Abstract: Technology for generating, reading, and using machine-readable codes is disclosed. There is a method, performed by an image capture device, for reading and using the codes. The method includes obtaining an image, identifying an area in the image having a machine-readable code. The method also includes, within the image area, finding a predefined start marker defining a start point and a predefined stop marker defining a stop point, an axis being defined there between. A plurality of axis points can be defined along the axis. For each axis point, a first distance within the image area to a mark is determined. The distance can be measured from the axis point in a first direction which is orthogonal to the axis. The first distances can be converted to a binary code using Gray code such that each first distance encodes at least one bit of data in the code.

Abstract: Methods, systems, and related products that provide enhanced broadening and narrowing navigation capabilities through visualized populations of items, such as media content items. Aspects of the disclosure relate to methods of identifying a population of items and groupings of those items for visualizing, via a graphical interface, in a meaningful and easily navigable configuration. Aspects of the present disclosure relate to pre-selection interactions with visualized populations to explore potential narrowing or broadening navigation actions, and selection interactions for performing a narrowing or broadening navigation.

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: The invention relates to a system and method to cause an apparatus to perform at least the following; identifying music preferences by understanding the listening history of a user from one or more context signals associated with a user mobile device.

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 client computer includes a web browser connected to a local web server that is coupled with a local utility. Upon loading a web page, the web browser sends, to the local web server, a first open-ended message that does not require a return message from the local web server. In response to and upon receiving a response to the first open-ended message, the web browser maintains communication with the local web server by sending a second open-ended message that does not require a return message to the local web server. The local web server receives the first open-ended message, waits until the local utility determines that there is information to be provided to the web browser, and in response to determining that there is information to be provided to the web browser, sends a first return message including the information to the web browser.

Abstract: A system, method and computer product for estimating a component of a provided audio signal. The method comprises converting the provided audio signal to an image, processing the image with a neural network trained to estimate one of vocal content and instrumental content, and storing a spectral mask output from the neural network as a result of the image being processed by the neural network. The neural network is a U-Net. The method also comprises providing the spectral mask to a client media playback device, which applies the spectral mask to a spectrogram of the provided audio signal, to provide a masked spectrogram. The media playback device also transforms the masked spectrogram to an audio signal, and plays back that audio signal via an output user interface.

Abstract: A playlist preview is generated to provide a preview of media content items identified by a media playlist. The playlist preview can be created by selecting all or some of the media content items in the playlist, determining preview portions of the selected media content items, and arranging the preview portions with or without a transition effect. The playlist preview can be easily shared with other users through, for example, social media sites.

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 includes one or more preset buttons for playing media content associated with the preset buttons. The media contents associated with the preset buttons are automatically determined to be personalized to the user of the system.

Abstract: A method for providing access to media content from a media content provider is performed at an electronic device. The method includes receiving, from a client device, a request for access to a media item. The method further includes, in response to the request for access to the media item: initiating an heuristic analysis to determine whether the client device is authorized to access the media item, including an examination of a media consumption log associated with the client device; and asynchronously providing access to the media item while performing the heuristic analysis. The method further includes, based on the examination of the media consumption log, determining that the client device has reached an access limit of requests to access media content. The method further includes, in accordance with the determination, after the client device has been provided to access the media item, terminating access to the media item.

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: 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: Methods, systems, and computer programs for generating a playlist of media content items that are popular with the friends of the first user. A first user taste profile is determined and a user taste profile is determined for each of a plurality of social connections. A similarity score is calculated between the first user taste profile and the user taste profile of each social connection. Media content items consumed by social connections with the highest similarity score are selected and placed in a playlist for the first user.

Abstract: Systems, devices, apparatuses, components, methods, and techniques for identifying and playing media content are provided. An example media-playback device for identifying and playing media content for a user traveling in a vehicle includes an audio identification engine and a media playback engine. Audio content is recorded and identified by comparison to media content databases. The audio content is identified and immediately played on the same device. Additional media content is selected for playback based on user listening preferences.

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: An electronic device associated with a media-providing service assigns one or more characteristics of media items to at least one respective personality trait of a plurality of personality traits. The media items are provided by the media-providing service. The electronic device assigns one or more user behaviors to a first personality trait and tracks behavior of a user. The electronic device determines that a tracked behavior of the user corresponds to a first user behavior of the one or more user behaviors and assigns the first personality trait to the user based at least in part on determining that the tracked behavior of the user corresponds to the first user behavior. The electronic device provides personalized content to the user in accordance with a determination that the degree to which the tracked behavior of the user corresponds to the first user behavior satisfies a threshold.

Abstract: Methods, systems, and related products that provide enhanced personalized media content experiences. Past media content consumption is inferred based on requests for media content selection associated with a time or period of time in the past. The requests are fulfilled by selecting one or more media content items from the inferred media content consumption.

Abstract: Methods, systems and computer program products are provided for identifying an audio stem. Audio stems (t1, . . . , tN) are stored on a stem database and songs (S1, . . . , SP) made with at least a subset of the plurality of the audio stems (t1, . . . , tN) are stored on a song database. At least partially composed song (S*) having a predetermined number of pre-selected stems (k) are received. In turn, a probability vector (or relevance value or ranking) is produced for each stem (t1, . . . , tN) to be complementary to the at least partially composed song (S*).

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.