利用原型信息在分层组织的景观中进行大规模音乐探索

M. Schedl, Christian Höglinger, Peter Knees
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引用次数: 14

摘要

我们提出了一个新颖的用户界面,提供了一个有趣的方式来探索音乐收藏的虚拟景观在一个游戏般的方式。扩展了以前的工作,特别关注可扩展性和用户交互。在这种情况下,今天不断增长的音乐收藏规模是通过两种方式解决的,这两种方式允许可视化和浏览几乎任意大小的音乐存储库。首先,提出的用户界面deepTune采用分层版本的自组织地图(SOM),使用多个分层排列的层对相似的音乐片段进行聚类。其次,为了通过向用户呈现已知的锚点来促进在景观中的定位,提出了基于web和基于音频信号的信息提取技术的组合,以确定聚类原型(歌曲)。选择有代表性和知名的原型——前者是通过使用基于信号的特征来保证的,后者是通过使用基于web的数据来保证的——对于浏览大型音乐收藏至关重要。我们进一步报告了评估所提出的聚类原型排序质量的评估结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Large-scale music exploration in hierarchically organized landscapes using prototypicality information
We present a novel user interface that offers a fun way to explore music collections in virtual landscapes in a game-like manner. Extending previous work, special attention is paid to scalability and user interaction. In this vein, the ever growing size of today's music collections is addressed in two ways that allow for visualizing and browsing nearly arbitrarily sized music repositories. First, the proposed user interface deepTune employs a hierarchical version of the Self-Organizing Map (SOM) to cluster similar pieces of music using multiple, hierarchically aligned layers. Second, to facilitate orientation in the landscape by presenting well-known anchor points to the user, a combination of Web-based and audio signal-based information extraction techniques to determine cluster prototypes (songs) is proposed. Selecting representative and well-known prototypes -- the former is ensured by using signal-based features, the latter by using Web-based data -- is crucial for browsing large music collections. We further report on results of an evaluation carried out to assess the quality of the proposed cluster prototype ranking.
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