广播媒体类型和节目的自动识别
Automatic Genre and Show Identification of Broadcast Media
摘要：
每天都有大量的数字视频被制作和播放，导致了巨大的媒体档案。需要有效的技术来进一步提供这类数据。广播媒体的自动元数据标记是多媒体索引的一项基本任务，在多媒体索引中，使用多模态输入是标准的。本文描述了一种使用声学特征、文本特征或其组合来自动检测媒体类型和节目身份的新方法。此外，包括可用的元数据，如时间的广播，显示导致非常高的性能。潜在狄利克雷分配被用于对声学和文本两者进行建模，从而产生媒体记录的固定维度表示，然后可以将其用于基于支持向量机的分类。实验进行了超过1200小时的电视广播从英国广播公司（BBC），在那里的任务是将广播分为8个流派或133个节目的身份。在200小时的测试集上，使用声学和文本特征与元数据相结合，分别实现了98.6%和85.7%的准确性。
英文摘要：
Huge amounts of digital videos are being produced and broadcast every day, leading to giant media archives. Effective techniques are needed to make such data accessible further. Automatic meta-data labelling of broadcast media is an essential task for multimedia indexing, where it is standard to use multi-modal input for such purposes. This paper describes a novel method for automatic detection of media genre and show identities using acoustic features, textual features or a combination thereof. Furthermore the inclusion of available meta-data, such as time of broadcast, is shown to lead to very high performance. Latent Dirichlet Allocation is used to model both acoustics and text, yielding fixed dimensional representations of media recordings that can then be used in Support Vector Machines based classification. Experiments are conducted on more than 1200 hours of TV broadcasts from the British Broadcasting Corporation (BBC), where the task is to categorise the broadcasts into 8 genres or 133 show identities. On a 200-hour test set, accuracies of 98.6% and 85.7% were achieved for genre and show identification respectively, using a combination of acoustic and textual features with meta-data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
