Chants and Orcas: semi-automatic tools for audio annotation and analysis in niche domains

MS Pub Date : 2008-10-31 DOI:10.1145/1460676.1460680
S. Ness, Matthew Wright, L. Martins, G. Tzanetakis
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引用次数: 5

Abstract

The recent explosion of web-based collaborative applications in business and social media sites demonstrated the power of collaborative internet scale software. This includes the ability to access huge datasets, the ability to quickly update software, and the ability to let people around the world collaborate seamlessly. Multimedia learning techniques have the potential to make unstructured multimedia data accessible, reusable, searchable, and manageable. We present two different web-based collaborative projects: Cantillion, and the Orchive. Cantillion enables ethnomusicology scholars to listen and view data relating to chants from a variety of traditions, letting them view and interact with various pitch contour representations of the chant. The Orchive is a project to digitize over 20,000 hours of Orcinus orca (killer whale) vocalizations, recorded over a period of approximately 35 years, and provide tools to assist their study. The developed tools utilize ideas and techniques that are similar to the ones used in general multimedia domains such as sports video or news. However, their niche nature has presented us with special challenges as well as opportunities. Unlike more traditional domains where there are clearly defined objectives one of the biggest challenges has been the desire to support researchers to formulate questions and problems related to the data even when there is no clearly defined objective.
圣歌和虎鲸:在小众领域用于音频注释和分析的半自动工具
最近在商业和社交媒体网站上基于web的协作应用程序的爆炸式增长,展示了协作互联网规模软件的力量。这包括访问庞大数据集的能力,快速更新软件的能力,以及让世界各地的人们无缝协作的能力。多媒体学习技术具有使非结构化多媒体数据可访问、可重用、可搜索和可管理的潜力。我们展示了两个不同的基于网络的合作项目:Cantillion和the Orchive。Cantillion使民族音乐学学者能够从各种传统中收听和查看与圣歌有关的数据,使他们能够查看圣歌的各种音高轮廓表示并与之互动。“兰花”是一个将超过2万小时的虎鲸(逆戟鲸)声音数字化的项目,这些声音记录了大约35年的时间,并为他们的研究提供工具。开发的工具利用的思想和技术类似于一般多媒体领域(如体育视频或新闻)中使用的思想和技术。然而,它们的小众性给我们带来了特殊的挑战和机遇。与有明确目标的传统领域不同,最大的挑战之一是支持研究人员在没有明确目标的情况下提出与数据相关的问题和问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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