Abstract: A method of determining relations between music items, wherein a music item is a submix of a musical composition comprising one or more music tracks, the method comprising determining a first input representation for at least part of a first music item, mapping the first input representation onto to one or more subspaces derived from a vector space using a first model, wherein each subspace models a characteristic of the music items, determining a second input representation for at least part of a second music item, mapping the second input representation onto the one or more subspaces using a second model, and determining a distance between the mappings of the first and second input representations in each subspace, wherein the distance represents the degree of relation between the first and second input representations with respect to the characteristic modelled by the subspace.

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

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.

Abstract: In accordance with an embodiment, described herein is a system and method for client-initiated playlist shuffle in a media content environment. A shuffle logic is configured to provide a shuffle order for a plurality of media content items, including associating each media content item with a placement interval within which the media content item can be placed, and a weight that determines the average position of the placement interval; associating each media content item with a random value that indicates a random offset or position within its placement interval; calculating an ordering score for each media content item based on its weight plus its random offset into its placement interval; collecting indications for the plurality of media content item that reflect their ordering scores; and placing the plurality of media content items into the shuffle order, for subsequent playback by a media device.

Abstract: A method for generating a seed for a shuffle algorithm, the method performed by a plurality of computing devices and comprising, at each computing device in the plurality, generating a respective plurality of seeds for a shuffle algorithm, determining a fitness score for each of the generated seeds, and identifying a seed among the generated seeds having the best fitness score, wherein the number of generated seeds is determined based on a predetermined limit.

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 system and method for media content sequencing. Prior tracks for a listening session are segmented into groups based on attribute scores for an audial attribute. A preferred group is then selected, which can be based on user feedback regarding the prior tracks in the listening session. Candidate tracks, such as from a candidate track pool for future playback in the listening session, are also segmented into the groups of the prior tracks. The candidate tracks can then be ranked based on their associated group and the preferred group.

Abstract: A wake word detector, at a server of a content delivery network (CDN) that provides audio (or other) content to a device, such as a voice-enabled device, detects false wake words in the audio content. The CDN wake word detector analyzes the audio stream to determine if the audio stream contains any audio that sounds like the wake word. If so, the CDN wake word detector can generate metadata that describes the time period, within the audio content, in which the false wake word was encountered. The metadata can include time offsets, from the start of the audio content, which can instruct a voice-enabled device to deactivate during the time period. This metadata is stored and then sent to the media-playback device requests the media content. The media-playback device can then instruct or inform the voice-enabled device of the presence of the false wake word. In this way, the wake word detector, at the voice-enabled device, is not activated to receive the false wake word.

Abstract: A second wake word detector, at a media-playback device, that plays audio (or other) content to a device, such as a voice-enabled device, detects false wake words in the audio content. The second wake word detector analyzes the audio stream to determine if the audio stream contains any audio that sounds like the wake word. If so, the second wake word detector can generate one of a plurality of instructions that describes the time period, within the audio content, in which the false wake word was encountered. The instruction can cause a first wake word detector to assume one of a plurality of configurations. The media-playback device can then instruct or inform the voice-enabled device of the presence of the false wake word. In this way, the wake word detector, at the voice-enabled device, is not activated to receive the false wake word or ignores the wake word.

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 program product for accessing content based on an image. The method comprises comparing an image to a database of images, each of the images of the database being associated with at least one corresponding audio track, identifying those ones images of the database that correspond to the image, and identifying the at least one corresponding audio track that corresponds to the identified images. In one example aspect, the method also comprises presenting the audio track to a user. Corresponding metadata also can be presented. The images may be classified by, e.g., genre, musical album, concept, or the like, and, in cases where an input image is determined to belong to any such classes, audio content and/or metadata relating thereto are identified and presented to the user.

Abstract: Methods, systems and computer program products are provided personalizing recommendations of items with associated explanations. The example embodiments described herein use contextual bandits to personalize explainable recommendations (“recsplanations”) as treatments (“Bart”). Bart learns and predicts satisfaction (e.g., click-through rate, consumption probability) for any combination of item, explanation, and context and, through logging and contextual bandit retraining, can learn from its mistakes in an online setting.

Abstract: A text-to-speech engine creates audio output that includes synthesized speech and one or more media content item snippets. The input text is obtained and partitioned into text sets. A track having lyrics that match a part of one of the text sets is identified. The location of the track's audio that contains the lyric is extracted based on forced alignment data. The extracted audio is combined with synthesized speech corresponding to the remainder of the input text to form audio output.

Abstract: Methods, systems, and computer programs for generating a playlist of media content items for a group of users. Media content items listened to by the selected users are compared to an average user taste profile to select media content items for playback to the group of users.

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 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 electronic device generates a score for each objective in a hierarchy of objectives. Generating the score comprises using a representation of the media content item and a user as inputs to a first machine learning algorithm, to generate 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 respective score between the user and the media content item using the score for the first objective and the score for the second objective and provides media content to the user based on the respective scores.

Abstract: Described herein are techniques for streaming digital media content, such as music, video, or television content. In accordance with some embodiments, a server system associated with a media-providing service provides first media content to a user of the media-providing service. The server system analyzes the first media content and selects second media content for delivery in conjunction with the first media content based at least in part on the analysis of the first media content. The method further comprises providing the second media content in conjunction with the first media content.

Abstract: A descriptive media content search solution is provided to allow a user to search for media content that better matches a user's descriptive search request. The descriptive media content search solution utilizes an extensive catalog of playlists each having a playlist description, such as a playlist title or other descriptive text, and identifies additional descriptive information for media content items to be searched. The descriptive media content search solution can set up a descriptive search database and utilize the descriptive search database to conduct a descriptive search responsive to the user's descriptive search request.

Abstract: A server system accesses a profile of a user of the media-providing service. The profile indicates a demographic group of the user. For each media item of a plurality of media items, the server system determines a year associated with the media item. The server system selects content for the user based at least in part on an affinity of members of the demographic group, as compared to members of other demographic groups, for content from the year associated with the media item. The server system provides the selected content to a client device associated with the user.