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| data science and music: Entertainment Science Thorsten Hennig-Thurau, Mark B. Houston, 2018-08-01 The entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage – the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston – two of our finest scholars in the area of entertainment marketing – have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can’t be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Kölmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science’s winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allègre Hadida, Associate Professor in Strategy, University of Cambridge |
| data science and music: Music Data Analysis Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph, 2016-11-17 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. |
| data science and music: Music Data Mining Tao Li, Mitsunori Ogihara, George Tzanetakis, 2011-07-12 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 |
| data science and music: The Computer and the Cancelled Music Lessons Shingai Manjengwa, 2019-04-23 This children's book introduces young readers (and older ones) to 'data science, ' the process of ethically acquiring, analyzing, visualizing and monetizing data. With advancements in technology, new jobs are emerging and old roles are being transformed as a result of the explosion in data from mobile technology, cloud computing, social media, the internet of things (IoT), and Artificial Intelligence (AI). Start this important conversation with kids in a fun way by reading and discussing with them, how one student in this story uses data to solve a problem at school |
| data science and music: Penelope Pie's Pizza Party Liv Buli, 2018-11 Help Penelope figure out how to solve the puzzle and make sure all the guests at her birthday party can enjoy their favorite slice of pizza pie! Penelope prefers plain, but Barnaby Barchart, Laney Line, and Bertie Boxplot all have a favorite topping. Penelope Pie's Pizza Party is the first book in the Vizkidz series. Delving into these stories, kids will learn how to compare data sets in a bar chart, that pie charts best illustrate parts of a whole, that correlation does not equal causation, and other valuable lessons about the fundamentals of data analytics. Data collection and analysis has become core to almost every industry and function in society today, but data literacy is lagging behind. The amount of information we process every day can be of massive value, but only if we are able to keep up. Through their fun-filled adventures and number-crunching challenges our Vizkidz help young readers explore the fundamentals of data analytics and computational thinking. In a STEM-centric digital world, these are skills kids definitely need for the future. |
| data science and music: Critical Excess J. Griffith Rollefson, 2021-06-07 Jay-Z and Kanye West's death dance for capitalism |
| data science and music: Data Smart John W. Foreman, 2013-10-31 Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the data scientist, toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know. |
| data science and music: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science and music: Data Science in Context Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing, 2022-10-20 Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world. |
| data science and music: Sound Knowledge J. Q. Davies, Ellen Lockhart, 2016 What does it mean to hear scientifically? What does it mean to see musically? This volume uncovers a new side to the long nineteenth century in London, a hidden history in which virtuosic musical entertainment and scientific discovery intersected in remarkable ways. Sound Knowledge examines how scientific truth was accrued by means of visual and aural experience, and, in turn, how musical knowledge was located in relation to empirical scientific practice. James Q. Davies and Ellen Lockhart gather work by leading scholars to explore a crucial sixty-year period, beginning with Charles Burney’s ambitious General History of Music, a four-volume study of music around the globe, and extending to the Great Exhibition of 1851, where musical instruments were assembled alongside the technologies of science and industry in the immense glass-encased collections of the Crystal Palace. Importantly, as the contributions show, both the power of science and the power of music relied on performance, spectacle, and experiment. Ultimately, this volume sets the stage for a new picture of modern disciplinarity, shining light on an era before the division of aural and visual knowledge. |
| data science and music: Fundamentals of Music Processing Meinard Müller, 2015-07-21 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. |
| data science and music: Spotify Teardown Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, Patrick Vonderau, 2019-02-19 An innovative investigation of the inner workings of Spotify that traces the transformation of audio files into streamed experience. Spotify provides a streaming service that has been welcomed as disrupting the world of music. Yet such disruption always comes at a price. Spotify Teardown contests the tired claim that digital culture thrives on disruption. Borrowing the notion of “teardown” from reverse-engineering processes, in this book a team of five researchers have playfully disassembled Spotify's product and the way it is commonly understood. Spotify has been hailed as the solution to illicit downloading, but it began as a partly illicit enterprise that grew out of the Swedish file-sharing community. Spotify was originally praised as an innovative digital platform but increasingly resembles a media company in need of regulation, raising questions about the ways in which such cultural content as songs, books, and films are now typically made available online. Spotify Teardown combines interviews, participant observations, and other analyses of Spotify's “front end” with experimental, covert investigations of its “back end.” The authors engaged in a series of interventions, which include establishing a record label for research purposes, intercepting network traffic with packet sniffers, and web-scraping corporate materials. The authors' innovative digital methods earned them a stern letter from Spotify accusing them of violating its terms of use; the company later threatened their research funding. Thus, the book itself became an intervention into the ethics and legal frameworks of corporate behavior. |
