Hierarchical music representation for composition and analysis

In this paper, we present intermediate results of continuing research into the utility of generalised hierarchical structures for the representation of musical information. We build on an abstract data type presented in Wigginset al. (1989), usingconstituents, which are structurally significant groupings of musical events. We suggest that a division into such groupings can be musically meaningful, and that it can be more flexible than similar approaches. We demonstrate our representation system at work in both analysis and composition, with output from computer programs. We conclude that it is possible and useful to represent music in a way independent of the particular style, tonal system, etc., of the music itself.The authors work in the Department of Artificial Intelligence, University of Edinburgh, Scotland.     

Music composition by interaction between human and computer

This paper aims at constructing a music composition system that composes music by the interaction between human and a computer. Even users without special musical knowledge can compose 16-bar musical works with one melody part and some backing parts using this system. The interactive Genetic Algorithm is introduced to music composition so that users’ feeling toward music is reflected in the composed music. One chromosome corresponds to 4-bar musical work information. Users participate in music composition by evaluating composed works after GA operators such as crossover, mutation, virus infection are applied to chromosomes based on the evaluation results. From the experimental results, it is found that the users’ evaluation values become high over the progress of generations. That is, the system can compose 16-bar musical works reflecting users’ feeling. Muneyuki Unehara: He received his M.S. in Engineering in 2002 from Institute of Science and Engineering, University of Tsukuba. Currently, he is a Ph.D. candidate of Graduate School of Systems and Information Engineering, University of Tsukuba. His research interests include the construction of intelligent systems by considering soft computing techniques and human interface. Takehisa Onisawa, Ph.D.: He received Dr.Eng. in Systems Science in 1986 from Tokyo Institute of Technology. Currently, he is a Professor in the Graduate School of Systems and Information Engineering, University of Tsukuba. His research interests include applications of soft computing techniques to human centered systems thinking. He is a member of IEEE and IFSA.     

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.     

Seven problems that keep MIR from attracting the interest of cognition and neuroscience

Despite one and a half decade of research and an impressive body of knowledge on how to represent and process musical audio signals, the discipline of Music Information Retrieval still does not enjoy broad recognition outside of computer science. In music cognition and neuroscience in particular, where MIR’s contribution could be most needed, MIR technologies are scarcely ever utilized—when they’re not simply brushed aside as irrelevant. This, we contend here, is the result of a series of misunderstandings between the two fields, about deeply different methodologies and assumptions that are not often made explicit. A collaboration between a MIR researcher and a music psychologist, this article attempts to clarify some of these assumptions, and offers some suggestions on how to adapt some of MIR’s most emblematic signal processing paradigms, evaluation procedures and application scenarios to the new challenges brought forth by the natural sciences of music.     

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.

On the use of a multimedia platform for music education with handicapped children: A case study

The use of Information and Communication Technologies (ICT) for students with special learning needs is being particularly studied by the research community. Likewise, music has gained recognition through the years in the field of learning disabilities, playing lately a key role in providing a better quality of life to people with special needs. However, there is still a gap on the combination of ICT and music teaching tools. In this work, we present the results of a case study where students with disabilities took a 3-month introductory music course using the multimedia tool called PLAIME (PLAtform for the Integration of handicapped children in Music Education) developed by the same authors. The aims of this work are three-fold: i) to explore the capability of disabled students to acquire musical knowledge using PLAIME, ii) to measure the improvement in their ICT skills, and iii) to observe their behavior along the study. At the end of the program, students showed an advance in their music knowledge and a positive improvement in behavioral development, being able to perfectly manage the computer platform.     

音乐可视化及其研究进展

随着计算机技术的发展,音乐的形象化出现了新的手段,这就是音乐可视化技术.作为听觉艺术与视觉艺术的结合体,音乐可视化已经得到了广泛关注.文章介绍了音乐可视化的概念与研究内容,从可视化音乐模型、可视化视觉模型、可视化系统、可视化的交互性四个层次着重分析和评述了目前国内外音乐可视化技术的研究进展,并分析了当前亟待解决的问题,展望了其未来的发展趋势.音乐可视化作为一种新型的可视化技术,仍存在许多问题需要解决,其研究具有很强的理论意义和实用价值.     

