Abstract: A computer extracts a vocal portion from a first audio content item and determines a first representative vector that corresponds to a vocal style of the first audio content item by applying a variational autoencoder (VAE) to the extracted vocal portion of the representation of the audio content item. The computer streams, to an electronic device, a second audio content item, selected from a plurality of audio content items, that has a second representative vector that corresponds to a vocal style of the second audio content item, wherein the second representative vector that corresponds the vocal style of the second audio content item meets similarity criteria with respect to the first representative vector that corresponds to the vocal style of the first audio content item.

Abstract: Apparatus, systems and methods for augmenting a group of media content items by forming a graph including a plurality of nodes and a plurality of edges, where each node represents a segue option at a position in the graph and each edge represents a connection between a first node in the graph at a first position and a second node in the graph at a second position and finding a path in the graph.

Abstract: This disclosure concerns the provision of media, and more particularly streaming of media. In particular, one aspect herein relates to a method performed by a server system of streaming an audio content item to an electronic device. In response to receiving a request message from the electronic device, a selected audio content item is retrieved. A second storage is browsed utilizing to locate non-static media content item(s) associated with the selected audi content item. In response to finding a non-static media content item associated with the selected audio content item, the selected audio content item is sent along with the located non-static media content item to the electronic device for simultaneous presentation of the audio content item and the located non static media content item.

Abstract: A method includes retrieving a plurality of transcripts from a database. Each transcript in the plurality of transcripts corresponds to audio from a media content item of a plurality of media content items that are provided by a media providing service. The method also includes applying each transcript of the plurality of transcripts to a trained computational model, and receiving a user request for information regarding a topic. The method further includes, in response to the user request, identifying a transcript from the database that is relevant to the topic, and a position within the transcript that is relevant to the topic. The method also includes providing, by the media providing service, at least a portion of a media content item corresponding to the identified transcript, beginning at a starting position that is based on the position within the identified transcript that is relevant to the topic.

Abstract: Systems, devices, apparatuses, components, methods, and techniques for building customized media programs for a specified duration are provided. An example media-playback device for generating customized media programs for a route to a specified duration includes a user preferences engine to determine a user's listening preferences, a duration engine to determine a duration of the route, and a duration-based media program engine for building a playlist of news and entertainment media content based on the duration of the route and the preferences of the user.

Abstract: An electronic device generates a respective user queue for each user of a plurality of users participating in a shared listening session. While providing a first media content item for playback, the device receives a second request, from a first user, to add a second media content item to the shared playback queue and updates the respective user queue for the first user. After receiving the second request, the electronic device receives a third request, from a second user, to add a third media content item to the shared playback queue and updates the respective user queue for the second user. The electronic device updates the shared playback queue using the respective user queues of the first user and the second user, including positioning the third media content item in an order of the shared playback queue to be played back before the second media content item.

Abstract: A method for communicating a playback order for a plurality of media content items to a user device operating in an online mode, the method performed at a server system and comprising receiving an indication that the user device will enter an offline mode, generating a playback order for the plurality of media content items, and transmitting the generated playback order to the user device before the user device enters the offline mode.

Abstract: A system for supporting a user's repetitive motion activity operates to manage cadence-based playlists identifying one or more media content items having a tempo corresponding to a user's cadence. The cadence-based playlists can be categorized by different tempi or tempo ranges that cover all likely cadences during the user's activities. A media-playback device is provided to acquire a user's cadence and retrieve a cadence-based playlist associated with a tempo or a tempo range corresponding to the cadence.

Abstract: An electronic device obtains a listening history for a media item, the listening history including retention information indicating, for each respective portion of a plurality of portions of the media item, a number of listeners who listened to the respective portion of the media item. The electronic device, using the retention information, determines a pattern indicating a reduction in the number of listeners who listened to corresponding portions of the media item and determines a start time and an end time corresponding to a first portion of the corresponding portions of the media item. In accordance with the determination that the first portion of the corresponding portions of the media item meets predefined sub-content criteria, the electronic device stores an indication that the first portion of the media item comprises first sub-content, different from primary content, embedded in the media item.

