Abstract: Embodiments are disclosed for performing a filler word detection process on input audio by a media editing system using trained neural networks. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence, analyzing the audio sequence to determine filler word candidates, classifying, by a filler word classification model, each filler word candidate of the filler word candidates into one of a set of categories, and generating an output audio sequence, the output audio sequence including an identification of a subset of the filler word candidates in a filler words category of the set of categories as identified filler words.

Abstract: Embodiments are directed to a thumbnail segmentation that defines the locations on a video timeline where thumbnails are displayed. Candidate thumbnail locations are determined from boundaries of feature ranges of the video indicating when instances of detected features are present in the video. In some embodiments, candidate thumbnail separations are penalized for being separated by less than a minimum duration corresponding to a minimum pixel separation (e.g., the width of a thumbnail) between consecutive thumbnail locations on a video timeline. The thumbnail segmentation is computed by solving a shortest path problem through a graph that models different thumbnail locations and separations. As such, a video timeline is displayed with thumbnails at locations on the timeline defined by the thumbnail segmentation, with each thumbnail depicting a portion of the video associated with the thumbnail location.

Abstract: Embodiments are directed to interactive tiles that represent video segments of a segmentation of a video. In some embodiments, each interactive tile represents a different video segment from a particular video segmentation (e.g., a default video segmentation). Each interactive tile includes a thumbnail (e.g., the first frame of the video segment represented by the tile), some transcript from the beginning of the video segment, a visualization of detected faces in the video segment, and one or more faceted timelines that visualize a category of detected features (e.g., a visualization of detected visual scenes, audio classifications, visual artifacts). In some embodiments, interacting with a particular interactive tile navigates to a corresponding portion of the video, adds a corresponding video segment to a selection, and/or scrubs through tile thumbnails.

Abstract: Embodiments are disclosed for correlating video sequences and audio sequences by a media recommendation system using a trained encoder network.

Abstract: Embodiments are disclosed for performing a section-based, within-song music similarity search by an audio recommendation system. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and a request to determine similar audio sequences to the audio sequence from a pre-processed audio catalog, analyzing the audio sequence to generate an audio embedding for the audio sequence, querying a pre-processed audio catalog to retrieve audio embeddings for catalog audio sequences at different time resolutions, generating a set of candidate audio sequences from the pre-processed audio catalog based on the audio embedding for the audio sequence, and providing the set of candidate audio sequences.

Abstract: Embodiments are disclosed for generating an audio segmentation of an audio sequence using deep embeddings. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an audio sequence and extracting features for each frame of the audio sequence, where each frame is associated with a beat of the audio sequence. The method may further comprise clustering frames of the audio sequence into one or more clusters based on the extracted features and generating segments of the audio sequence based on the clustered frames, where each segment includes frames of the audio sequence from a same cluster. The method may further comprise constructing a multi-level audio segmentation of the audio sequence and performing a segment fusioning process that merges shorter segments with neighboring segments based on cluster assignments.

Abstract: Various embodiments of a modular signage system having a modular post that enables placement and display of a plurality of signs. The modular post includes a tab chamber that engages one or more signs and is configured to engage a stake for embedding within a ground surface.

Abstract: Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Abstract: A computer system is trained to understand audio-visual spatial correspondence using audio-visual clips having multi-channel audio. The computer system includes an audio subnetwork, video subnetwork, and pretext subnetwork. The audio subnetwork receives the two channels of audio from the audio-visual clips, and the video subnetwork receives the video frames from the audio-visual clips. In a subset of the audio-visual clips the audio-visual spatial relationship is misaligned, causing the audio-visual spatial cues for the audio and video to be incorrect. The audio subnetwork outputs an audio feature vector for each audio-visual clip, and the video subnetwork outputs a video feature vector for each audio-visual clip. The audio and video feature vectors for each audio-visual clip are merged and provided to the pretext subnetwork, which is configured to classify the merged vector as either having a misaligned audio-visual spatial relationship or not.

