Analysis of words: algorithms for biological sequences, music and texts

单词分析：生物序列、音乐和文本的算法

• 批准号: RGPIN-2016-03661
• 负责人: Brown, Daniel
• 金额: $ 1.6万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678589
• 关键词: Analysis; words; algorithms; biological; sequences

We will develop new methods in three interrelated areas of computer science: bioinformatics, music information retrieval and computational creativity. All three projects will develop the skills of young Canadian researchers to work in the new economy.******For bioinformatics, our focus is on building better methods for reconstructing evolutionary trees. We have previously designed extremely fast algorithms for this problem that depend on sequences being pre-aligned, and that depend on the high quality of those alignments. In this project, we will focus on the faster, but traditionally less accurate, methods that don't start with alignments, and build a hybrid system that merges some alignment approaches with existing alignment-free methods, to join high speed and high accuracy in this important evolutionary biology task.******In music information retrieval, our focus is on using bioinformatics algorithms to enhance similarity search in symbolically-represented music. We will develop methods that look for similar motifs and common patterns, as a way of identifying musical influence and historical changes in style. Our approach, derived from DNA sequence alignment, allows us to speed up what has traditionally been a slow area of music information retrieval; we may also be able to develop algorithms that work on recorded music.******Another focus is on music lyrics, where we will continue our work on analysis of patterns: rhyme, meter, wordplay, authorial voice, and other features. Previously, we have mostly focused on rap music, but we will also see to what extent we can characterize what makes a successful lyric in other genres, such as pop music. Preliminary work has suggested that highly complex rhymes predict song success and popularity on music sites like Shazam, and we will continue to explore the power and quality of lyrics.******Finally, in computational creativity, we will build a cyborg poet that combines current event news tweets with structure and style rules of successful poetry to build a "deadline poet" that responds to the news. We will investigate which features of tweets make them more and less successful in such a poet, and also analyze which other features are necessary to separate successful poems from unsuccessful ones. Along the way, we will also critique what it means to create artistic, creative objects from human-generated ones, and what this dichotomy has to say about whether computers can be creative agents.

我们将在计算机科学的三个相互关联的领域开发新方法：生物信息学、音乐信息检索和计算创造力。这三个项目都将培养年轻的加拿大研究人员在新经济中工作的技能。对于生物信息学，我们的重点是建立更好的方法来重建进化树。我们之前已经为这个问题设计了极快的算法，这些算法依赖于被预比对的序列，并且依赖于这些比对的高质量。在这个项目中，我们将专注于更快但传统上不那么准确的方法，这些方法不是从比对开始的，并构建一个混合系统，将一些比对方法与现有的非比对方法相结合，以在这一重要的进化生物学任务中结合高速和高精度。*在音乐信息检索中，我们的重点是使用生物信息学算法来增强符号表示的音乐中的相似性搜索。我们将开发寻找相似主题和共同模式的方法，作为识别音乐影响和风格历史变化的一种方式。我们的方法源于DNA序列比对，使我们能够加快传统上缓慢的音乐信息检索领域；我们还可以开发适用于录制音乐的算法。*另一个重点是音乐歌词，在那里我们将继续分析模式：韵律、节拍、文字游戏、作者的声音和其他特征。之前，我们主要关注说唱音乐，但我们也将看看在多大程度上我们可以在其他流派中描述一首成功的歌词，比如流行音乐。初步工作表明，高度复杂的押韵预测歌曲的成功和在Shazam等音乐网站上的受欢迎程度，我们将继续探索歌词的力量和质量。*最后，在计算创造力方面，我们将建立一个机器人诗人，将时事新闻推文与成功诗歌的结构和风格规则相结合，建立一个对新闻做出回应的“截止日期诗人”。我们将调查推特的哪些特征使他们在这样一位诗人身上成功和不成功，并分析哪些其他特征是区分成功和不成功的诗歌所必需的。在此过程中，我们还将批评从人类创造的艺术、创造性对象中创造出来的意味着什么，以及关于计算机是否可以成为创造性代理人的这种二分法所说的话。