With advancement of various information processing and storage techniques, the scale of digital music collections has been growing at very fast speed during recent decades. To support high-quality content-based retrieval over such a large volume of music data, how to develop indexing structure with good effectiveness, efficiency and scalability becomes an important research issue. However, existing techniques mainly focus on improving query efficiency. Very few approaches have been proposed to address issues related to scalability and accuracy. In this study, we address the problem via introducing a novel indexing technique called effective music indexing framework (EMIF) to facilitate scalable and accurate music retrieval. It is designed based on a “classification-and-indexing” principle and consists of two main functionality modules: (1) music classification—a novel semantic-sensitive classification to identify an input song’s category and (2) indexing module—multiple local indexing structures, one for each semantic category to reduce query response time significantly. In particular, the classification model combining linear discriminative mixture model (LDMM) and advanced score fusion scheme has been applied to estimate category of music accurately. Layered architecture enables EMIF to enjoy superior scalability and efficiency. To evaluate the approach, a set of experimental studies has been carried out using two large music test collections and the results demonstrate various advantages of EMIF over state-of-the-art approaches including efficiency, scalability and effectiveness.

中文翻译：

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面向可扩展的基于内容的音乐检索的高效索引结构

随着各种信息处理和存储技术的进步，近几十年来数字音乐收藏的规模以非常快的速度增长。为了支持对如此大量音乐数据进行高质量的基于内容的检索，如何开发具有良好有效性、效率和可扩展性的索引结构成为一个重要的研究问题。然而，现有技术主要集中在提高查询效率上。很少有人提出解决与可扩展性和准确性相关的问题的方法。在这项研究中，我们通过引入一种称为有效音乐索引框架 (EMIF) 的新型索引技术来解决这个问题，以促进可扩展和准确的音乐检索。它基于“分类和索引”原则设计，由两个主要功能模块组成：(1) 音乐分类——一种新颖的语义敏感分类，用于识别输入歌曲的类别；(2) 索引模块——多个本地索引结构，每个语义类别一个，以显着减少查询响应时间。特别是将线性判别混合模型（LDMM）和高级分数融合方案相结合的分类模型已被应用于准确估计音乐类别。分层架构使 EMIF 享有卓越的可扩展性和效率。为了评估该方法，使用两个大型音乐测试集进行了一组实验研究，结果证明了 EMIF 相对于最先进方法的各种优势，包括效率、可扩展性和有效性。