| data science and music: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science and music: Deep Learning Techniques for Music Generation Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet, 2019-11-08 This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website. |
| data science and music: The Art of Data Science Roger D. Peng, Elizabeth Matsui, 2016-06-08 This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.--Leanpub.com. |
| data science and music: Science, Music, And Mathematics: The Deepest Connections Michael Edgeworth Mcintyre, 2021-11-03 Professor Michael Edgeworth McIntyre is an eminent scientist who has also had a part-time career as a musician. From a lifetime's thinking, he offers this extraordinary synthesis exposing the deepest connections between science, music, and mathematics, while avoiding equations and technical jargon. He begins with perception psychology and the dichotomization instinct and then takes us through biological evolution, human language, and acausality illusions all the way to the climate crisis and the weaponization of the social media, and beyond that into the deepest parts of theoretical physics — demonstrating our unconscious mathematical abilities.He also has an important message of hope for the future. Contrary to popular belief, biological evolution has given us not only the nastiest, but also the most compassionate and cooperative parts of human nature. This insight comes from recognizing that biological evolution is more than a simple competition between selfish genes. Rather, he suggests, in some ways it is more like turbulent fluid flow, a complex process spanning a vast range of timescales.Professor McIntyre is a Fellow of the Royal Society of London (FRS) and has worked on problems as diverse as the Sun's magnetic interior, the Antarctic ozone hole, jet streams in the atmosphere, and the psychophysics of violin sound. He has long been interested in how different branches of science can better communicate with each other and with the public, harnessing aspects of neuroscience and psychology that point toward the deep 'lucidity principles' that underlie skilful communication. |
| data science and music: The Science of Sound and Music Shar Levine, Leslie Johnstone, 2000 Provides a variety of simple experiments investigating the science behind sound. |
| data science and music: Nature's Music Peter R. Marler, Hans Slabbekoorn, 2004-10-05 The voices of birds have always been a source of fascination. Nature's Music brings together some of the world's experts on birdsong, to review the advances that have taken place in our understanding of how and why birds sing, what their songs and calls mean, and how they have evolved. All contributors have strived to speak, not only to fellow experts, but also to the general reader. The result is a book of readable science, richly illustrated with recordings and pictures of the sounds of birds. Bird song is much more than just one behaviour of a single, particular group of organisms. It is a model for the study of a wide variety of animal behaviour systems, ecological, evolutionary and neurobiological. Bird song sits at the intersection of breeding, social and cognitive behaviour and ecology. As such interest in this book will extend far beyond the purely ornithological - to behavioural ecologists psychologists and neurobiologists of all kinds.* The scoop on local dialects in birdsong* How birdsongs are used for fighting and flirting* The writers are all international authorities on their subject |
| data science and music: Music in the Social and Behavioral Sciences William Forde Thompson, 2014-07-18 This first definitive reference resource to take a broad interdisciplinary approach to the nexus between music and the social and behavioral sciences examines how music affects human beings and their interactions in and with the world. The interdisciplinary nature of the work provides a starting place for students to situate the status of music within the social sciences in fields such as anthropology, communications, psychology, linguistics, sociology, sports, political science and economics, as well as biology and the health sciences. Features: Approximately 450 articles, arranged in A-to-Z fashion and richly illustrated with photographs, provide the social and behavioral context for examining the importance of music in society. Entries are authored and signed by experts in the field and conclude with references and further readings, as well as cross references to related entries. A Reader's Guide groups related entries by broad topic areas and themes, making it easy for readers to quickly identify related entries. A Chronology of Music places material into historical context; a Glossary defines key terms from the field; and a Resource Guide provides lists of books, academic journals, websites and cross-references. The multimedia digital edition is enhanced with video and audio clips and features strong search-and-browse capabilities through the electronic Reader’s Guide, detailed index, and cross references. Music in the Social and Behavioral Sciences, available in both multimedia digital and print formats, is a must-have reference for music and social science library collections. |
