Abstract: An electronic device and method that automatically adjusts an audio output volume level based on a live environmental acoustic scenario input via a microphone using a machine learning algorithm trained with Human Activity Recognition (HAR). Equipped with such an intelligence the electronic device classifies ambient sounds occurring in the environment of the listening area in which the device is situated into different acoustic scenario mappings such a voice or conversation, for an ambient human conversation detected event, and noise, such as for example a vacuum cleaner or dish washer noise detected event, and automatically adjust the audio output volume accordingly.

Abstract: An electronic device and method that automatically adjusts an audio output volume level based on a live environmental acoustic scenario input via a microphone using a machine learning algorithm trained with Human Activity Recognition (HAR). Equipped with such an intelligence the electronic device classifies ambient sounds occurring in the environment of the listening area in which the device is situated into different acoustic scenario mappings such a voice or conversation, for an ambient human conversation detected event, and noise, such as for example a vacuum cleaner or dish washer noise detected event, and automatically adjust the audio output volume accordingly.

Abstract: An electronic device and method that automatically adjusts an audio output volume level based on a live environmental acoustic scenario input via a microphone using a machine learning algorithm trained with Human Activity Recognition (HAR). Equipped with such an intelligence the electronic device classifies ambient sounds occurring in the environment of the listening area in which the device is situated into different acoustic scenario mappings such a voice or conversation, for an ambient human conversation detected event, and noise, such as for example a vacuum cleaner or dish washer noise detected event, and automatically adjust the audio output volume accordingly.

Abstract: An electronic device and method that automatically adjusts an audio output volume level based on a live environmental acoustic scenario input via a microphone using a machine learning algorithm trained with Human Activity Recognition (HAR). Equipped with such an intelligence the electronic device classifies ambient sounds occurring in the environment of the listening area in which the device is situated into different acoustic scenario mappings such a voice or conversation, for an ambient human conversation detected event, and noise, such as for example a vacuum cleaner or dish washer noise detected event, and automatically adjust the audio output volume accordingly.

Abstract: Methods, systems, and computer readable media may be operable to facilitate the creation of personalized partial recordings of multimedia content. A partial recording of a piece of content may be created based upon a user request being received at a multimedia device and a determination of partial recording start and termination points. In embodiments, the start point of a partial recording may be based upon a predetermined or user-input setback duration, or a preceding scene boundary may be identified as the partial recording start point. A partial recording may include a content preview comprising one or more portions of one or more content segments, and each of the one or more content segments may include a start point that is identified within the associated piece of content based upon a predetermined interval or an identification of scene boundaries within the content.

Abstract: Methods, systems, and computer readable media described herein may facilitate the characterization of content segments based on a type of content, thus providing a user with the flexibility to: record only the scene types of interest to the user; playback only the scene types of interest to the user; search a media library, wherein the results of the search will include a list of only the content having scene types of interest to the user; browse available user selected scenes from clients; reposition content to user interested scene type during playback; and/or generate a detailed listing of the content based on the scene types. Scene change detection and scene type metadata may be used to identify scene boundaries within a piece of content and to characterize a scene based on a scene type.

Abstract: Methods, systems, and computer readable media described herein may facilitate the characterization of content segments based on a type of content, thus providing a user with the flexibility to: record only the scene types of interest to the user; playback only the scene types of interest to the user; search a media library, wherein the results of the search will include a list of only the content having scene types of interest to the user; browse available user selected scenes from clients; reposition content to user interested scene type during playback; and/or generate a detailed listing of the content based on the scene types. Scene change detection and scene type metadata may be used to identify scene boundaries within a piece of content and to characterize a scene based on a scene type.

Abstract: Methods, systems, and computer readable media may be operable to facilitate the creation of personalized partial recordings of multimedia content. A partial recording of a piece of content may be created based upon a user request being received at a multimedia device and a determination of partial recording start and termination points. In embodiments, the start point of a partial recording may be based upon a predetermined or user-input setback duration, or a preceding scene boundary may be identified as the partial recording start point. A partial recording may include a content preview comprising one or more portions of one or more content segments, and each of the one or more content segments may include a start point that is identified within the associated piece of content based upon a predetermined interval or an identification of scene boundaries within the content.