Abstract: Apparatus, methods and computer-readable medium are provided for processing wind noise. Audio input is processed by receiving an audio input. A wind noise level representative of a wind noise at the microphone array is measured using the audio input and a determination is made, based on the wind noise level, whether to perform either (i) a wind noise suppression process on the audio input on-device, or (ii) the wind noise suppression process on the audio input on-device and an audio reconstruction process in-cloud.

Abstract: While a first media content item from a shared playback session is being presented on a set of presentation devices, each of the set of presentation devices corresponding to a respective user of a plurality of users in the shared playback session, a master device receives a request to modify playback of the shared playback session from an observer device of a set of observer devices that corresponds to a presentation device for a first user, wherein the set of observer devices is different from the set of presentation devices. In response to the request to modify playback of the shared playback session, the master device sends a command for an action selected based on the request to each of the set of presentation devices.

Abstract: A system for refinement of a voice query interpretation interprets a voice query received at a voice-enabled device to identify commands responsive to the voice query for execution at the voice-enabled device, and enables refinement of the interpretation of the voice query through a graphical user interface generated and displayed at a GUI-capable device. The graphical user interface includes a set of selectable options relating to the voice query and identifying a refinement of the interpretation of the voice query to enable control and/or adjustment of commands to be executed by the voice-enabled device. For example, if one of the selectable options is selected, then a command associated with the selected option is identified and executed by the voice-enabled device.

Abstract: An electronic device stores, for a user of a media-providing service, a playback history that includes information about media items that have previously been consumed by the user. The electronic device receives a request to search for media content including search criteria. In response to the request, and without additional user intervention, the electronic device generates a vector representation of the user using media items from the playback history of the user that are relevant to the search criteria. The electronic device identifies one or more media content items from a media content library that match the vector representation of the user and the search criteria and provides, to the user, the one or more media content items.

Abstract: Systems, devices, apparatuses, components, methods, and techniques for predicting user and media-playback device states are provided. Systems, devices, apparatuses, components, methods, and techniques for representing cached, user-selected, and streaming content are also provided.

Abstract: An electronic device generates a score for each objective in a hierarchy of objectives. Generating the score comprises, using a first machine learning algorithm, generating a score for a first objective corresponding to a first level in the hierarchy of the objectives and using an output of the first machine learning algorithm, distinct from the score for the first objective, as an input to a second machine learning algorithm to generate a score for a second objective corresponding to a second level in the hierarchy of objectives. The electronic device generates a combined score using the score for the first objective and the score for the second objective. The electronic device selects, automatically without user input, media content based on the combined scores for the plurality of media content items and streams, using an application of the media-providing service, one or more of the selected media content to a user.

Abstract: A media content steering solution is provided to identify a user query to steer playback of media content that is currently playing or has been played. The user steering query can include a voice request for playing media content that is relatively different from the media content being currently played or having been played. The media content steering solution analyzes the utterance of the user query and uses it to identify such different content that satisfies the user intent contained in the user query.

Abstract: A server system receives, from a first client device, a video recording created by the first client device and an indication that the video recording is to be associated with a media content item. The server system retrieves text associated with the media content item and provides the text for display at the first client device as a text lens overlay that is mapped to a portion of an object in the video recording of the first client device and follows movement of the portion of an object in the video recording created by the first client device. The server system provides, to a second client device, the video recording in combination with the media content item; and the text associated with the media content item as the text lens overlay that is mapped to the portion of the object of the first client device.

Abstract: A method is provided for initiating a first shared playback session at a first device of a host user. The method includes, in response to receiving the request from the second device: in accordance with a determination that the second device is a first type of device, providing, to the second device, a first joining method for joining the first shared playback session and in accordance with a determination that the second device is a second type of device, providing, to the second device, a second joining method, distinct from the first joining method for joining the first shared playback session.

Abstract: A system implements a dynamic word correlated topic model (DWCTM) to model an evolution of topic popularity, word embedding, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DWCTM receives the set of documents and a quantity of topics for modeling. The DWCTM processes the set computing, for each topic, various distributions to capture a popularity, word embedding, and correlation with other topics across the period of time. In other examples, a dataset of user listening sessions comprised of media content items for modeling by the DWCTM. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be modeled by the DWCTM.

