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: 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: Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.

Abstract: Methods, apparatus, and systems are disclosed to segment audio and determine audio segment similarities. An example apparatus includes at least one memory storing instructions and processor circuitry to execute instructions to at least select an anchor index beat of digital audio, identify a first segment of the digital audio based on the anchor index beat to analyze, the first segment having at least two beats and a respective center beat, concatenate time-frequency data of the at least two beats and the respective center beat to form a matrix of the first segment, generate a first deep feature based on the first segment, the first deep feature indicative of a descriptor of the digital audio, and train internal coefficients to classify the first deep feature as similar to a second deep feature based on the descriptor of the first deep feature and a descriptor of a second deep feature.

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

Abstract: In one aspect, an example method to be performed by a computing device includes (a) determining that a ride-sharing session is active; (b) in response to determining the ride-sharing session is active, using a microphone of the computing device to capture audio content; (c) identifying reference audio content that has at least a threshold extent of similarity with the captured audio content; (d) determining that the ride-sharing session is inactive; and (e) outputting an indication of the identified reference audio content.

Abstract: Examples described herein may perform various operations based on mood congruency. An example method involves accessing, by a processor, from a database, a score that represents a degree of congruency between a first mood vector that describes first media data and a second mood vector that describes second media data, wherein the score is generated based on (i) a first value that the first mood vector associates with a first mood, (ii) a second value that the second mood vector associates with a second mood, and (iii) a degree of congruency between the first and second moods, based on the score, comparing, by the processor, a first characteristic of the first media data, other than the first mood, with a second characteristic of the second media data, other than the second mood, and based at least in part on an output of the comparing, providing an indicator to a module.

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.

Abstract: Methods and apparatus for audio identification during a performance are disclosed herein. An example apparatus includes at least one memory and at least one processor to transform a segment of audio into a log-frequency spectrogram based on a constant Q transform using a logarithmic frequency resolution, transform the log-frequency spectrogram into a binary image, each pixel of the binary image corresponding to a time frame and frequency channel pair, each frequency channel representing a corresponding quarter tone frequency channel in a range from C3-C8, generate a matrix product of the binary image and a plurality of reference fingerprints, normalize the matrix product to form a similarity matrix, select an alignment of a line in the similarity matrix that intersects one or more bins in the similarity matrix with the largest calculated Hamming similarities, and select a reference fingerprint based on the alignment.

Abstract: Methods and apparatus for harmonic source enhancement are disclosed herein. An example apparatus includes an interface to receive a media signal. The example apparatus also includes a harmonic source enhancer to determine a magnitude spectrogram of audio corresponding to the media signal; generate a time-frequency mask based on the magnitude spectrogram; and apply the time-frequency mask to the magnitude spectrogram to enhance a harmonic source of the media signal.

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: 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: A computing system automatically detects, in a sequence of video frames, a video frame region that depicts a scoreboard. The video frames of the sequence depict image elements including (i) scoreboard image elements that are unchanging across the video frames of the sequence and (ii) other image elements that change across the video frames of the sequence. Given this, the computing system (a) receives the sequence, (b) engages in an edge-detection process to detect, in the video frames of the sequence, a set of edges of the depicted image elements, (c) identifies a subset of the detected set of edges based on each edge of the subset being unchanging across the video frames of the sequence, and (d) detects, based on the edges of the identified subset, the video frame region that depicts the scoreboard.

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: In one aspect, an example method includes a processor (1) applying a feature map network to an image to create a feature map comprising a grid of vectors characterizing at least one feature in the image and (2) applying a probability map network to the feature map to create a probability map assigning a probability to the at least one feature in the image, where the assigned probability corresponds to a likelihood that the at least one feature is an overlay. The method further includes the processor determining that the probability exceeds a threshold, and responsive to the processor determining that the probability exceeds the threshold, performing a processing action associated with the at least one feature.

Abstract: Example systems and methods to transform events and/or mood associated with playing media into lighting effects are disclosed herein. An example apparatus includes a content identifier to identify a first event occurring during presentation of media content at a first time. The example apparatus includes a content driven analyzer to determine a first lighting effect to be produced by a light-producing device based on the first event and instruct the light-producing device to produce the first lighting effect based on the first event during presentation of the media content. The content identifier is to identify a second media event occurring during presentation of the media content at a second time after the first time. The content driven analyzer is to instruct the light-producing device to one of maintain the first lighting effect based on the second event or produce a second lighting effect based on the second event during presentation of the media content.

Abstract: Methods, apparatus, and systems are disclosed for synchronizing streaming media content. An example apparatus includes a storage device, and a processor to execute instructions to identify a first source streaming broadcast media to a first computing device based on an audio fingerprint of audio associated with the broadcast media, identify sources broadcasting the broadcast media streaming to the first computing device, the sources available to a second computing device including the processor, select a second source of the identified sources for streaming the broadcast media to the second computing device, the second source different than the first source, detect termination of the streaming of the broadcast media on the first computing device, the termination corresponding to a termination time of the broadcast media, and automatically start, by using the selected second source, streaming of the broadcast media to the second computing device at the termination time.

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: An embodiment may involve a server device transmitting, over a wide area network, a first playlist with a first duration to a client device. Possibly while the client device is playing out a current audio file of a first plurality of audio files in the playlist, the server device may receive an instruction from the client device and generate a second playlist. The second playlist may include references to a second plurality of audio files, where playout of the second plurality of audio files may have a duration that is less than the duration of the playout of the first plurality of audio files. The server device may transmit, over the wide area network, the second playlist to the client device. Reception of the second playlist at the client device may cause the audio player application to retrieve and play out the second plurality of audio files.

Abstract: A machine may be configured to generate one or more audio fingerprints of one or more segments of audio data. The machine may access audio data to be fingerprinted and divide the audio data into segments. For any given segment, the machine may generate a spectral representation from the segment; generate a vector from the spectral representation; generate an ordered set of permutations of the vector; generate an ordered set of numbers from the permutations of the vector; and generate a fingerprint of the segment of the audio data, which may be considered a sub-fingerprint of the audio data. In addition, the machine or a separate device may be configured to determine a likelihood that candidate audio data matches reference audio data.