Abstract: The various implementations described herein include methods and devices for media segmentation. In one aspect, a method includes obtaining audio content for a podcast and generating sentence embeddings for the audio content. The method also includes generating segment embeddings using the sentence embeddings and context information, and determining, for each segment embedding, whether the segment embedding includes a topic transition for the podcast. The method further includes generating one or more topic transition timestamps for the podcast in accordance with the determining.

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.

Abstract: A computer system receives, while a first user is participating in a shared playback session that includes the first user and a plurality of users other than the first user, a request for a set of recommended media items. In response to receiving the request, the computer system: retrieves a first set of media items from a playback history of the first user; retrieves a plurality of probabilistic data structures for the plurality of users other than the first user, each probabilistic data structure indicating a playback history of a respective user of the plurality of users other than the first user; and provides, for display in a user interface, the set of recommended media items that comprises a subset of the first set of media items selected based on the playback histories of the plurality of users as indicated by the plurality of probabilistic data structures.

Abstract: Stateless playback is performed by receiving a request from a media playback device, the request requesting a playback position associated with a collaborative media consumption session. Upon receiving the request, a playback position for the collaborative media consumption session is computed. The playback position is then communicated to the media playback device.

Abstract: An example embodiment may involve: obtaining a ranking of media content items recommended for a user, wherein the media content items are respectively associated with sets of descriptors, and wherein the sets of descriptors were generated by a natural language model based on metadata associated with the media content items; based on relevance of the descriptors to the user and appearances of the descriptors in the ranking of the media content items, determining a ranking at least a portion of the descriptors; selecting groups of the media content items respectively associated with the descriptors in the ranking of the descriptors; and providing, for display on a user device associated with the user, the groups of the media content items populated on virtual shelves representing the respectively associated descriptors.

Abstract: Audio translation system includes a feature extractor and a style transfer machine learning model. The feature extractor generates for each of a plurality of source voice files one or more source voice parameters encoded as a collection of source feature vectors, and generates for each of a plurality of target voice files one or more target voice parameters encoded as a collection of target feature vectors. The style transfer machine learning model trained on the collection of source feature vectors for the plurality of source voice files and the collection of target feature vectors for the plurality of target voice files to generate a style transformed feature vector.

Abstract: An electronic system obtains a first plurality of records corresponding to a plurality of media items, wherein each record of the first plurality of records has at least one attribute of a plurality of attributes. The electronic system trains a machine-learning model by, for each record of the first plurality of records, masking a portion of an attribute of the record. An encoder of the machine-learning model produces a training embedding for the record, and a decoder of the machine-learning model predicts the masked portion of the attribute of the record, based on the training embedding. The electronic system uses the trained machine-learning model to produce an embedding for each record of a second plurality of records, and groups two or more records of the second plurality of records into a first group based on the embeddings of the two or more records.

Abstract: An electronic device associated with a media-providing service stores, in a vector space, a plurality of respective vector representations for respective media content items. The electronic device receives a user input, including a text string. The electronic device generates, using a neural network, a structured query based on the text string. The electronic device determines, based on the structured query, whether to generate a vector representation of a portion of the text string. When the electronic device determines to generate the vector representation of the portion of the text string, it generates the vector representation of the portion of the text string, wherein the vector representation is embedded in the vector space, and identifies a set of media items using the vector representation of the portion of the text string. And the electronic device provides one or more select media items from the set of media items to a user.

Abstract: A reinforcement learning ranker can take into account previously-recommended media content items to produce a ranked list of media content items to recommend next. The ranker finds a policy that gives the probability of sampling a media content item given a state. The policy is learned such that it maximizes a reward. A reward function associated with the media content item can be defined with respect to whether the user finds the media content item relevant (likelihood that the user will like the media content item) and a diversity score of the media content item.

Abstract: Systems and methods for performing bias bounded sensitivity analysis of synthetic control models are provided. Bias bounds may be determined for a synthetic control model using data from the synthetic control model. If a difference between a synthetic control determined by the synthetic control model and an observed outcome are within the bias bounds, a causal effect determined using the synthetic control model may be untrustworthy. Graphical representations may be presented based, at least in part, on the observed outcome, the synthetic control, and the bias bounds.

Abstract: An architecture is provided for displaying a list of connected devices by generating a first list of devices connected to the same local network and scanning for devices within a defined physical range of a media playback device. A second list of devices within the defined physical range is generated and compared to the first list to create a third list of devices with overlapping identifiers. The media playback device then displays the third list of devices on its interface according to proximity for the user to select an appropriate device for media playback.

Abstract: A computer system determines one or more media placement opportunities for a media application at a client device and generates a request message that includes an indication of each media placement opportunity. The request message has a hierarchical framework with a plurality of levels that includes, for each media placement opportunity: (i) a media application context level; and (ii) a sub-context level. The computer system transmits the request message to a server system; and receives, from the server system, a response to the request message that includes one or more media content items selected based on the information included in the hierarchical framework of the request message. The computer system provides, to the media application at the client device, at least one of the one or more media content items within at least one of the media placement opportunities.

Abstract: A method for stabilizing a magnetic field of an inertial measurement unit (IMU), is provided that includes initializing accelerometer and gyroscope (AG) heading data for the IMU and initializing accelerometer, gyroscope and magnetometer (AGM) heading data for the IMU. Determining whether a tracking state exists and completing processing of the AG heading data and the AGM heading data if the tracking state does not exist. Calculating a magnetic field error if the tracking state exists.

Abstract: A computer system associated with a media-providing service obtains a set of text descriptors from a plurality of media items in a pool of media items personalized for a first user. The system selects, from the set of text descriptors, a first text descriptor using first criteria and a second text descriptor using second criteria. The system causes an application associated with the media-providing service, executing on a client device of the first user, to concurrently display a first option for playing back a first playlist of media items associated with the first text descriptor and a second option for playing back a second playlist of media items associated with the second text descriptor. In response to receiving selection by the first user of the first option, the system provides, to the application, media items from the first playlist of media items associated with the first text descriptor.

Abstract: Systems and methods for snapping to seek positions in media content using seek guides are provided. Seek guides may be associated with a media content item. Each seek guide may have a position and a radius. When a user seeks to a reference position, a seek guide selector may select a seek guide to use in setting a new playback position. The seek guide selector may use a probabilistic distribution positioned based on the reference position to select a seek guide. Probabilities may be calculated for each seek guide using a cumulative distribution function, and a seek guide may be selected based on the calculated probabilities.

Abstract: A system for processing audio data, comprising a diffusion data filter coupled to a source of digital audio data, the diffusion data filter configured to generate filtered audio data from the digital audio data. A delay coupled to the source of digital audio data, the delay configured to delay the digital audio data by a predetermined amount. A first multiplier configured to multiply the filtered audio data by a distance gain parameter to generate a first intermediate output. A second multiplier configured to multiply the delayed digital audio data by a complementary distance gain parameter to generate a second intermediate output. An adder configured to add the first intermediate output and the second intermediate output to generate an audio output.