A machine learning approach to two-voice counterpoint composition

Algorithmic composition of musical pieces is one of the most popular areas of computer aided music research. Various attempts have been made successfully in the area of music composition. Artificial intelligence methods have been extensively applied in this area. Representation of musical pieces in a computer-understandable form plays an important role in computer aided music research.This paper presents a neural network-based knowledge representation schema for representing notes, melodies, and time in first species counterpoint pieces. A musical note is composed of pitch and duration in this representation schema. The proposed representation technique was tested using the back-propagation algorithm to generate two-voice counterpoint pieces.     

Onset detection for tar solo

This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features. Therefore, it can be utilized to detect either soft or hard onsets. Through this combination, we obtained a more precise separation between two adjacent notes. This ability is especially useful to detect the reaz, repeatedly played notes with the same frequency and short durations. For the evaluation of the method, a data set with predetermined onsets was produced and the results were compared with an energy-based method explained in terms of F-measure.     

Composing by musical analog: a look at planetary orbits

Generating musical compositions using models from probability statistics, fractal geometry, and other nonlinear and chaotic systems, referred to by some composers as a mapping technique or mapping, is discussed. Composition based on celestial coordinates made audible through an interactive Pascal computer program that generates such coordinates for all the planets and the Earth's Moon as seen from any point on Earth for any given time frame is reported. The compositional environment is examined, the durational schemes used are described, and the composer's options are discussed     

A novel method to musicalize shape and visualize music and a novel technique in music cryptography

Since many years ago, musicians have composed music based on the images that they have had in their minds. On the other hand, music affects people’s imagination while hearing it. This research provides a method that can transform shape to music and music to shape. This method defines musical notations for horizontal, diagonal and vertical line segments, filled circle and curve with different colors, which are the basis of many shapes in transforming shapes into music. Then these primary mappings are generalized to more complex forms to transform any shape. Moreover, music can be transformed into shape by this method. For this transformation, primary musical notations such as simple notes, notes joined by a legato, notes with a staccato, notes joined by a legato and have crescendo or decrescendo and notes with an accent or a trill are defined. These primary musical notations are generalized to more complex forms to transform any music into shape. Also, the method of this research can be used in music cryptography. It employs mapping of notes in a twelve-tone equal musical system into shapes and mappings of shapes with an equal line width and different colors into music.

     

Has classical music a fractal nature? — A reanalysis

It has recently been proposed that classical music has a fractal nature. A reanalysis of this proposal reveals some logical flaws in the argument. Chaos, fractals, time series and Schenkerian analysis are contrasted and inter-related. Further consideration of Bach's Invention No 1 (BWV772) leads to the conclusion that there is no inherent fractal nature in classical music; although the converse is not true. In other words, it is feasible to use fractal ideas to compose musical pieces — an area of much interest in recent years.     

A Modeling of Singing Voice Robust to Accompaniment Sounds and Its Application to Singer Identification and Vocal-Timbre-Similarity-Based Music Information Retrieval

This paper describes a method of modeling the characteristics of a singing voice from polyphonic musical audio signals including sounds of various musical instruments. Because singing voices play an important role in musical pieces with vocals, such representation is useful for music information retrieval systems. The main problem in modeling the characteristics of a singing voice is the negative influences caused by accompaniment sounds. To solve this problem, we developed two methods, accompaniment sound reduction and reliable frame selection. The former makes it possible to calculate feature vectors that represent a spectral envelope of a singing voice after reducing accompaniment sounds. It first extracts the harmonic components of the predominant melody from sound mixtures and then resynthesizes the melody by using a sinusoidal model driven by these components. The latter method then estimates the reliability of frame of the obtained melody (i.e., the influence of accompaniment sound) by using two Gaussian mixture models (GMMs) for vocal and nonvocal frames to select the reliable vocal portions of musical pieces. Finally, each song is represented by its GMM consisting of the reliable frames. This new representation of the singing voice is demonstrated to improve the performance of an automatic singer identification system and to achieve an MIR system based on vocal timbre similarity.      

Performance-driven assignment and mapping for reliable networks-on-chips

Network-on-chip （NoC） communication architectures present promising solutions for scalable communication requests in large system-on-chip （SoC） designs. Intellectual property （IP） core assignment and mapping are two key steps in NoC design, significantly affecting the quality of NoC systems. Both are NP-hard problems, so it is necessary to apply intelligent algorithms. In this paper, we propose improved intelligent algorithms for NoC assignment and mapping to overcome the draw-backs of traditional intelligent algorithms. The aim of our proposed algorithms is to minimize power consumption, time, area, and load balance. This work involves multiple conflicting objectives, so we combine multiple objective optimization with intelligent algorithms. In addition, we design a fault-tolerant routing algorithm and take account of reliability using comprehensive performance indices. The proposed algorithms were implemented on embedded system synthesis benchmarks suite （E3S）. Experimental results show the improved algorithms achieve good performance in NoC designs, with high reliability.     

