A first playback is configured to (i) determine that it is connected to a local data network using a first networking protocol, (ii) receive a command to join a second playback device in a group of playback devices that are configured for synchronous playback of audio content from an audio source, (iii) determine that the second playback device is connected to the local data network using a second networking protocol different from the first networking protocol, (iv) based on determining that the second playback device is connected to the local data network using a second networking protocol different from the first networking protocol, configure the first playback device as a group coordinator of the group of playback devices, and (v) while rendering the audio content, (a) receive the audio content from the audio source and (b) forward at least a portion of the received audio content to the second playback device.

A layered-keyframe-based, presentation recording service provides for presentation recording sessions, the recording of presentations, and the creation of presentation videos. A user records with the user's device the document pages and page annotations, as well audio and video streams, that are presented using the device during the course of a presentation recording session. The pages, annotations and video streams are efficiently and separately recorded as keyframes. These keyframes are used as document, annotation and video layers to create layered keyframes. A presentation video is created from the layered keyframes and the recorded audio stream. Users can then playback presentation videos at a time, place and manner that is available to, accessible by and/or convenient to them.

Certain exemplary embodiments relate to entertainment systems and, more particularly, certain exemplary embodiments relate to jukebox systems that incorporate digital downloading jukebox features along with karaoke jukebox and/or photo booth features. A combined karaoke/photo booth/jukebox may enable more integrated performance-like experiences in an in-home or out-of-home location or venue. By leveraging vast audio media libraries, trusted rights-respecting network infrastructure, and on-site image/video capturing from integrated recorders and/or remote portable devices, a more sociable experience may be created for karaoke jukebox patrons, e.g., where custom content can be generated and shared in a safe and legally appropriate manner.

The present disclosure relates generally to content delivery techniques in audio-visual streaming systems. The techniques include altering video or audio portions of media content based on user input or interaction. The techniques further include altering text or messaging distributed to multiple users based on user input.

Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e., non-overlapping) video segments with a corresponding level of granularity.

Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e., non-overlapping) video segments with a corresponding level of granularity.

Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e., non-overlapping) video segments with a corresponding level of granularity.

Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e., non-overlapping) video segments with a corresponding level of granularity.

A video sequence layout method, electronic device and storage medium are provided, and relate to fields of deep learning, virtual reality, cloud computing, video layout processing and the like. The method includes: acquiring a first video sequence, the first video sequence including a main sequence for describing a first posture of a human body and a subordinate sequence for describing a plurality of second postures of the human body; extracting the main sequence and the subordinate sequence from the first video sequence; and in a case that it is detected that a sequencing identification frame exists in the first video sequence, performing random mixed sequencing processing on video frames in the main sequence and the subordinate sequence based on the sequencing identification frame and taking a sequence combination obtained by the random mixed sequencing processing as a second video sequence.

Embodiments are directed to segmentation and hierarchical clustering of video. In an example implementation, a video is ingested to generate a multi-level hierarchical segmentation of the video. In some embodiments, the finest level identifies a smallest interaction unit of the video—semantically defined video segments of unequal duration called clip atoms. Clip atom boundaries are detected in various ways. For example, speech boundaries are detected from audio of the video, and scene boundaries are detected from video frames of the video. The detected boundaries are used to define the clip atoms, which are hierarchically clustered to form a multi-level hierarchical representation of the video. In some cases, the hierarchical segmentation identifies a static, pre-computed, hierarchical set of video segments, where each level of the hierarchical segmentation identifies a complete set (i.e., covering the entire range of the video) of disjoint (i.e., non-overlapping) video segments with a corresponding level of granularity.