The Geometry of Musical Rhythm
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Separating Point Sets in Polygonal Environments
Separating point sets in polygonal environments Erik D. Demaine Jeff Erickson MIT Laboratory Department of Computer Science for Computer Science U. of Illinois at Urbana-Champaign [email protected] jeff[email protected] Ferran Hurtado∗ John Iacono Departement de Matem`atica Aplicada II Dept. of Computer and Information Science Universitat Polit`ecnica de Catalunya Polytechnic University [email protected] [email protected] Stefan Langerman† Henk Meijer D´epartement d’Informatique School of Computing Universit´eLibre de Bruxelles Queen’s University [email protected] [email protected] Mark Overmars Sue Whitesides Institute of Information and Computing Sciences School of Computer Science Utrecht University McGill University [email protected] [email protected] ABSTRACT 1 Introduction We consider the separability of two point sets inside a poly- Let us consider the following basic question: given two point gon by means of chords or geodesic lines. Specifically, given sets R and B in the interior of a polygon (the red points and a set of red points and a set of blue points in the interior of the blue points, respectively), is there a chord separating R a polygon, we provide necessary and sufficient conditions for from B? This is the starting problem we study in this paper, the existence of a chord and for the existence of a geodesic where we also consider several related problems. path which separate the two sets; when they exist we also Problems on separability of point sets and other geomet- derive efficient algorithms for their obtention. We study as ric objects have generated a significant body of research in well the separation of the two sets using a minimum number computational geometry, and many kind of separators have of pairwise non-crossing chords. -
The Rhythm That Conquered the World: What Makes a “Good” Rhythm Good?
The Rhythm that Conquered the World: What Makes a “Good” Rhythm Good? (to appear in Percussive Notes.) By Godfried T. Toussaint Radcliffe Institute for Advanced Study Harvard University Dedicated to the memory of Bo Diddley. THE BO DIDDLEY BEAT Much of the world’s traditional and contemporary music makes use of characteristic rhythms called timelines. A timeline is a distinguished rhythmic ostinato, a rhythm that repeats throughout a piece of music with no variation, that gives the particular flavor of movement of the piece that incorporates it, and that acts as a timekeeper and structuring device for the musicians. Timelines may be played with any musical instrument, although a percussion instrument is usually preferred. Timelines may be clapped with the hands, as in the flamenco music of Southern Spain, they may be slapped on the thighs as was done by Buddy Holly’s drummer Jerry Allison in the hit song “Everyday”, or they may just be felt rather than sounded. In Sub-Saharan African music, timelines are usually played using an iron bell such as the gankogui or with two metal blades. Perhaps the most quintessential timeline is what most people familiar with rockabilly music dub the Bo Diddley Beat, and salsa dancers call the clave son, which they attribute to much other Cuban and Latin American music. This rhythm is illustrated in box notation in Figure 1. Each box represents a pulse, and the duration between any two adjacent pulses is one unit of time. There are a total of sixteen pulses, the first pulse occurs at time zero and the sixteenth at time 15. -
Efficiently Computing All Delaunay Triangles Occurring Over
Efficiently Computing All Delaunay Triangles Occurring over All Contiguous Subsequences Stefan Funke Universität Stuttgart, Germany [email protected] Felix Weitbrecht Universität Stuttgart, Germany [email protected] Abstract Given an ordered sequence of points P = {p1, p2, . , pn}, we are interested in computing T , the set of distinct triangles occurring over all Delaunay triangulations of contiguous subsequences within P . We present a deterministic algorithm for this purpose with near-optimal time complexity O(|T | log n). Additionally, we prove that for an arbitrary point set in random order, the expected number of Delaunay triangles occurring over all contiguous subsequences is Θ(n log n). 2012 ACM Subject Classification Theory of computation → Computational geometry Keywords and phrases Computational Geometry, Delaunay Triangulation, Randomized Analysis Digital Object Identifier 10.4230/LIPIcs.ISAAC.2020.28 1 Introduction For an ordered sequence of points P = {p1, p2, . , pn}, we consider for 1 ≤ i < j ≤ n the contiguous subsequences Pi,j := {pi, pi+1, . , pj} and their Delaunay triangulations S Ti,j := DT (Pi,j). We are interested in the set T := i<j{t | t ∈ Ti,j} of distinct Delaunay triangles occurring over all contiguous subsequences. Figure 1 shows a sequence of points 2 n p1, . , pn where |T | = Ω(n ) (collinearities could be perturbed away). For j > 2 , any point p will be connected to all points {p , . , p n } in T , so any such T contains Θ(n) j 1 2 1,j 1,j 0 2 Delaunay triangles not contained in any T1,j0 with j < j, hence |T | = Ω(n ). -
