翻唱网络中社区结构的刻画与开发
Characterization and exploitation of community structure in cover song networks
摘要：
社区发现算法的使用是在翻唱歌曲识别的框架内探索的，即，同一基础音乐作品的不同音频再现的自动检测。到目前为止，这个任务一直被认为是一个典型的按示例查询的任务，其中一个人提交一首查询歌曲，系统检索一个可能的匹配列表，并根据它们与查询的相似性进行排序。在这项工作中，我们提出了一种新的方法，它使用歌曲社区提供更多的相关答案给一个给定的查询。从最先进的系统的输出开始，歌曲被嵌入到一个复杂的加权网络中，该网络的链接表示相似性（相关的音乐内容）。然后，网络内的社区被识别为覆盖物组，并且该信息用于增强系统的结果。特别是，我们表明，这种方法增加了系统的一致性和准确性。此外，我们提供了深入了解个人翻唱歌曲社区的内部组织，显示有一种趋势，为原来的歌曲是中央的社会。我们假设，这里提出的方法和结果可能与其他查询的例子任务。
英文摘要：
The use of community detection algorithms is explored within the framework of cover song identification, i.e. the automatic detection of different audio renditions of the same underlying musical piece. Until now, this task has been posed as a typical query-by-example task, where one submits a query song and the system retrieves a list of possible matches ranked by their similarity to the query. In this work, we propose a new approach which uses song communities to provide more relevant answers to a given query. Starting from the output of a state-of-the-art system, songs are embedded in a complex weighted network whose links represent similarity (related musical content). Communities inside the network are then recognized as groups of covers and this information is used to enhance the results of the system. In particular, we show that this approach increases both the coherence and the accuracy of the system. Furthermore, we provide insight into the internal organization of individual cover song communities, showing that there is a tendency for the original song to be central within the community. We postulate that the methods and results presented here could be relevant to other query-by-example tasks.
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