协同声音采集中的搜索结果聚类
Search Result Clustering in Collaborative Sound Collections
摘要：
当今在线多媒体数据库的庞大规模使得检索其内容成为一项困难且耗时的任务。在线声音收藏的用户通常提交表达广泛意图的搜索查询，通常使系统返回大型且难以管理的结果集。搜索结果聚类是一种将搜索结果内容组织成连贯组的技术，它允许用户在其结果中识别有用的子集。获得一致的和独特的集群，可以探索一个合适的接口是至关重要的，使这项技术的一个有用的补充，传统的搜索引擎。在我们的工作中，我们提出了一个基于图的方法，使用音频功能的聚类不同的声音集合时，查询大型在线数据库。我们提出了一种方法来评估不同的功能在规模上的性能，通过利用与每个声音相关联的元数据。这种分析与使用手动注释数据集的地面实况标签的评估相补充。我们表明，使用置信度的措施，丢弃不一致的集群提高了分区的质量。在确定了最合适的聚类功能后，我们进行了一个实验，用户执行一个健全的设计任务，以评估我们的方法和它的用户界面。定性分析包括可用性问卷调查和半结构化访谈。这为我们提供了有价值的新的见解，促进有效的互动与集群的功能。
英文摘要：
The large size of nowadays' online multimedia databases makes retrieving their content a difficult and time-consuming task. Users of online sound collections typically submit search queries that express a broad intent, often making the system return large and unmanageable result sets. Search Result Clustering is a technique that organises search-result content into coherent groups, which allows users to identify useful subsets in their results. Obtaining coherent and distinctive clusters that can be explored with a suitable interface is crucial for making this technique a useful complement of traditional search engines. In our work, we propose a graph-based approach using audio features for clustering diverse sound collections obtained when querying large online databases. We propose an approach to assess the performance of different features at scale, by taking advantage of the metadata associated with each sound. This analysis is complemented with an evaluation using ground-truth labels from manually annotated datasets. We show that using a confidence measure for discarding inconsistent clusters improves the quality of the partitions. After identifying the most appropriate features for clustering, we conduct an experiment with users performing a sound design task, in order to evaluate our approach and its user interface. A qualitative analysis is carried out including usability questionnaires and semi-structured interviews. This provides us with valuable new insights regarding the features that promote efficient interaction with the clusters.
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。