NetNeg: A connectionist-agent integrated system for representing musical knowledge

The system presented here shows the feasibility of modeling the knowledge involved in a complex musical activity by integrating sub-symbolic and symbolic processes. This research focuses on the question of whether there is any advantage in integrating a neural network together with a distributed artificial intelligence approach within the music domain. The primary purpose of our work is to design a model that describes the different aspects a user might be interested in considering when involved in a musical activity. The approach we suggest in this work enables the musician to encode his knowledge, intuitions, and aesthetic taste into different modules. The system captures these aspects by computing and applying three distinct functions: rules, fuzzy concepts, and learning. As a case study, we began experimenting with first species two-part counterpoint melodies. We have developed a hybrid system composed of a connectionist module and an agent-based module to combine the sub-symbolic and symbolic levels to achieve this task. The technique presented here to represent musical knowledge constitutes a new approach for composing polyphonic music. This revised version was published online in June 2006 with corrections to the Cover Date.     

移动机器人环境认知理论与技术的研究

未知环境中的移动机器人只具有较少的先验知识,因此对环境的认知是实现环境建模、定位、规划、行动等自主导航控制的基本前提.移动机器人的认知理论与方法研究涉及计算机科学、人工智能!认知心理学、神经学、仿生学等领域,是新兴的交叉学科---认知科学的一项重要前沿研究项目.本文介绍了移动机器人的环境感知技术与理论研究的现状,并从认知理论与方法出发,总结了有待进一步研究的相关问题.􀁱     

Melodic analysis with segment classes

This paper presents a representation for melodic segment classes and applies it to music data mining. Melody is modeled as a sequence of segments, each segment being a sequence of notes. These segments are assigned to classes through a knowledge representation scheme which allows the flexible construction of abstract views of the music surface. The representation is applied to sequential pattern discovery and to the statistical modeling of musical style. Editor: Gerhard Widmer     

A comparison of connectionist models of music recognition and human performance

Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed.     

The design and implementation of a digital music library

The design and implementation of Harbin Institute of Technology—Digital Music Library (HIT-DML) is presented in this paper. Firstly, a novel framework, a music data model, and a query language are proposed as the theoretical foundation of the library. Secondly, music computing algorithms used in the library for feature extracting and matching are described. In addition, indices are introduced for both mining themes of music objects and accelerating content-based information retrieval. Finally, experimental results on the indices and the current development of the library are provided. HIT-DML is distinguished by the following points. First, it is inherently based on database systems, and combines database technologies with multimedia technologies seamlessly. Musical data are structurally stored. Second, it has a solid theoretical foundation, from framework and data model to query language. Last, it can retrieve musical information based on content against different kinds of musical instruments. The indices used, also power the library.     

音乐可视化研究综述

随着计算机技术的发展,音乐可视化日益得到研究机构和公司的重视,出现了各种音乐可视化系统。作为一种新的可视化技术,音乐可视化更多地关注音频和图形、图像的关系,是跨音乐、数字音频、图形学、图像处理、虚拟现实等领域的又一新的研究方向,在娱乐、教育、艺术和商业中具有广阔的应用前景。本文介绍了音乐可视化的概念和分类,探讨了音乐可视化的研究内容与国内外研究进展,分析了当前亟待解决的问题,并展望了未来的发展趋势。音乐可视化是一种新的概念系统,但也存在很多问题需要解决,其研究具有很强的理论和实用意义。     

Music Genre Classification Using DenseNet and Data Augmentation

It can be said that the automatic classification of musical genres plays a very important role in the current digital technology world in which the creation, distribution, and enjoyment of musical works have undergone huge changes. As the number of music products increases daily and the music genres are extremely rich, storing, classifying, and searching these works manually becomes difficult, if not impossible. Automatic classification of musical genres will contribute to making this possible. The research presented in this paper proposes an appropriate deep learning model along with an effective data augmentation method to achieve high classification accuracy for music genre classification using Small Free Music Archive (FMA) data set. For Small FMA, it is more efficient to augment the data by generating an echo rather than pitch shifting. The research results show that the DenseNet121 model and data augmentation methods, such as noise addition and echo generation, have a classification accuracy of 98.97% for the Small FMA data set, while this data set lowered the sampling frequency to 16000 Hz. The classification accuracy of this study outperforms that of the majority of the previous results on the same Small FMA data set.     

