Abstract:
Systems, devices, apparatuses, components, methods, and techniques for cadence determination and media content selection are provided. An example media-playback device comprises a media-output device that plays media content items, a cadence-acquiring device, and a cadence-based media content selection engine. The cadence-acquiring device includes an accelerometer and a cadence-determination engine configured to determine a cadence based on acceleration data captured by the accelerometer. The cadence-based media content selection engine is configured to identify a media content item based on the cadence determined by the cadence-determining engine and cause the media-output device to playback the identified media content item.
Abstract:
Systems, devices, apparatuses, components, methods, and techniques for identifying media content for playback during a repetitive motion activity are provided. An example media-playback device includes a media-output device that plays media content items and a repetitive-motion activity content identification engine. The repetitive-motion activity content identification engine is configured to: review media content items to identify the media content items that are conducive to performing repetitive-motion activities; and select certain media content items from the media content items, the certain media content items being conducive to performing the repetitive-motion activities.
Abstract:
A system for playing media content items operates to provide smooth transitions between the media content items to continuously support a user's repetitive motion activity. The system can generate crossfade data containing information for transitions between media content items. The mix-in and mix-out points for the transitions are calculated to eliminate one or more portions of media content items that have lower musical energy than a majority portion of the items, and to maintain substantially consistent and/or stable musical energy (e.g., audio power or sound power) throughout the media content items including transitions therebetween.
Abstract:
Systems, devices, apparatuses, components, methods, and techniques for automatically generating media previews are provided. An example media system for automatically generating media previews for a particular artist include a trailer generation application configured to receive input specifying an artist and duration of a trailer, automatically select clips from two or more media items by the artist, and automatically arrange and combine the clips into a media trailer for later playback.
Abstract:
A media-playback device acquires a heart rate, selects a song with a first tempo, and initiates playback of the song. The song meets a set of qualification criteria and the first tempo is based on the heart rate, such as being equal to or less than the heart rate. The media-playback device also initiates playback of a binaural beat at a first frequency. Over a period of time, the binaural beat's first frequency is changed to a second frequency. Over the period of time, the first tempo can also be changed to a second tempo, where the second tempo is slower than the first tempo.
Abstract:
Systems, devices, apparatuses, components, methods, and techniques for automatically generating media previews are provided. An example media system for automatically generating media previews for a particular artist include a trailer generation application configured to receive input specifying an artist and duration of a trailer, automatically select clips from two or more media items by the artist, and automatically arrange and combine the clips into a media trailer for later playback.
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 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 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 lyrics analyzer generates tags and explicitness indicators for a set of tracks. These tags may indicate the genre, mood, occasion, or other features of each track. The lyrics analyzer does so by generating an n-dimensional vector relating to a set of topics extracted from the lyrics and then using those vectors to train a classifier to determine whether each tag applies to each track. The lyrics analyzer may also generate playlists for a user based on a single seed song by comparing the lyrics vector or the lyrics and acoustics vectors of the seed song to other songs to select songs that closely match the seed song. Such a playlist generator may also take into account the tags generated for each track.