Author: M. Sai Chaitanya
Publisher: GRIN Verlag
ISBN: 3346455246
Category : Music
Languages : en
Pages : 40
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Book Description
Research Paper (postgraduate) from the year 2021 in the subject Musicology - Miscellaneous, grade: 8.0, , course: IMSc Mathematics and Computing, language: English, abstract: This work gives a comprehensive overview of research on the multidisciplinary field of Music Information Retrieval (MIR). MIR uses knowledge from areas as diverse as signal processing, machine learning, information and music theory. The Main Feature of this work is to explore how this knowledge can be used for the development of novel methodologies for browsing and retrieval on large music collections, a hot topic given recent advances in online music distribution and searching. Emphasis would be given to audio signal processing techniques. Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many realworld applications. Those involved in MIR may have a background in musicology, sychoacoustics, psychology, academic music study, signal processing, informatics, machine learning, optical music recognition, computational intelligence or some combination of these. MIR is being used by businesses and academics to categorize, manipulate and even create music. One of the classical MIR research topics is genre classification, which is categorizing music items into one of pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, and music tagging are also popular topics.
Author: Markus Schedl
Publisher:
ISBN: 9781601988065
Category : Computers
Languages : en
Pages : 154
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Book Description
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.
Author: Peter Knees
Publisher: Springer
ISBN: 3662497220
Category : Computers
Languages : en
Pages : 313
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Book Description
This book provides a summary of the manifold audio- and web-based approaches to music information retrieval (MIR) research. In contrast to other books dealing solely with music signal processing, it addresses additional cultural and listener-centric aspects and thus provides a more holistic view. Consequently, the text includes methods operating on features extracted directly from the audio signal, as well as methods operating on features extracted from contextual information, either the cultural context of music as represented on the web or the user and usage context of music. Following the prevalent document-centered paradigm of information retrieval, the book addresses models of music similarity that extract computational features to describe an entity that represents music on any level (e.g., song, album, or artist), and methods to calculate the similarity between them. While this perspective and the representations discussed cannot describe all musical dimensions, they enable us to effectively find music of similar qualities by providing abstract summarizations of musical artifacts from different modalities. The text at hand provides a comprehensive and accessible introduction to the topics of music search, retrieval, and recommendation from an academic perspective. It will not only allow those new to the field to quickly access MIR from an information retrieval point of view but also raise awareness for the developments of the music domain within the greater IR community. In this regard, Part I deals with content-based MIR, in particular the extraction of features from the music signal and similarity calculation for content-based retrieval. Part II subsequently addresses MIR methods that make use of the digitally accessible cultural context of music. Part III addresses methods of collaborative filtering and user-aware and multi-modal retrieval, while Part IV explores current and future applications of music retrieval and recommendation.>
Author: Tao Li
Publisher: CRC Press
ISBN: 1439835527
Category : Business & Economics
Languages : en
Pages : 386
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Book Description
The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.
Author: Jesper Højvang Jensen
Publisher:
ISBN: 9788792328243
Category :
Languages : en
Pages : 152
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Book Description
Author: Claus Weihs
Publisher: CRC Press
ISBN: 1498719570
Category : Business & Economics
Languages : en
Pages : 694
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Book Description
This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.
Author: Alexander Lerch
Publisher: John Wiley & Sons
ISBN: 1119890977
Category : Technology & Engineering
Languages : en
Pages : 467
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Book Description
An Introduction to Audio Content Analysis Enables readers to understand the algorithmic analysis of musical audio signals with AI-driven approaches An Introduction to Audio Content Analysis serves as a comprehensive guide on audio content analysis explaining how signal processing and machine learning approaches can be utilized for the extraction of musical content from audio. It gives readers the algorithmic understanding to teach a computer to interpret music signals and thus allows for the design of tools for interacting with music. The work ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. A multitude of audio content analysis tasks related to the extraction of tonal, temporal, timbral, and intensity-related characteristics of the music signal are presented. Each task is introduced from both a musical and a technical perspective, detailing the algorithmic approach as well as providing practical guidance on implementation details and evaluation. To aid in reader comprehension, each task description begins with a short introduction to the most important musical and perceptual characteristics of the covered topic, followed by a detailed algorithmic model and its evaluation, and concluded with questions and exercises. For the interested reader, updated supplemental materials are provided via an accompanying website. Written by a well-known expert in the music industry, sample topics covered in Introduction to Audio Content Analysis include: Digital audio signals and their representation, common time-frequency transforms, audio features Pitch and fundamental frequency detection, key and chord Representation of dynamics in music and intensity-related features Beat histograms, onset and tempo detection, beat histograms, and detection of structure in music, and sequence alignment Audio fingerprinting, musical genre, mood, and instrument classification An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis covers a wide range of introductory topics pertaining to music information retrieval and machine listening, allowing students and researchers to quickly gain core holistic knowledge in audio analysis and dig deeper into specific aspects of the field with the help of a large amount of references.
Author: Meinard Müller
Publisher: Springer
ISBN: 3319219456
Category : Computers
Languages : en
Pages : 509
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Book Description
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.
Author: Alexander Lerch
Publisher: GRIN Verlag
ISBN: 3640294963
Category : Music
Languages : en
Pages : 201
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Book Description
Doctoral Thesis / Dissertation from the year 2008 in the subject Musicology, grade: 1.0, Technical University of Berlin, 259 entries in the bibliography, language: English, abstract: Different music performances of the same score may significantly differ from each other. It is obvious that not only the composer's work, the score, defines the listener's music experience, but that the music performance itself is an integral part of this experience. Music performers use the information contained in the score, but interpret, transform or add to this information. Four parameter classes can be used to describe a performance objectively: tempo and timing, loudness, timbre and pitch. Each class contains a multitude of individual parameters that are at the performers' disposal to generate a unique physical rendition of musical ideas. The extraction of such objective parameters is one of the difficulties in music performance research. This work presents an approach to the software-based extraction of tempo and timing, loudness and timbre parameters from audio files to provide a tool for the automatic parameter extraction from music performances. The system is applied to extract data from 21 string quartet performances and a detailed analysis of the extracted data is presented. The main contributions of this thesis are the adaptation and development of signal processing approaches to performance parameter extraction and the presentation and discussion of string quartet performances of a movement of Beethoven's late String Quartet op. 130.
Author: Francis F. Li
Publisher: CRC Press
ISBN: 1351644157
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
Starting with essential maths, fundamentals of signals and systems, and classical concepts of DSP, this book presents, from an application-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include but are not limited to room acoustic parameter measurements, filter design, codecs, machine learning for audio pattern recognition and machine audition, spatial audio, array technologies and hearing aids. Some research outcomes are fed into book as worked examples. As a research informed text, the book attempts to present DSP and machine learning from a new and more relevant angle to acousticians and audio engineers. Some MATLAB® codes or frameworks of algorithms are given as downloads available on the CRC Press website. Suggested exploration and mini project ideas are given for "proof of concept" type of exercises and directions for further study and investigation. The book is intended for researchers, professionals, and senior year students in the field of audio acoustics.
