Audio source segmentation using spectral correlation features for automatic indexing of broadcast news
Audio source segmentation using spectral correlation features for automatic indexing of broadcast news
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使用频谱相关特征进行音频源分割，用于广播新闻的自动索引
DOI：
10.5281/zenodo.38371
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Yoshihiko Hayashi
中科院分区：
文献类型：
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作者：
S. Matsunaga;Osamu Mizuno;K. Ohtsuki;Yoshihiko Hayashi
针对广播新闻的自动标引问题，提出了一种新的音源间隔检测方法。该过程由音频源检测部分和平滑检测序列的部分组成。检测部分使用了基于谱互相关的三个新的声学特征参数：谱稳定性、白噪声相似度和声谱形状。这些参数使得能够比使用常规参数更准确地捕获音频源。平滑部分有一种新的合并方法，它丢弃了持续时间较短的错误检测结果。对广播新闻片段进行了音频源分类实验。当使用所提出的参数时，性能提高了6.6%，而当使用所提出的合并方法时，性能提高了3.1%，表明了该方法的有效性。实验证实了该算法对广播新闻索引的影响。
This paper proposes a new segmentation procedure to detect audio source intervals for automatic indexing of broadcast news. The procedure is composed of an audio source detection part and a part that smoothes the detected sequences. The detection part uses three new acoustic feature parameters that are based on spectral cross-correlation: spectral stability, white noise similarity, and sound spectral shape. These parameters make it possible to capture the audio sources more accurately than can be done with conventional parameters. The smoothing part has a new merging method that drops erroneous detection results of short duration. Audio source classification experiments are conducted on broadcast news segments. Performance is increased by 6.6% when the proposed parameters are used and by 3.1% when the proposed merging method is used, showing the usefulness of our approach. Experiments confirm the impact of this proposal on broadcast news indexing.