Representations Of Musical Signals
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Representations Of Musical Signals
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Author : Professor of Media Arts and Technology Curtis Roads
language : en
Publisher: Mit Press
Release Date : 1991
Representations Of Musical Signals written by Professor of Media Arts and Technology Curtis Roads and has been published by Mit Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.
Representations of Musical Signals describes a new generation of digital audio and computer music systems made possible by recent advances in digital signal processing theory, hardware design, and programming techniques. It explores new representations of musical signals that can have profound effects on the way musicians conceive of and realize musical ideas. In particular, the book focuses on models that combine time-domain and frequency-domain representations (grains, wavelets, and physical models), visual programming and advanced user interfaces, and that incorporate musical knowledge using artificial intelligence techniques and adaptive neural networks. The 14 contributions take up issues of how musical signals should be displayed to musicians, engineers, and scientists who want to work with them, how professionals can work with the representations to accomplish musical tasks, how systems can be designed to permit working with multiple views of the same signal, and how representations of musical signals should be organized to promote efficient communication between devices using these signals. Giovanni DePoli is a member of the faculty of the Department of Informatics and Electronics at the University of Padua. Aldo Piccialli is a member of the faculty of the Department of Physics at the University of Naples. Curtis Roads is a composer and consulting editor of Computer Music Journal. Contributors: J. M. Adrien. D. Arfib. R. D'Autilia. C. Cadoz. S. Cavaliere G. De Poli, G. Evangelista. J. Florens. G. Garnett. A. Grossman. F. Guerra. K. Hebei. R. KronlandMartinet. C. Lischka. A. Piccialli. J-C. Risset. C. Roads. C. Scaletti., J. Sundberg.
International Workshop On Models And Representations Of Musical Signals
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Author : International Workshop on Models and Representations of Musical Signals (1992, Capri)
language : en
Publisher:
Release Date : 1992
International Workshop On Models And Representations Of Musical Signals written by International Workshop on Models and Representations of Musical Signals (1992, Capri) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
International Workshop On Models And Representations Of Musical Signals
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Author :
language : en
Publisher:
Release Date : 1992
International Workshop On Models And Representations Of Musical Signals written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computer composition categories.
Time Frequency Representation Of Musical Signals Using The Discrete Hermite Transform
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Author : Jacob Joseph Trombetta
language : en
Publisher:
Release Date : 2011
Time Frequency Representation Of Musical Signals Using The Discrete Hermite Transform written by Jacob Joseph Trombetta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Electrical engineering categories.
Time-frequency representations of musical signals help with the visualization and understanding of the building blocks of music. Difficulties with different representations limit the amount of information that can be extracted from the signal processing techniques used. The discrete Hermite transform has already proven to be an excellent choice in biomedical signal processing applications. We will show that the discrete Hermite transfrom is an excellent choice for the analysis of musical signals as well. The discrete Hermite basis functions are approximately sinusoidal with shorter function supports than the Fourier techniques. This gives them better frequency resolution than both classic Fourier transforms and current wavelet transforms for musical signals. We will show that the discrete Hermite transform and the time-frequency display it produces, the Hermitian distribution, will be an invaluable resource to the sign processing community.
Musical Signal Processing
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Author : Curtis Roads
language : en
Publisher: Routledge
Release Date : 2013-12-19
Musical Signal Processing written by Curtis Roads and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-19 with Music categories.
Compiled by an international array of musical and technical specialists, this book deals with some of the most important topics in modern musical signal processing. Beginning with basic concepts, and leading to advanced applications, it covers such essential areas as sound synthesis (including detailed studies of physical modelling and granular synthesis) ,control signal synthesis, sound transformation (including convolution), analysis/resynthesis (phase vocodor, wavelets, analysis by chaotic functions), object-oriented and artificial intelligence representations, musical interfaces and the integration of signal processing techniques in concert performance.
