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

我们将在计算机科学的三个相互关联的领域开发新的方法：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非常快的算法，它依赖于序列的预对齐，并且依赖于这些对齐的高质量。在这个项目中，我们将专注于更快，但传统上不太准确的方法，这些方法不是从比对开始的，并建立一个混合系统，将一些比对方法与现有的无比对方法结合起来，在这项重要的进化生物学任务中加入高速和高精度。******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音乐的相似性搜索。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；我们或许还能开发出适用于录制音乐的算法。******另一个重点是音乐歌词，我们将继续分析模式：押韵，韵律，文字游戏，作者的声音和其他特征。以前，我们主要关注说唱音乐，但我们也会看到我们在多大程度上可以描述其他类型（如流行音乐）中成功的歌词的特征。初步研究表明，高度复杂的押韵预示着歌曲在Shazam等音乐网站上的成功和受欢迎程度，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，我们将构建一个电子诗人，将时事新闻推文与成功诗歌的结构和风格规则结合起来，构建一个对新闻做出回应的“截止日期诗人”。我们将研究推文的哪些特征使其在这样一位诗人身上更成功或更不成功，并分析哪些其他特征是区分成功诗歌和不成功诗歌所必需的。在此过程中，我们还将批评从人类生成的对象中创造艺术和创造性对象意味着什么，以及这种二分法对计算机是否可以成为创造性代理的看法。

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.