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Musimathics, Volume 2
The second volume of a commonsense, self-contained introduction to the mathematics and physics of music, focusing on the digital and computational domain; essential reading for musicians, music engineers, and anyone interested in the intersection of art and science.
Volume 2 of Musimathics continues the story of music engineering begun in Volume 1, focusing on the digital and computational domain. Loy goes deeper into the mathematics of music and sound, beginning with digital audio, sampling, and binary numbers, as well as complex numbers and how they simplify representation of musical signals. Chapters cover the Fourier transform, convolution, filtering, resonance, the wave equation, acoustical systems, sound synthesis, the short-time Fourier transform, and the wavelet transform. These subjects provide the theoretical underpinnings of today's music technology. The examples given are all practical problems in music and audio.
Additional material can be found at http://www.musimathics.com.
eBook
Music by the numbers : from Pythagoras to Schoenberg
How music has influenced mathematics, physics, and astronomy from ancient Greece to the twentieth century.
Book
Musimathics : the mathematical foundations of music
The second volume of a commonsense, self-contained introduction to the mathematics and physics of music, focusing on the digital and computational domain; essential reading for musicians, music engineers, and anyone interested in the intersection of art and science.
eBook
A perfect harmony : music, mathematics and science
From the earliest of civilisations, humans have found ways to make music, whether through makeshift drums or artfully drilled bone flutes. But how did music - effectively little more than a series of certain tones and rhythms - become so integral to the human experience? Untangling the curious links between notes and number, musical perception, psychology and physics, David Darling examines the fascinating science behind music, from its Palaeolithic origins to the present.
BOOK
Visualizing Music
To feel the emotional force of music, we experience it
aurally. But how can we convey musical understanding
visually?
Visualizing Music explores the art of communicating
about music through images. Drawing on principles from the fields
of vision science and information visualization, Eric Isaacson
describes how graphical images can help us understand music. By
explaining the history of music visualizations through the lens of
human perception and cognition, Isaacson offers a guide to
understanding what makes musical images effective or ineffective
and provides readers with extensive principles and strategies to
create excellent images of their own. Illustrated with over 300
diagrams from both historical and modern sources, including
examples and theories from Western art music, world music, and
jazz, folk, and popular music, Visualizing Music explores
the decisions made around image creation.
Together with an extensive online supplement and dozens of
redrawings that show the impact of effective techniques,
Visualizing Music is a captivating guide to thinking
differently about design that will help music scholars better
understand the power of musical images, thereby shifting the
ephemeral to material.
eBook
Mathematics in South Africa’s Intermediate Phase: Music integration for enhanced learning
BackgroundEmbracing the influential role of music in education, teachers an cultivate an environment that fosters learners’ curiosity, creativity, and enthusiasm for acquiring knowledge. The first author, experienced in teaching Intermediate Phase music and mathematics, was keen to explore how to bridge the gap between the educational vision for 21st-century knowledge and skills and current teaching practices through the adoption of active music integration and appropriate pedagogy to full engage learners.AimThe study aimed to explore how general teachers, with no previous formal music exposure perceived and engaged with the process of correlating concepts and learning experiences in music and mathematics.SettingThe research was conducted over nine weeks in three South African, Afrikaans-medium, middle-class governmental primary schools located in the Tshwane North district of the Gauteng province.MethodsThis study employed a qualitative case study research approach and was situated within the pedagogical design of constructive alignment for effective teaching and learning.ResultsThe results underscore the significance of generalist teachers’ ability to effectively incorporate music into mathematics lessons without extensive musical training or instrumental skills.ConclusionThe article challenges the notion that musical expertise is a prerequisite for integration, highlighting the fact that generalist teachers can successfully incorporate music into mathematics instruction by fostering meaningful connections between the two subjects.ContributionThis article draws attention to the importance of constructive alignment in promoting independent thinking and the practical application of knowledge. These findings offer guidance for the development of pedagogical frameworks and instructional practices that prioritise meaningful teaching and learning experiences.
Journal Article
Deep learning-based expressive speech synthesis: a systematic review of approaches, challenges, and resources
Speech synthesis has made significant strides thanks to the transition from machine learning to deep learning models. Contemporary text-to-speech (TTS) models possess the capability to generate speech of exceptionally high quality, closely mimicking human speech. Nevertheless, given the wide array of applications now employing TTS models, mere high-quality speech generation is no longer sufficient. Present-day TTS models must also excel at producing expressive speech that can convey various speaking styles and emotions, akin to human speech. Consequently, researchers have concentrated their efforts on developing more efficient models for expressive speech synthesis in recent years. This paper presents a systematic review of the literature on expressive speech synthesis models published within the last 5 years, with a particular emphasis on approaches based on deep learning. We offer a comprehensive classification scheme for these models and provide concise descriptions of models falling into each category. Additionally, we summarize the principal challenges encountered in this research domain and outline the strategies employed to tackle these challenges as documented in the literature. In the Section
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, we pinpoint some research gaps in this field that necessitate further exploration. Our objective with this work is to give an all-encompassing overview of this hot research area to offer guidance to interested researchers and future endeavors in this field.
Journal Article
A review of infant cry analysis and classification
This paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.
Journal Article