Abstract: Embodiments are directed to interactive tiles that represent video segments of a segmentation of a video. In some embodiments, each interactive tile represents a different video segment from a particular video segmentation (e.g., a default video segmentation). Each interactive tile includes a thumbnail (e.g., the first frame of the video segment represented by the tile), some transcript from the beginning of the video segment, a visualization of detected faces in the video segment, and one or more faceted timelines that visualize a category of detected features (e.g., a visualization of detected visual scenes, audio classifications, visual artifacts). In some embodiments, interacting with a particular interactive tile navigates to a corresponding portion of the video, adds a corresponding video segment to a selection, and/or scrubs through tile thumbnails.

Abstract: Embodiments are directed to a snap point segmentation that defines the locations of selection snap points for a selection of video segments. Candidate snap points are determined from boundaries of feature ranges of the video indicating when instances of detected features are present in the video. In some embodiments, candidate snap point separations are penalized for being separated by less than a minimum duration corresponding to a minimum pixel separation between consecutive snap points on a video timeline. The snap point segmentation is computed by solving a shortest path problem through a graph that models different snap point locations and separations. When a user clicks or taps on the video timeline and drags, a selection snaps to the snap points defined by the snap point segmentation. In some embodiments, the snap points are displayed during a drag operation and disappear when the drag operation is released.

Abstract: Embodiments are directed to a thumbnail segmentation that defines the locations on a video timeline where thumbnails are displayed. Candidate thumbnail locations are determined from boundaries of feature ranges of the video indicating when instances of detected features are present in the video. In some embodiments, candidate thumbnail separations are penalized for being separated by less than a minimum duration corresponding to a minimum pixel separation (e.g., the width of a thumbnail) between consecutive thumbnail locations on a video timeline. The thumbnail segmentation is computed by solving a shortest path problem through a graph that models different thumbnail locations and separations. As such, a video timeline is displayed with thumbnails at locations on the timeline defined by the thumbnail segmentation, with each thumbnail depicting a portion of the video associated with the thumbnail location.

Abstract: A computer system is trained to understand audio-visual spatial correspondence using audio-visual clips having multi-channel audio. The computer system includes an audio subnetwork, video subnetwork, and pretext subnetwork. The audio subnetwork receives the two channels of audio from the audio-visual clips, and the video subnetwork receives the video frames from the audio-visual clips. In a subset of the audio-visual clips the audio-visual spatial relationship is misaligned, causing the audio-visual spatial cues for the audio and video to be incorrect. The audio subnetwork outputs an audio feature vector for each audio-visual clip, and the video subnetwork outputs a video feature vector for each audio-visual clip. The audio and video feature vectors for each audio-visual clip are merged and provided to the pretext subnetwork, which is configured to classify the merged vector as either having a misaligned audio-visual spatial relationship or not.

Abstract: Various embodiments of a modular signage system having a modular post that enables placement and display of a plurality of signs. The modular post includes a tab chamber that engages one or more signs and is configured to engage a stake for embedding within a ground surface.

Abstract: Certain embodiments involve techniques for automatically identifying sounds in an audio recording that match a selected sound. An audio search and editing system receives the audio recording and preprocesses the audio recording into audio portions. The audio portions are provided as a query to the neural network that includes a trained embedding model used to analyze the audio portions in view of the selected sound to estimate feature vectors. The audio search and editing system compares the feature vectors for the audio portions against the feature vector for the selected sound and the feature vector for the negative samples to generate an audio score that is a numerical representation of the level of similarity between the audio portion and the selected sound and uses the audio scores to classify the audio portions into a first class of matching sounds and a second class of non-matching sounds.

Abstract: A system for monitoring a condition of a machine includes an acoustic detector configured to capture an audio signal of the machine. A controller is communicatively coupled to the audio detector and configured to transmit the audio signal to a remote computing unit. The remote computing unit configured to generate a condition status signal based on at least one of an unsupervised machine learning process or a supervised machine learning process. The controller is configured to receive the condition status signal from the remote computing unit and communicate a condition status based on the received condition status signal.