Experimental Results on Quadrangulations of Sets of Fixed Points
Experimental Results on Quadrangulations of Sets of Fixed Points Prosenjit Bose∗ Suneeta Ramaswamiy Godfried Toussaintz Alain Turkix Abstract We consider the problem of obtaining \nice" quadrangulations of planar sets of points. For many applications \nice" means that the quadrilaterals obtained are convex if possible and as \fat" or as squarish as possible. For a given set of points a quadrangulation, if it exists, may not admit all its quadrilaterals to be convex. In such cases we desire that the quadrangulations have as many convex quadrangles as possible. Solving this problem optimally is not practical. Therefore we propose and experimentally investigate a heuristic approach to solve this problem by converting \nice" triangulations to the desired quadrangulations with the aid of maximum matchings computed on the dual graph of the triangulations. We report experiments on several versions of this approach and provide theoretical justification for the good results obtained with one of these methods. The results of our experiments are particularly relevant for those applications in scattered data interpolation which require quadrangulations that should stay faithful to the original data. 1 Introduction Finite element mesh generation is a problem that has received considerable attention in recent years, due to its numerous applications in a number of areas such as medical imaging, computer graphics, and Geographic Information Systems (GIS). Important applications also arise in the manufacturing industry, where a central problem is the simulation of processes such as fluid flow in injection molding, by solving systems of partial differential equations [11]. To make such tasks easier, the classical approach is to use the method of finite elements, where the bounding surface of the relevant object is partitioned into small pieces by using data points sampled on the object surface. -
Computational Geometric Aspects of Rhythm, Melody, and Voice-Leading
Computational Geometry 43 (2010) 2–22 Contents lists available at ScienceDirect Computational Geometry: Theory and Applications www.elsevier.com/locate/comgeo Computational geometric aspects of rhythm, melody, and voice-leading Godfried Toussaint 1 School of Computer Science and Center for Interdisciplinary Research in Music Media and Technology, McGill University, Montréal, Québec, Canada article info abstract Article history: Many problems concerning the theory and technology of rhythm, melody, and voice- Received 1 February 2006 leading are fundamentally geometric in nature. It is therefore not surprising that the Accepted 1 January 2007 field of computational geometry can contribute greatly to these problems. The interaction Available online 21 March 2009 between computational geometry and music yields new insights into the theories of Communicated by J. Iacono rhythm, melody, and voice-leading, as well as new problems for research in several areas, Keywords: ranging from mathematics and computer science to music theory, music perception, and Musical rhythm musicology. Recent results on the geometric and computational aspects of rhythm, melody, Melody and voice-leading are reviewed, connections to established areas of computer science, Voice-leading mathematics, statistics, computational biology, and crystallography are pointed out, and Evenness measures new open problems are proposed. Rhythm similarity © 2009 Elsevier B.V. All rights reserved. Sequence comparison Necklaces Convolution Computational geometry Music information retrieval Algorithms Computational music theory 1. Introduction Imagine a clock which has 16 hours marked on its face instead of the usual 12. Assume that the hour and minute hands have been broken off so that only the second-hand remains. Furthermore assume that this clock is running fast so that the second-hand makes a full turn in about 2 seconds. -
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search
