Abstract: A method includes displaying a user interface of a digital audio workstation, which includes a first region for generating a composition. The first region includes a first compositional segment that has been added to the composition by a user. Based on the first compositional segment, one or more recommended predefined compositional segments are identified and displayed in a second region. The method includes receiving the selection of a second compositional segment. The method includes adding the compositional segment to the composition.

Abstract: A training set for use during content generation is generated by applying a first machine learning process P1 to a first finite sequence s wherein s has a length Ls, to generate a first statistical model M(s). The first statistical model M(s) is sampled using a first sampling process G to generate a second finite sequence t wherein t has a length Lt. A second machine learning process P2 is applied to the second finite sequence t to generate a second statistical model M(t), wherein no substring of the second finite sequence t of length d is identical to a substring of the first finite sequence s, wherein d is a predetermined number of elements in a sequence.

Abstract: A system and method for generating dynamic playlists utilizing device co-presence proximity. In accordance with an embodiment, the method can include identifying music preferences by analyzing the listening history on a device of a user; and recognizing what devices are co-present within a set proximity for the purposes of matching such devices. In accordance with an embodiment, the system can be determine what music each individual in a group has been listening to historically, and determine when those individuals are within a certain proximity to one another, including for example analyzing the media content played on a smartphone and recognising when two smartphones are in a same location.

Abstract: Systems, apparatuses, components, methods, and techniques for location-based tagging and retrieving of media content are provided. Some embodiments include a media-playback device. The media-playback device includes a location-determining device that determines a geographical location of the media-playback device, a media-output device that plays media content items, at least one computer readable data storage device. The media-playback device operates to receive a selection of a location-based media playlist, the location-based media playlist including playlist data that identifies a set of media content items and defines a route. A geographic location of the media-playback device is determined using the location-determining device. A media content item of the location-based playlist is identified based on the determined geographic location and the route. The media-playback device plays the media content item.

Abstract: Term masking is performed by generating a time-alignment value for a plurality of units of sound in vocal audio content contained in a mixed audio track, force-aligning each of the plurality of units of sound to the vocal audio content based on the time-alignment value, thereby generating a plurality of force-aligned identifiable units of sound, identifying from the plurality of force-aligned units of sound a force-aligned unit of sound to be altered, and altering the identified force-aligned unit of sound.

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: A system, method and computer product for training a neural network system. The method comprises inputting an audio signal to the system to generate plural outputs f(X, ?). The audio signal includes one or more of vocal content and/or musical instrument content, and each output f(X, ?) corresponds to a respective one of the different content types. The method also comprises comparing individual outputs f(X, ?) of the neural network system to corresponding target signals. For each compared output f(X, ?), at least one parameter of the system is adjusted to reduce a result of the comparing performed for the output f(X, ?), to train the system to estimate the different content types. In one example embodiment, the system comprises a U-Net architecture. After training, the system can estimate various different types of vocal and/or instrument components of an audio signal, depending on which type of component(s) the system is trained to estimate.

Abstract: An electronic device stores a shared playback queue for the shared playback session, the shared playback queue comprising one or more media content items, including a first media content item requested by a first user. While the shared playback queue includes the first media content item, the device receives a second request, from a second user, to add a second media content item to the shared playback queue. The device determines an order of the shared playback queue based at least in part on the musical preferences indicated in a profile of a third user of the plurality of users participating in the shared playback session. The device provides, for playback in the shared playback session, the first media content item and the second media content item based on the order of the shared playback queue.

Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.

Abstract: A first client device is associated with a first user hosting a shared playback session. While a first media content item from the shared playback session is being presented on a set of presentation devices, the first client device communicates with a set of observer devices for the shared playback session. The first client device receives a request to modify playback of the shared playback session from a second client device, the second client device being one observer device of the set of observer devices. In response to the request to modify playback of the shared playback session, the first client device determines an action to take with respect to the shared playback session. In response to determining the action to take with respect to the shared playback session, the first client device sends a command for the action to each of the set of presentation devices.

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: 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 media delivery system is configured to link a user media streaming account with a particular personal media streaming appliance to provide personalized media content to the appliance. Media contexts are assigned to multiple preset settings automatically so that the personal media streaming appliance system is configured to output personalized media content upon first use.

Abstract: An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.

Abstract: An electronic device obtains a plurality of collections of media content items, each collection of media content items being associated with text generated by one or more users of the media-providing service. The electronic device determines a coincidence metric for a first descriptor and a first media content item, the coincidence metric corresponding to a likelihood that the first descriptor appears in the text associated with a respective collection of media content items that includes the first media content item. Based on the coincidence metric, the electronic device generates a new collection of media content items for a first user. The new collection of media content items corresponds to the first descriptor and includes the first media content item.

Abstract: A first device includes one or more processors and memory storing one or more programs configured to be executed by the one or more processors. The one or more programs include instructions for receiving, from a second device, audio mix information for a first audio item and receiving, from the second device, an indication that the first audio item is to be mixed with a second audio item distinct from the first audio item. In response to the indication, the one or more programs include instructions for transmitting to the second device an audio stream including the first audio item and the second audio item mixed in accordance with the audio mix information.

Abstract: A first electronic device is provided. While a media content item provided by a media-providing service is emitted by a second electronic device that is remote from the first electronic device, the first electronic device receives, from the media-providing service, data that includes an audio stream that corresponds to the media content item. The first electronic device detects ambient sound that includes sound corresponding to the media content item emitted by the second electronic device. The first electronic device generates a cleaned version of the ambient sound, which includes: using the data received from the media-providing service to align the audio stream with the ambient sound; and performing a subtraction operation to subtract the audio stream from the ambient sound. The first electronic device detects a voice command in the cleaned version of the ambient sound.

Abstract: An electronic device modifies a first media content item by superimposing a first set of data over a first accented musical event. The first accented musical event has a first audio profile. The first set of data has a second audio profile configured to be masked by the first audio profile during playback of the first media content item. The electronic device transmits, to a second electronic device, the modified first media content item.