| data science and music: Music and Connectionism Peter M. Todd, D. Gareth Loy, 1991 Annotation As one of our highest expressions of thought and creativity, music has always been a difficult realm to capture, model, and understand. The connectionist paradigm, now beginning to provide insights into many realms of human behavior, offers a new and unified viewpoint from which to investigate the subtleties of musical experience. Music and Connectionism provides a fresh approach to both fields, using the techniques of connectionism and parallel distributed processing to look at a wide range of topics in music research, from pitch perception to chord fingering to composition.The contributors, leading researchers in both music psychology and neural networks, address the challenges and opportunities of musical applications of network models. The result is a current and thorough survey of the field that advances understanding of musical phenomena encompassing perception, cognition, composition, and performance, and in methods for network design and analysis.Peter M. Todd is a doctoral candidate in the PDP Research Group of the Psychology Department at Stanford University. Gareth Loy is an award-winning composer, a lecturer in the Music Department of the University of California, San Diego, and a member of the technical staff of Frox Inc.Contributors. Jamshed J. Bharucha. Peter Desain. Mark Dolson. Robert Gjerclingen. Henkjan Honing. B. Keith Jenkins. Jacqueline Jons. Douglas H. Keefe. Tuevo Kohonen. Bernice Laden. Pauli Laine. Otto Laske. Marc Leman. J. P. Lewis. Christoph Lischka. D. Gareth Loy. Ben Miller. Michael Mozer. Samir I. Sayegh. Hajime Sano. Todd Soukup. Don Scarborough. Kalev Tiits. Peter M. Todd. Kari Torkkola. |
| data science and music: The Science of Sci-Fi Music Andrew May, 2020-06-30 The 20th century saw radical changes in the way serious music is composed and produced, including the advent of electronic instruments and novel compositional methods such as serialism and stochastic music. Unlike previous artistic revolutions, this one took its cues from the world of science. Creating electronic sounds, in the early days, required a well-equipped laboratory and an understanding of acoustic theory. Composition became increasingly “algorithmic”, with many composers embracing the mathematics of set theory. The result was some of the most intellectually challenging music ever written – yet also some of the best known, thanks to its rapid assimilation into sci-fi movies and TV shows, from the electronic scores of Forbidden Planet and Dr Who to the other-worldly sounds of 2001: A Space Odyssey. This book takes a close look at the science behind science fiction music, as well as exploring the way sci-fi imagery found its way into the work of musicians like Sun Ra and David Bowie, and how music influenced the science fiction writings of Philip K. Dick and others. |
| data science and music: Data Science Ivo D. Dinov, Milen Velchev Velev, 2021-12-06 The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time. The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public. |
| data science and music: Physics and Music Harvey E. White, Donald H. White, 2014-04-15 Comprehensive and accessible, this foundational text surveys general principles of sound, musical scales, characteristics of instruments, mechanical and electronic recording devices, and many other topics. More than 300 illustrations plus questions, problems, and projects. |
| data science and music: Information Retrieval for Music and Motion Meinard Müller, 2007-09-09 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. |
| data science and music: The Science of Song Alan Cross, Emme Cross, Nicole Mortillaro, 2021-09-07 The coolest facts about the music we make, listen to and love. This illustrated book explores how music and the ways we experience it has transformed over the years and the science behind all of it. It starts with the basics — how does sound work? and what, exactly, is music? — then follows the progression of music-recording technology, from the phonograph to streaming. It covers how everyday items like headphones were created, and includes a look at the science of how we experience music (like why we can’t get certain songs out of our heads). All while suggested playlists accompany the text so that readers can listen along! Kids know that music moves them. Now they can learn how! |
| data science and music: Music and Science Tuomas Eerola, 2024-11-25 Music and Science provides an introduction and practical guidance for a scientific and systematic approach to music research. Students with a background in humanities may find the field hard to tackle and this accessible guide will show them how to consider using an appropriate range of methods, introducing them to current standards of research practices including research ethics, open access, and using computational tools such as R for analysis. These research methods are used to identify the underlying patterns behind the data to better understand how music is constructed and how we are influenced by music. The book focusses on music perception and the experience of music as approached through empirical experiments and by analysing music using computational tools spanning audio and score materials. The process of research, collaboration, and publishing in this area of study is also explained and emphasis is given to transparent and replicable research principles. The book will be essential reading for students undertaking empirical projects, particularly in the area of music psychology but also in digital humanities and media studies. |
