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    • 1. 发明授权
    • Automated rich presentation of a semantic topic
    • 自动丰富的语义主题演示
    • US08572088B2
    • 2013-10-29
    • US11256411
    • 2005-10-21
    • Lie LuWei-Ying MaZhiwei Li
    • Lie LuWei-Ying MaZhiwei Li
    • G06F7/00G06F17/30
    • G06F17/30705G06F17/30056
    • Automated rich presentation of a semantic topic is described. In one aspect, respective portions of multimodal information corresponding to a semantic topic are evaluated to locate events associated with the semantic topic. The probability that a document belongs to an event is determined based on document inclusion of one or more of persons, times, locations, and keywords, and document distribution along a timeline associated with the event. For each event, one or more documents objectively determined to be substantially representative of the event are identified. One or more other types of media (e.g., video, images, etc.) related to the event are then extracted from the multimodal information. The representative documents and the other media are for presentation to a user in a storyboard.
    • 描述了语义主题的自动丰富呈现。 在一个方面,评估与语义主题相对应的多模态信息的相应部分,以定位与语义主题相关联的事件。 基于文档包含一个或多个人,时间,位置和关键字以及与事件相关联的时间轴的文档分发来确定文档属于事件的概率。 对于每个事件,识别客观地确定为基本上代表事件的一个或多个文档。 然后从多模态信息中提取与事件相关的一个或多个其他类型的媒体(例如,视频,图像等)。 代表性文件和其他媒体用于向故事板中的用户呈现。
    • 4. 发明授权
    • Automatic music mood detection
    • 自动音乐心情检测
    • US07396990B2
    • 2008-07-08
    • US11275100
    • 2005-12-09
    • Lie LuHong-Jiang Zhang
    • Lie LuHong-Jiang Zhang
    • G10H1/40G10H7/00G06F17/00
    • G10H1/0008G10H2210/071G10H2210/076G10H2210/081G10H2240/061G10H2240/081G10H2240/085G10H2240/091G10H2240/135G10H2240/155G10H2250/031
    • A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.
    • 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪,包括满足感,抑郁症,繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中,算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中,算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情,可用作音乐元数据,通过音乐表示和分类来管理音乐。
    • 5. 发明申请
    • Automatic Music Mood Detection
    • 自动音乐心情检测
    • US20070131096A1
    • 2007-06-14
    • US11275100
    • 2005-12-09
    • Lie LuHong-Jiang Zhang
    • Lie LuHong-Jiang Zhang
    • G10H1/40G10H7/00
    • G10H1/0008G10H2210/071G10H2210/076G10H2210/081G10H2240/061G10H2240/081G10H2240/085G10H2240/091G10H2240/135G10H2240/155G10H2250/031
    • A system and methods use music features extracted from music to detect a music mood within a hierarchical mood detection framework. A two-dimensional mood model divides music into four moods which include contentment, depression, exuberance, and anxious/frantic. A mood detection algorithm uses a hierarchical mood detection framework to determine which of the four moods is associated with a music clip based on the extracted features. In a first tier of the hierarchical detection process, the algorithm determines one of two mood groups to which the music clip belongs. In a second tier of the hierarchical detection process, the algorithm then determines which mood from within the selected mood group is the appropriate, exact mood for the music clip. Benefits of the mood detection system include automatic detection of music mood which can be used as music metadata to manage music through music representation and classification.
    • 系统和方法使用从音乐中提取的音乐特征来检测分层情绪检测框架内的音乐心情。 二维情绪模型将音乐分为四种情绪,包括满足感,抑郁症,繁荣感和焦虑/疯狂。 情绪检测算法使用分级情绪检测框架来基于提取的特征来确定四种情绪中的哪一种与音乐剪辑相关联。 在层次检测过程的第一层中,算法确定音乐剪辑所属的两个心情组之一。 在层次检测过程的第二层中,算法然后确定来自所选择的心情组中的哪个心情是音乐剪辑的适当的精确心情。 情绪检测系统的优点包括自动检测音乐心情,可用作音乐元数据,通过音乐表示和分类来管理音乐。
    • 9. 发明授权
    • Technologies for finding ringtones that match a user's hummed rendition
    • 找到匹配用户嗡嗡声演绎的铃声的技术
    • US08116746B2
    • 2012-02-14
    • US11712707
    • 2007-03-01
    • Lie LuYutao XieXing XieJiafan OuRuihao Weng
    • Lie LuYutao XieXing XieJiafan OuRuihao Weng
    • H04M3/42
    • H04M19/04G06F17/30743G06F17/30758G06F17/30864G10H2210/056G10H2230/021H04M3/02H04W4/14H04W4/16H04W88/02
    • Described is a technology by which a user hums, sings or otherwise plays a user-provided rendition of a ringtone (or ringback tone) through a mobile telephone to a ringtone search service (e.g., a WAP, interactive voice response or SMS-based search platform). The service matches features of the user's rendition against features of actual ringtones to determine one or more matching candidate ringtones for downloading. Features may include pitch contours (up or down), pitch intervals and durations of notes. Matching candidates may be ranked based on the determined similarity, possibly in conjunction with weighting criterion such as the popularity of the ringtone and/or the importance of the matched part. The candidate set may be augmented with other ringtones independent of the matching, such as the most popular ones downloaded by other users, ringtones from similar artists, and so forth.
    • 描述了一种用户通过移动电话哼唱,唱歌或以其他方式播放用户提供的铃声(或回铃音)到铃声搜索服务(例如,WAP,交互式语音响应或基于SMS的搜索)的技术 平台)。 该服务将用户的再现特征与实际铃声的特征相匹配,以确定用于下载的一个或多个匹配的候选铃声。 功能可能包括音高轮廓(上或下),音高间隔和音符持续时间。 匹配的候选者可以基于所确定的相似度来排序,可能与诸如铃声的普及度和/或匹配部分的重要性的加权标准相结合。 候选集可以用独立于匹配的其他铃声进行增强,诸如由其他用户下载的最流行的,来自类似的艺术家的铃声等等。