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Shen J., Shepherd J., Cui B., Liu L. Intelligent Music Information Systems. Tools and Methodologies

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Shen J., Shepherd J., Cui B., Liu L. Intelligent Music Information Systems. Tools and Methodologies
Information Science Reference, 2008. — 380 p.
As an important form of human expression and creativity, music data has permeated into every corner of our daily life. At the beginning of 21st century, empowered by advances in networking, data compression and physical storage, modern information systems deal with ever-increasing amounts of musical data. However, effective searching and retrieving continue to be one of the most challenging research problems. The development of new technology to facilitate music information retrieval and management has gained considerable momentum. However, relatively less attention has been paid in this field. In comparison with data from other application domains, music information enjoys many unique characteristics and the most important ones are:
Rich semantics: Music data contains a large amount of information relative to its high-level semantic meaning. On the other hand, most user queries are semantically-based (e.g., find all music items with guitar and drums performed by Michael Jackson). How to generate a concise comprehensive representation for such information is an important but challenging problem. Any useful solution must be efficient in term of computation cost and effective in calculating a content descriptor that represents the semantics of the data item.
Large volume: In music applications, the size of data is huge (each item is much larger than a tuple in a conventional (relational) data repository). Dealing with such data items requires large amounts of computer resources such as storage and data processing power. A typical example is that audio and video data may exceed gigabytes on personal computers. Storage and data management solutions provided by state-of-art relational database management systems (DBMSs) are generally not adequate in such cases. New techniques for managing such large data sets need to be developed to provide economic and effective access and management.
High dimensionality: Representations of low-level acoustic features are high-dimensional in nature. Typical examples are the feature vectors for properties such as pitch and timbre extracted from raw audio data. In extreme cases, it could require thousands of dimensions to represent a particular feature, and dozens of dimensions are typical. It is extremely difficult for the current techniques to deal efficiently with such kinds of data. As a consequence, dimensionality reduction becomes an important technique in dealing with music data. However, it is important that the reduction does not lose useful discriminative information for indexing and classification.
Complex structure: Music can be treated as a nonlinear composite of various kinds of characteristics from different sources. Applying traditional solutions developed for the extraction of knowledge and querying results from structured data (e.g., tabular data) is not feasible in this case. Further, it appears that knowledge discovery and retrieval in music data cannot be simply based on the concatenation of the partial information obtained from each part of the target object. Therefore, developing multimodal techniques to integrate different kinds of information seamlessly is essential for effective knowledge discovery and information retrieval.
All of these musical characteristics make information retrieval, knowledge discovery and content management on music data challenging. Indeed, modern information technologies lag far behind in their support for efficiently accessing and managing music data. The main objective is to assemble together, in a single volume, contributions on the topic of modern music information retrieval and management, including tools, methodologies, theory and frameworks. The book will present and provide insights into both the state-of-the-art music information retrieval issues and techniques and future trends in the field. It will also serve as a useful guide for researchers, practitioners, developers and graduate students who are interested or involved in the design, state-of-the-art development, and deployment of in music retrieval, music data management, music knowledge discovery and other related applications.
Section I Indexing and Retrieving Music Database
Content-Based Indexing of Symbolic Music Documents
MARSYAS-0.2: A Case Study in Implementing Music Information Retrieval Systems
Melodic Query Input for Music Information Retrieval Systems
Section II Music Identification and Recognition
An Expert System Devoted to Automated Music Identification and Recognition
Identifying Saxophonists from Their Playing Styles
Tools for Music Information Retrieval and Playing
Section III P2P and Distributed System
Collaborative Use of Features in a Distributed System for the Organization of Music Collections
A P2P Based Secure Digital Music Distribution Channel: The Next Generation
Music Information Retrieval in P2P Networks
DART: A Framework for Distributed Audio Analysis and Music Information Retrieval
Section IV Music Analysis
Motivic Pattern Extraction in Symbolic Domain
Pen-Based Interaction for Intuitive Music Composition and Editing
MusicStory: An Autonomous, Personalized Music Video Creator
Music Representation of Score, Sound, MIDI, Structure and Metadata All Integrated in a Single Multilayer Environment Based on XML
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