A unified approach to content-based and fault-tolerant music recognition

In this paper, we propose a unified approach to fast index-based music recognition. As an important area within the field of music information retrieval (MIR), the goal of music recognition is, given a database of musical pieces and a query document, to locate all occurrences of that document within the database, up to certain possible errors. In particular, the identification of the query with regard to the database becomes possible. The approach presented in this paper is based on a general algorithmic framework for searching complex patterns of objects in large databases. We describe how this approach may be applied to two important music recognition tasks: The polyphonic (musical score-based) search in polyphonic score data and the identification of pulse-code modulation audio material from a given acoustic waveform. We give an overview on the various aspects of our technology including fault-tolerant search methods. Several areas of application are suggested. We describe several prototypic systems we have developed for those applications including the notify! and the audentify! systems for score- and waveform-based music recognition, respectively.     

Evolving the process of a virtual composer

In this paper we present a genetic programming system that evolves the music composition process rather than the musical product. We model the composition process using a Turing-complete virtual register machine, which renders musical pieces. These are evaluated using a series of fitness tests, which judge their statistical similarity against a corpus of Bach keyboard exercises. We explore the space of parameters for the system, looking specifically at population size, single-versus multi-track pieces and virtual machine instruction set design. Results demonstrate that the methodology succeeds in creating pieces of music that converge towards the properties of the chosen corpus. The output pieces exhibit certain musical qualities (repetition and variation) not specifically targeted by our fitness tests, emerging solely based on the statistical similarities.

     

Use of IFS Codes for Learning 2D Isolated-Object Classification Systems

Automatic recognition of complex images is a hard and computationally expensive task, mainly because it is extremely difficult to capture in an automatic way and with a few features the necessary discriminant information. If such features were available, a proper learning system could be trained to distinguish images of different kinds of objects, starting from a set of labeled examples. In this paper we show that fractal features obtained from Iterated Function System encodings capture the kind of information that is needed by learning systems and, thus, allow the successful classification of 2-dimensional images of objects. We also present a fractal feature extraction algorithm and report the classification results obtained on two very different test-beds by applying Machine Learning techniques to sets of encoded images.     

Stream segregation algorithm for pattern matching in polyphonic music databases

As music can be represented symbolically, most of the existing methods extend some string matching algorithms to retrieve musical patterns in a music database. However, not all retrieved patterns are perceptually significant because some of them are, in fact, inaudible. Music is perceived in groupings of musical notes called streams. The process of grouping musical notes into streams is called stream segregation. Stream-crossing musical patterns are perceptually insignificant and should be pruned from the retrieval results. This can be done if all musical notes in a music database are segregated into streams and musical patterns are retrieved from the streams. Findings in auditory psychology are utilized in this paper, in which stream segregation is modelled as a clustering process and an adapted single-link clustering algorithm is proposed. Supported by experiments on real music data, streams are identified by the proposed algorithm with considerable accuracy.

Rule ordering in bottom-up fixpoint evaluation of logic programs

Logic programs can be evaluated bottom-up by repeatedly applying all rules, in “iterations”, until the fixpoint is reached. However, it is often desirable-and, in some cases, e.g. programs with stratified negation, it is even necessary to guarantee the semantics-to apply the rules in some order. We present two algorithms that apply rules in a specified order without repeating inferences. One of them (GSN) is capable of dealing with a wide range of rule orderings, but with a little more overhead than the well-known seminaive algorithm (which we call BSN). The other (PSN) handles a smaller class of rule orderings, but with no overheads beyond those in BSN. We also demonstrate that by choosing a good ordering, we can reduce the number of rule applications (and thus the number of joins). We present a theoretical analysis of rule orderings and identify orderings that minimize the number of rule applications (for all possible instances of the base relations) with respect to a class of orderings called fair orderings. We also show that though nonfair orderings may do a little better on some data sets, they can do much worse on others. The analysis is supplemented by performance results     

Creating and evaluating a particle system for music visualization

In this paper, we present a simplified 3D particle system and fast translation algorithm we have designed and implemented to generate real-time animated particle emitter fountains choreographed by a classical music. The approach we used to translate and map the controlling information into the musical fountain animation is also introduced, as well as the process to build the physical model of the music fountains. A proof of concept is implemented to demonstrate the main system's aspects, its feasibility, and that it has met the system's design goals. Moreover, it shows that is possible to observe visual patterns that match the theme of the musical composition, as an example of how the system can be used not only for visual appreciation and entertainment, but also as a possible support tool for music composition. We have also conducted a user study as an evaluation of the system. The results of this have provided us with positive and useful feedback on the effectiveness of our visual mappings as well as further directions to explore.     

