Content-based multimedia retrieval is a challenging research field with many unsolved problems. This monograph details concepts and algorithms for robust and efficient information retrieval of two different types of multimedia data: waveform-based music data and human motion data. It first examines several approaches in music information retrieval, in particular general strategies as well as efficient algorithms. The book then introduces a general and unified framework for motion analysis, retrieval, and classification, highlighting the design of suitable features, the notion of similarity used to compare data streams, and data organization.
Music Information Retrieval: Recent Developments and Applications surveys the young but established field of research that is Music Information Retrieval (MIR). In doing so, it pays particular attention to the latest developments in MIR, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. Music Information Retrieval: Recent Developments and Applications starts by reviewing the well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification ("query by example"). Subsequently, it elaborates on the current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. It concludes with a discussion about the major open challenges facing MIR.
This textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval. Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, computer science, multimedia, and musicology. The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform—concepts that are then used throughout the book. In the subsequent chapters, concrete music processing tasks serve as a starting point. Each of these chapters is organized in a similar fashion and starts with a general description of the music processing scenario at hand before integrating it into a wider context. It then discusses—in a mathematically rigorous way—important techniques and algorithms that are generally applicable to a wide range of analysis, classification, and retrieval problems. At the same time, the techniques are directly applied to a specific music processing task. By mixing theory and practice, the book’s goal is to offer detailed technological insights as well as a deep understanding of music processing applications. Each chapter ends with a section that includes links to the research literature, suggestions for further reading, a list of references, and exercises. The chapters are organized in a modular fashion, thus offering lecturers and readers many ways to choose, rearrange or supplement the material. Accordingly, selected chapters or individual sections can easily be integrated into courses on general multimedia, information science, signal processing, music informatics, or the digital humanities.
This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Symposium on Computer Music Modeling and Retrieval, CMMR 2013, held in Marseille, France, in October 2013. The 38 conference papers presented were carefully reviewed and selected from 94 submissions. The chapters reflect the interdisciplinary nature of this conference with following topics: augmented musical instruments and gesture recognition, music and emotions: representation, recognition, and audience/performers studies, the art of sonification, when auditory cues shape human sensorimotor performance, music and sound data mining, interactive sound synthesis, non-stationarity, dynamics and mathematical modeling, image-sound interaction, auditory perception and cognitive inspiration, and modeling of sound and music computational musicology.
Music Accessing and Retrieval is the first comprehensive survey of the vast new field of Music Information Retrieval (MIR). It describes a number of issues which are peculiar to the language of music - including forms, formats, and dimensions of music - together with the typologies of users and their information needs. To fulfil these needs a number of approaches are discussed, from direct search to information filtering and clustering of music documents. The emphasis is on tools, techniques, and approaches for content-based MIR, rather than on the systems that implement them. The interested reader can, however, find descriptions of more than 35 systems for music retrieval with links to their Web sites. Music Accessing and Retrieval can be used as both a guide for beginners who are embarking on research in this relatively new area, and a useful reference for established researchers in this field.
The textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval (MIR). Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, signal processing, computer science, digital humanities, and musicology. The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform—concepts used throughout the book. Each of the subsequent chapters starts with a general description of a concrete music processing task and then discusses—in a mathematically rigorous way—essential techniques and algorithms applicable to a wide range of analysis, classification, and retrieval problems. By mixing theory and practice, the book’s goal is to offer detailed technological insights and a deep understanding of music processing applications. As a substantial extension, the textbook’s second edition introduces the FMP (fundamentals of music processing) notebooks, which provide additional audio-visual material and Python code examples that implement all computational approaches step by step. Using Jupyter notebooks and open-source web applications, the FMP notebooks yield an interactive framework that allows students to experiment with their music examples, explore the effect of parameter settings, and understand the computed results by suitable visualizations and sonifications. The FMP notebooks are available from the author’s institutional web page at the International Audio Laboratories Erlangen.
Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with
This book constitutes the refereed proceedings of the 14th International Symposium on Perception, Representations, Image, Sound, Music, CMMR 2019, held in Marseille, France, in October 2019. The 46 full papers presented were selected from 105 submissions. The papers are grouped in 9 sections. The first three sections are related to music information retrieval, computational musicology and composition tools, followed by a section on notations and instruments distributed on mobile devices. The fifth section concerns auditory perception and cognition, while the three following sections are related to sound design and sonic and musical interactions. The last section contains contributions that relate to Jean-Claude Risset's research.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
