particular iconic digital image for placing a banner of text associated with the particular media program, by applying an analytical model of banner-placement criteria to the iconic digital images. The system may apply another analytical model for banner generation to the particular iconic image to determine (i) dimensions and placement of a bounding box for containing the text, (ii) segmentation of the text for display within the bounding box, and (iii) selection of font, text size, and font color for display of the text. The system may store the particular iconic digital image and banner metadata specifying the banner.

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 includes (i) extracting a sequence of audio features from a portion of a sequence of media content; (ii) extracting a sequence of video features from the portion of the sequence of media content; (iii) providing the sequence of audio features and the sequence of video features as an input to a transition detector neural network that is configured to classify whether or not a given input includes a transition between different content segments; (iv) obtaining from the transition detector neural network classification data corresponding to the input; (v) determining that the classification data is indicative of a transition between different content segments; and (vi) based on determining that the classification data is indicative of a transition between different content segments, outputting transition data indicating that the portion of the sequence of media content includes a transition between different content segments.

Abstract: Example systems and methods of 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 a sequence of video image frames to determine for each respective video image frame a respective score corresponding to a highest statistical confidence associated with one or more subsets of cropping boundaries predicted for the respective video image frame. Information indicative of the respective video image frame having the highest score may be stored or recorded.

Abstract: An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented automated face recognition program may be applied to the digital image to recognize, based on at least one feature vector from a prior-determined set of feature vectors, one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the prior-determined set of feature vectors may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

Abstract: An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented face recognition program together with a set of computational models associated with the particular media content program may be applied to the digital image to recognize one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the set of the computational models may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

Abstract: An example method may include applying an automated face detection program implemented on a computing device to a plurality of training digital images associated with a particular media content program to identify a sub-plurality of the training digital images, each containing a single face of a particular person associated with the particular media content program. An automated feature extraction program may be applied to the sub-plurality to generate a set of feature vectors associated with the particular person, each feature vector of the set corresponding to a different training digital image. An automated face recognition program may be applied to a runtime digital image associated with the particular media content program to recognize the particular person, together with respective geometric coordinates. The runtime digital image may be stored together with information identifying the particular person and the respective geometric coordinates of the particular person in the runtime digital image.

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: Example systems and methods of 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 a sequence of video image frames to determine for each respective video image frame a respective score corresponding to a highest statistical confidence associated with one or more subsets of cropping boundaries predicted for the respective video image frame. Information indicative of the respective video image frame having the highest score may be stored or recorded.