A survey of music emotion recognition

Music is the language of emotions. In recent years, music emotion recognition has attracted widespread attention in the academic and industrial community since it can be widely used in fields like recommendation systems, automatic music composing, psychotherapy, music visualization, and so on. Especially with the rapid development of artificial intelligence, deep learning-based music emotion recognition is gradually becoming mainstream. This paper gives a detailed survey of music emotion recognition. Starting with some preliminary knowledge of music emotion recognition, this paper first introduces some commonly used evaluation metrics. Then a three-part research framework is put forward. Based on this three-part research framework, the knowledge and algorithms involved in each part are introduced with detailed analysis, including some commonly used datasets, emotion models, feature extraction, and emotion recognition algorithms. After that, the challenging problems and development trends of music emotion recognition technology are proposed, and finally, the whole paper is summarized.     

音乐领域本体的建立和分析

音乐是一门重要的社会学科，具有丰富的领域知识，这些知识是计算机音乐应用系统所需要的专业知识。本文基于本体论方法形式化地探讨音乐领域本体的建立和分析，具体介绍了本体的建立方法、本体元素的描述、本体的一致性分析等方面内容。我们给出两个判断类间关系一致性的算法并分析其性质，还探讨了基于公理的知识的一致性。     

Music, Sociality, and Digital Technology

Digital technology can be used to amplify the wonderful benefits of music. Modern digital technology has great potential to boost music education, especially in very early education. Music drill and practice by computer must always be a background to social musical activity arranged by educators and parents. The great benefit of the computer is that it can impart basic music skill and knowledge that present-day mentors often no longer have because it has faded so much from school and society.     

A general framework for reasoning about change

The capability to represent and use concepts like time and events in computer science is essential to solve a wide class of problems characterized by the notion of change. Real-time, databases and multimedia are just a few of several areas which needs good tools to deal with time. Another area where this concepts are essential is artificial intelligence because an agent must be able to reason about a dynamic environment. In this work a formalism is proposed which allows the representation and use of several features that had been recognized as useful in the attempts to solve such class of problems. A general framework based on a many-sorted logic is proposed centering our attention in issues such as the representation of time, actions, properties, events and causality. The proposal is compared with related work from the temporal logic and artificial intelligence areas. This work complements and enhances previously related efforts on formalizing temporal concepts with the same purpose. Juan Carlos Augusto, Ph.D.: He is a Lecturer in the Department of Computer Science at Universidad Nacional del Sur (Argentina), where he graduated as Licenciado en Ciencias de la Computacion and Doctor en Ciencias de la Computacion. Currently on leave in the Department of Electronics and Computer Science, University of Southampton (United Kingdom). His research interests are focused in the dynamic aspects of computing systems. This involves solving conceptual problems related to the specification of time and change and designing tools to improve systems in several areas of computer science, such as artificial intelligence, databases, multimedia, software verification and real-time systems. He has been conducting research on temporal representation and reasoning since 1993. Throughout these years he had the opportunity to contribute to several research projects as a researcher and has head or co-head of research groups. Other activities and contributions to highlight are the organization of international events, editorial work and supervision of postgraduate students, all of which contributes to the generation and dissemination of knowledge about the dynamic aspects of computing systems.     

人工鱼的认知建模方法研究

“人工鱼”利用人工生命方法创作计算机动画。为了进一步提高动画角色的智能水平,将人工智能方法学引入到“人工鱼”系统中,建立认知模型,控制人工动物的行为,使人工动物成为更加自主的和智能的角色。该文介绍了认知建模的概念、特点,给出人工鱼的认知建模方法研究的主要内容和模型结构。