摘要翻译：
我们提出了一个sung查询错误模型，它是隐马尔可夫模型(HMM)的一个变体。该方法解决了音乐信息检索领域的一个重要问题，即音乐作品数据库中的一个（通常是充满错误的）歌曲查询与一个潜在目标之间的相似度识别问题。相似性度量是哼唱式查询(QBH)应用程序的关键组成部分，该应用程序在音频和多媒体数据库中搜索与口头查询强匹配的内容。我们的模型全面地表达了目标和查询之间的错误或变化类型：累积和非累积局部错误、转置、节奏和节奏变化、插入、删除和调制。该模型不仅具有表达能力，而且可以自动训练，或者能够从查询示例中学习和概括。我们给出了模拟的结果，旨在评估该模型的歧视潜力，并用真实的sung查询进行测试，以证明与现实世界的应用程序的相关性。
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英文标题：
《A Comprehensive Trainable Error Model for Sung Music Queries》
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作者：
W. P. Birmingham, C. J. Meek
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of query-by-humming (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications.
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PDF链接：
https://arxiv.org/pdf/1107.0054