Topics Focus On Microstructural And Macrostructural Representations Models For Musical Signal Analysis Models For Musical Signal Synthesis Representation Of Musical Signals Musical Applications Auditory Models
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Author :
language : en
Publisher:
Release Date : 1992
Topics Focus On Microstructural And Macrostructural Representations Models For Musical Signal Analysis Models For Musical Signal Synthesis Representation Of Musical Signals Musical Applications Auditory Models written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with categories.
Learning Feature Representations For Music Classification
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Author : Juhan Nam
language : en
Publisher:
Release Date : 2012
Learning Feature Representations For Music Classification written by Juhan Nam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
In the recent past music has become ubiquitous as digital data. The scale of music collections in some online music services surpasses ten million tracks. This significant growth and resulting changes in the music content industry pose challenges in terms of efficient and effective content search, retrieval and organization. The most common approach to these needs involves the use of text-based metadata or user data. However, limitations of these methods, such as popularity bias, have prompted research in content-based methods that use audio data directly. The content-based methods are generally composed of two processing modules--extracting features from audio and training a system using the features and ground truth. The audio features, the main interest of this thesis, are conventionally designed in a highly engineered manner based on acoustic knowledge, such as in mel-frequency cepstral coefficients (MFCCs) or chroma. As an alternative approach, there is increasing interest in learning features automatically from data without relying on domain knowledge or manual refinement. This feature representation approach has been studied primarily in the areas of computer vision or speech recognition. In this thesis, we investigate the learning-based feature representation with applications to content-based music information retrieval. Specifically, we suggest a data processing pipeline to effectively learn short-term acoustic dependencies from musical signals and build a song-level feature for music genre classification and music annotation/retrieval. While visualizing the learned acoustics patterns, we will attempt to interpret how they are associated with high-level musical semantics such as genre, emotion or song quality. Through a detailed analysis, we will show the effect of individual processing units in the pipeline and meta parameters of learning algorithms on performance. In addition to these tasks, we also examine the feature learning approach for classification-based piano transcriptions. Throughout experiments on popularly used datasets, we will show that the learned feature representations achieve results comparable to state-of-the-art algorithms or outperform them.
Towards Automatic Rhythm Description Of Musical Audio Signals
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Author : Fabien Gouyon
language : en
Publisher:
Release Date : 2003
Towards Automatic Rhythm Description Of Musical Audio Signals written by Fabien Gouyon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Signal Processing Methods For Music Transcription
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Author : Anssi Klapuri
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-02-26
Signal Processing Methods For Music Transcription written by Anssi Klapuri and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-02-26 with Technology & Engineering categories.
This book serves as an ideal starting point for newcomers and an excellent reference source for people already working in the field. Researchers and graduate students in signal processing, computer science, acoustics and music will primarily benefit from this text. It could be used as a textbook for advanced courses in music signal processing. Since it only requires a basic knowledge of signal processing, it is accessible to undergraduate students.
Musical Signals Spectrogram Analysis With Morphological Filters
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Author : Laura Bustos Manzanet
language : en
Publisher:
Release Date : 2020
Musical Signals Spectrogram Analysis With Morphological Filters written by Laura Bustos Manzanet and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The way an instrument is learned to be played has changed a lot since the rise of the internet, specially the show up of YouTube. High amounts of people learn to play instruments such as the guitar from watching videos and are able to play different songs without knowing any musical language. So, when they listen to a song that they want to play, they do not know the notes that are being played. This dissertation develops a guitar music transcription software that tends to contribute to solve this problem from a new point of view. Currently, the melodic extraction of songs has been analysed from the rough signal analysis. The spectrograms are frequential representations of a song that show accurate information of frequency and time, which are the two main points involved in the melodic analysis of a song. Through analysing the spectrogram as an image and not as a complex matrix, we introduce a new way to extract musical information from a song. The frequency related to the note or notes being played is estimated through the application of morphological filters and delivered to the user.