Abstract: An electronic device receives a first audio content item that includes a plurality of sound sources. The electronic device generates a representation of the first audio content item. The electronic device determines, from the representation of the first audio content item: a representation of an isolated sound source, and frequency data associated with the isolated sound source. Determining the representation of the isolated sound source and the frequency data associated with the isolated sound source includes using a neural network to jointly determine the representation of the isolated sound source and the frequency data associated with the isolated sound source. The electronic device determines that a portion of a second audio content item matches the first audio content item using the representation of the isolated sound source and/or the frequency data associated with the isolated sound source.

Abstract: A system, method and computer product for training a neural network system. The method comprises applying an audio signal to the neural network system, the audio signal including a vocal component and a non-vocal component. The method also comprises comparing an output of the neural network system to a target signal, and adjusting at least one parameter of the neural network system to reduce a result of the comparing, for training the neural network system to estimate one of the vocal component and the non-vocal component. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate vocal or instrumental components of an audio signal, depending on which type of component the system is trained to estimate.

Abstract: An example system for playing media content with a media playback device in a vehicle can be programmed to obtain a sound measurement indicative of a sound level associated with playback of the media content by the media playback device in the vehicle. The example system also can determine a deviation in an expected sound level based upon the sound measurement. Finally, the system can modify playback of the media content by the media playback device (110) based upon the deviation.

Abstract: Methods, systems and computer program products are provided for content generation. A distribution of policies is defined based on an action space. Distribution parameters are received from a reinforcement learning (RL) algorithm. In turn, a policy is randomly sampled from the distribution of policies. A candidate content item is generated using the sampled policy. A quality of the candidate content item is measured based on a predefined quality criteria and a parameter model is adjusted as specified by the reinforcement learning algorithm to obtain a plurality of updated distribution parameters. Environment settings are passed to a trained parameter model to obtain a plurality of policy distribution parameters. A predetermined number of policies from the distribution of policies are then sampled and the plurality of environment settings are passed to the predetermined number of sampled policies to obtain at least one content item.

Abstract: A media content item recommendation system recommends media content items based on one or more attributes of a seed playlist. The recommended media content items can be determined from a plurality of existing playlists that have been created over a period of time. Such existing playlists can be selected based on similarity to the seed playlist.

Abstract: An adaptive multi-model item selection method, comprising: receiving, from one of a plurality of client devices, a request including a client-side feature vector representing a state of the client device; determining, by an advocate model, a probability distribution of a plurality of specialist cluster models from the client-side feature vector; choosing, by a use case selector, a cluster corresponding to a use case from the probability distribution; and obtaining, by the use case selector based on the cluster (i.e., the cluster that was sampled by the user case selector), a specialist cluster model from the plurality of specialist cluster models.

Abstract: An electronic device, for each media content item of a plurality of media content items, receives a respective score for each a first set of objectives and one or more other objectives and generates a respective score between a user and the media content item. The generating includes applying a first ordered weighted average to the respective scores for the first set of objectives, to produce a first combined score for the first set of objectives, applying a second ordered weighted average to the respective scores for a second set of objectives, wherein the second set of objectives includes (i) a resulting objective corresponding to the first set of objectives and having the first combined score and (ii) the one or more other objectives. The electronic device streams media content to the user selected based on the respective scores between the user and the media content items.

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: Systems, devices, apparatuses, components, methods, and techniques for generating and playing a selectable content depth media program are provided. Media content items are edited to produce selectable depth media segments which are assembled into selectable depth media programs. A media-playback device is configured to navigate and play the selectable depth media program through interaction by a listening user. The user selects the desired content depth for each media segment.

Abstract: Systems, methods, and devices for human-machine interfaces for utterance-based playlist selection are disclosed. In one method, a list of playlists is traversed and a portion of each is audibly output until a playlist command is received. Based on the playlist command, the traversing is stopped and a playlist is selected for playback. In examples, the list of playlists is modified based on a modification input.

Abstract: A system is provided for streaming media content in a vehicle. The system includes a personal media streaming appliance system configured to connect to a media delivery system and receive media content from the media delivery system at least via a cellular network. The personal media streaming appliance system operates to transmit a media signal representative to the received media content to a vehicle media playback system so that the vehicle media playback system operates to play the media content in the vehicle. Customized voice communications are generated based on receiving input, such as a user query and/or a media track change indication.