A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search Mengzhao Wang1, Xiaoliang Xu1, Qiang Yue1, Yuxiang Wang1,∗ 1Hangzhou Dianzi University, China {mzwang,xxl,yq,lsswyx}@hdu.edu.cn 1 ABSTRACT result vertex Approximate nearest neighbor search (ANNS) constitutes an im- 2 query query 4 portant operation in a multitude of applications, including recom- seed vertex 3 mendation systems, information retrieval, and pattern recognition. (a) Original dataset (b) Graph index In the past decade, graph-based ANNS algorithms have been the Figure 1: A toy example for the graph-based ANNS algorithm. leading paradigm in this domain, with dozens of graph-based ANNS algorithms proposed. Such algorithms aim to provide effective, ef- actual requirements for efficiency and cost61 [ ]. Thus, much of ficient solutions for retrieving the nearest neighbors for agiven the literature has focused on efforts to research approximate NNS query. Nevertheless, these efforts focus on developing and optimiz- (ANNS) and find an algorithm that improves efficiency substantially ing algorithms with different approaches, so there is a real need while mildly relaxing accuracy constraints (an accuracy-versus- for a comprehensive survey about the approaches’ relative perfor- efficiency tradeoff [59]). mance, strengths, and pitfalls. Thus here we provide a thorough ANNS is a task that finds the approximate nearest neighbors comparative analysis and experimental evaluation of 13 represen- among a high-dimensional dataset for a query via a well-designed tative graph-based ANNS algorithms via a new taxonomy and index. According to the index adopted, the existing ANNS algo- fine-grained pipeline. We compared each algorithm in a uniform rithms can be divided into four major types: hashing-based [40, 45]; test environment on eight real-world datasets and 12 synthetic tree-based [8, 86]; quantization-based [52, 74]; and graph-based [38, datasets with varying sizes and characteristics. -
Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries∗
Output-Sensitive Algorithms for Computing Nearest-Neighbour Decision Boundaries∗ David Bremner† Erik Demaine‡ Jeff Erickson§ John Iacono¶ Stefan Langermank Pat Morin∗∗ Godfried Toussaint†† ABSTRACT. Given a set R of red points and a set B of blue points, the nearest-neighbour decision rule classifies a new point q as red (respectively, blue) if the closest point to q in R ∪ B comes from R (respectively, B). This rule implicitly partitions space into a red set and a blue set that are separated by a red-blue decision boundary. In this paper we develop output-sensitive algorithms for computing this decision boundary for point sets on the line and in R2. Both algorithms run in time O(n log k), where k is the number of points that contribute to the decision boundary. This running time is the best possible when parameterizing with respect to n and k. 1 Introduction Let S be a set of n points in the plane that is partitioned into a set of red points denoted by R and a set of blue points denoted by B. The nearest-neighbour decision rule classifies a new point q as the color of the closest point to q in S. The nearest-neighbour decision rule is popular in pattern recognition as a means of learning by example. For this reason, the set S is often referred to as a training set. Several properties make the nearest-neighbour decision rule quite attractive, including its intu- itive simplicity and the theorem that the asymptotic error rate of the nearest-neighbour rule is bounded from above by twice the Bayes error rate [6, 8, 15]. -
Geometric Decision Rules for High Dimensions
Geometric Decision Rules for High Dimensions Binay Bhattacharya ∗ Kaustav Mukherjee School of Computing Science School of Computing Science Simon Fraser University Simon Fraser University Godfried Toussaint y School of Computer Science McGill University Abstract In this paper we report on a new approach to the instance-based learning problem. The new approach combines five tools: first, editing the data using Wilson-Gabriel- editing to smooth the decision boundary, second, applying Gabriel-thinning to the edited set, third, filtering this output with the ICF algorithm of Brighton and Mellish, fourth, using the Gabriel-neighbor decision rule to clasify new incoming queries, and fifth, using a new data structure that allows the efficient computation of approximate Gabriel graphs in high dimensional spaces. Extensive experiments suggest that our approach is the best on the market. 1 Introduction In the typical non-parametric classification problem (see Devroye, Gyorfy and Lugosi [3]) we have available a set of d measurements or observations (also called a feature vector) taken from each member of a data set of n objects (patterns) denoted by fX; Y g = f(X1; Y1); :::; (Xn; Yn)g, where Xi and Yi denote, respectively, the feature vector on the ith object and the class label of that object. One of the most attractive decision procedures, conceived by Fix and Hodges in 1951, is the nearest-neighbor rule (1-NN-rule). Let Z be a new pattern (feature vector) to be classified and let Xj be the feature vector in fX; Y g = f(X1; Y1); :::; (Xn; Yn)g closest to Z. The nearest neighbor decision rule classifies the unknown pattern Z into class Yj.