Tune Retrieval in the Multimedia Library

Musical scores are traditionally retrieved by title, composer or subject classification. Just as multimedia computer systems increase the range of opportunities available for presenting musical information, so they also offer new ways of posing musically-oriented queries. This paper shows how scores can be retrieved from a database on the basis of a few notes sung or hummed into a microphone. The design of such a facility raises several interesting issues pertaining to music retrieval. We first describe an interface that transcribes acoustic input into standard music notation. We then analyze string matching requirements for ranked retrieval of music and present the results of an experiment which tests how accurately people sing well known melodies. The performance of several string matching criteria are analyzed using two folk song databases. Finally, we describe a prototype system which has been developed for retrieval of tunes from acoustic input and evaluate its performance.     

Using machine-learning methods for musical style modeling

The ability to construct a musical theory from examples presents a great intellectual challenge that, if successfully met, could foster a range of new creative applications. Inspired by this challenge, we sought to apply machine-learning methods to the problem of musical style modeling. Our work so far has produced examples of musical generation and applications to a computer-aided composition system. Machine learning consists of deriving a mathematical model, such as a set of stochastic rules, from a set of musical examples. The act of musical composition involves a highly structured mental process. Although it is complex and difficult to formalize, it is clearly far from being a random activity. Our research seeks to capture some of the regularity apparent in the composition process by using statistical and information theoretic tools to analyze musical pieces. The resulting models can be used for inference and prediction and, to a certain extent, to generate new works that imitate the style of the great masters.     

Recursive turtle programs and iterated affine transformations

We provide a formal proof of equivalence between the class of fractals created by recursive-turtle programs (RTP) and iterated affine transformations (IAT). We begin by reviewing RTP (a geometric interpretation of non-bracketed L-systems with a single production rule) and IAT (iterated function systems restricted to affine transformations). Next, we provide a simple extension to RTP that generalizes RTP from conformal transformations to arbitrary affine transformations. We then present constructive proofs of equivalence between the fractal geometry generated by RTP and IAT that yield conversion algorithms between these two methods. We conclude with possible extensions and a few open questions for future research.     

自然交互研究——笔式简谱编辑器的设计与实现

自然交互是指用户可以用自己熟悉的日常技能与计算机进行交互。中国有大量的音乐爱好者使用简谱,但是针对简谱编辑的编辑软件却相对较少,并且现有软件的交互方式不够自然,降低了用户对软件的使用效率。他们需要一种更自然、更方便使用的简谱编辑软件。该文介绍了一个基于笔式输入的简谱编辑器原型系统的设计和实现。基于对现有软件、简谱特性和可利用的新技术的综合分析,设计并实现了这个原型系统。对此原型系统进行了非正式的用户实验之后,对结果进行了分析,并提出了修改的方案。     

Scalable Load Balancing Techniques for Parallel Computers

In this paper we analyze the scalability of a number of load balancing algorithms which can be applied to problems that have the following characteristics: the work done by a processor can be partitioned into independent work pieces; the work pieces are of highly variable sizes; and it is not possible (or very difficult) to estimate the size of total work at a given processor. Such problems require a load balancing scheme that distributes the work dynamically among different processors. Our goal here is to determine the most scalable load balancing schemes for different architectures such as hypercube, mesh, and network of workstations. For each of these architectures, we establish lower bounds on the scalability of any possible load balancing scheme. We present the scalability analysis of a number of load balancing schemes that have not been analyzed before. This gives us valuable insights into their relative performance for different problem and architectural characteristics. For each of these architectures, we are able to determine near optimal load balancing schemes. Results obtained from implementation of these schemes in the context of the Tautology Verification problem on the Ncube/2 (a trademark of the Ncube Corporation) multicomputer are used to validate our theoretical results for the hypercube architecture. These results also demonstrate the accuracy and viability of our framework for scalability analysis.     

Penalty Approximation for Dynamical Systems Submitted to Multiple Non-Smooth Constraints

We consider discrete mechanical systems subject to perfect unilateral constraints. Moreau's impact law uses the decomposition of the velocity on the normaland tangent cones to the set of admissible positions at the impact point, and it iswell-known that this gives the only possible rule in the case of a singleperfect unilateral constraint. In the multi-constraint case, there areother possible energetically and geometrically consistant impact laws. Weshow here that in a number of cases, the limiting behavior of impact asits rigidity tends to infinity is given by Moreau's rule, i.e., in a more mathematical language, we justify this impact law by a penalty approach.First we describe the penalty method, then we apply it to amulti-constraint model problem. We choose an overdamped approximationand we obtain Moreau's rule for inelastic shocks in the limit. Finally,we present the computational drawbacks of the penalty method.