Abstract: One aspect herein relates to a method performed by a server system. In response to receiving a request message from a first electronic device, an audio content item is retrieved and a non-static content item associated with the audio content item is located. The server system transmits, to the first electronic device, the audio content item, and the located non-static media content item. The server system receives a second request message from a second electronic device, the second request message including an instruction for the server system to modify the non-static media content item associated with the audio content item. In response to the second request message, the server system modifies the non-static media content item associated with the audio content item in accordance with the instruction of the second request message from the second electronic device.

Abstract: Methods, systems and computer program products are provided for performing personalization. A segments reader receives a request for episode metadata corresponding to an episode. A determination is made whether the episode metadata should be personalized to an account associated with the request. In turn, personalized-episode metadata associated with the account and the request is retrieved and the personalized-episode metadata is provided to a client device associated with the request.

Abstract: A text-to-speech synthesis method comprising: receiving text; inputting the received text in a prediction network; and generating speech data, wherein the prediction network comprises a neural network, and wherein the neural network is trained by: receiving a first training dataset comprising audio data and corresponding text data; acquiring an expressivity score for each audio sample of the audio data, wherein the expressivity score is a quantitative representation of how well an audio sample conveys emotional information and sounds natural, realistic and human-like; training the neural network using a first sub-dataset, and further training the neural network using a second sub-dataset, wherein the first sub-dataset and the second sub-dataset comprise audio samples and corresponding text from the first training dataset and wherein the average expressivity score of the audio data in the second sub-dataset is higher than the average expressivity score of the audio data in the first sub-dataset.

Abstract: Simulator augmented content selection is provided by initializing a content selection object according to session initialization parameter values associated with a simulated media content playback session. The content selection object corresponds to a candidate content selection machine learning model trained to predict selectable content media items for at least one simulated user. A simulated session including a sequence of predicted simulated user next actions and one or more predicted sets of selectable content items are generated by applying a simulated user model to content items identified by the initialized content selection object, where the simulated user model is trained to predict a next action of the simulated user in response to a simulated playback input received from the simulated user and each set of the selectable content items are correlated to each next action in the sequence of predicted simulated user next actions.

Abstract: A system for device-to-device media capturing is described herein. An example system includes a media delivery system of a media service and at least a first and second device associated with respective first and second user accounts of the media service. The first device is also associated with an active media content item provided by the media delivery system that is automatically captured by the second device. For example, as the second device is moved proximate to the first device, one or more wireless communications are transmitted between the devices that trigger device-to-device media capturing. Resultantly, an identifier for the active media content item is stored to a library of the second user account of the media service. The identifier is stored in response to detecting the proximity of the devices and determining that the second device is moving towards the first device.

Abstract: A media system includes: a media-playback device including: a media-output device that plays media content items; and a tempo control engine to: select media content based upon suitability for a repetitive-motion activity; place each media content item from the media content into two or more pools of media content, with a first pool including media content which the user has previously indicated as being relevant, and a second pool including media content that the user has not previously indicated as being relevant; and sort the media content in each of the first pool and the second pool based upon tempo.

Abstract: Systems, devices, and methods for identifying media content using indirect qualities are provided. An example media-delivery system includes a content identification engine that applies a model associated with an indirect quality to media content items to generate indirect quality scores for the media content items, filters the media content items based on metadata associated with the media content items to generate filtered media content items, and presents at least some of the filtered media content items based on the indirect quality scores. An example media-playback device includes a search control engine that presents a search interface with a user-actuatable control for specifying a value of an indirect quality for use as a search criteria, transmits the search criteria to a media-delivery service, and receives media content items matching the search criteria from the media-delivery service, wherein the media content items are identified using a statistical model.

Abstract: An electronic device obtains a collection of images and obtains a media item. The electronic device selects a subset of the collection of images, including: selecting an initial subset of the collection of images, wherein the initial subset of the collection of images is based on descriptors associated with the collection of images and/or the media item; obtaining a set of preferences for a user of the media-providing service; and selecting the subset of the collection of images from the initial subset of the collection of images based on the set of preferences for the user of the media-providing service. The electronic device concurrently presents: a respective image of the subset of the collection of images; and the media item.

Abstract: A training audio track feature vector is generated for training audio tracks. The training audio track feature vector includes training track vector components based on one or more feature sets. Each of the training track vector components is grouped into at least one cluster. Audio filters are mapped to one or more of the clusters, thereby building a feature-filter mapping function. Mapping functions from filters to audio output devices and/or physical space acoustic features can also be built. A media playback device receives the mapping function(s) and is enabled to apply the mapping function(s) to a query audio track feature vector to identify at least one audio filter corresponding to the query audio track. The media playback device can then apply the at least one audio filter to the query audio track.