| data science and music: The Music Machine Curtis Roads, 1989 In The Music Machine, Curtis Roads brings together 53 classic articles published in Computer Music Journal between 1980 and 1985. |
| data science and music: Computational Music Analysis David Meredith, 2015-10-27 This book provides an in-depth introduction and overview of current research in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition, machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmonic and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally, classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry. |
| data science and music: Building the New Economy Alex Pentland, Alexander Lipton, Thomas Hardjono, 2021-10-12 How to empower people and communities with user-centric data ownership, transparent and accountable algorithms, and secure digital transaction systems. Data is now central to the economy, government, and health systems—so why are data and the AI systems that interpret the data in the hands of so few people? Building the New Economy calls for us to reinvent the ways that data and artificial intelligence are used in civic and government systems. Arguing that we need to think about data as a new type of capital, the authors show that the use of data trusts and distributed ledgers can empower people and communities with user-centric data ownership, transparent and accountable algorithms, machine learning fairness principles and methodologies, and secure digital transaction systems. It’s well known that social media generate disinformation and that mobile phone tracking apps threaten privacy. But these same technologies may also enable the creation of more agile systems in which power and decision-making are distributed among stakeholders rather than concentrated in a few hands. Offering both big ideas and detailed blueprints, the authors describe such key building blocks as data cooperatives, tokenized funding mechanisms, and tradecoin architecture. They also discuss technical issues, including how to build an ecosystem of trusted data, the implementation of digital currencies, and interoperability, and consider the evolution of computational law systems. |
| data science and music: Hands-On Music Generation with Magenta Alexandre DuBreuil, 2020-01-31 Design and use machine learning models for music generation using Magenta and make them interact with existing music creation tools Key FeaturesLearn how machine learning, deep learning, and reinforcement learning are used in music generationGenerate new content by manipulating the source data using Magenta utilities, and train machine learning models with itExplore various Magenta projects such as Magenta Studio, MusicVAE, and NSynthBook Description The importance of machine learning (ML) in art is growing at a rapid pace due to recent advancements in the field, and Magenta is at the forefront of this innovation. With this book, you’ll follow a hands-on approach to using ML models for music generation, learning how to integrate them into an existing music production workflow. Complete with practical examples and explanations of the theoretical background required to understand the underlying technologies, this book is the perfect starting point to begin exploring music generation. The book will help you learn how to use the models in Magenta for generating percussion sequences, monophonic and polyphonic melodies in MIDI, and instrument sounds in raw audio. Through practical examples and in-depth explanations, you’ll understand ML models such as RNNs, VAEs, and GANs. Using this knowledge, you’ll create and train your own models for advanced music generation use cases, along with preparing new datasets. Finally, you’ll get to grips with integrating Magenta with other technologies, such as digital audio workstations (DAWs), and using Magenta.js to distribute music generation apps in the browser. By the end of this book, you'll be well-versed with Magenta and have developed the skills you need to use ML models for music generation in your own style. What you will learnUse RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequencesUse WaveNet and GAN models to generate instrument notes in the form of raw audioEmploy Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequencesPrepare and create your dataset on specific styles and instrumentsTrain your network on your personal datasets and fix problems when training networksApply MIDI to synchronize Magenta with existing music production tools like DAWsWho this book is for This book is for technically inclined artists and musically inclined computer scientists. Readers who want to get hands-on with building generative music applications that use deep learning will also find this book useful. Although prior musical or technical competence is not required, basic knowledge of the Python programming language is assumed. |
| data science and music: This is Your Brain on Music Daniel Levitin, 2019-07-04 From the author of The Changing Mind and The Organized Mind comes a New York Times bestseller that unravels the mystery of our perennial love affair with music ***** 'What do the music of Bach, Depeche Mode and John Cage fundamentally have in common?' Music is an obsession at the heart of human nature, even more fundamental to our species than language. From Mozart to the Beatles, neuroscientist, psychologist and internationally-bestselling author Daniel Levitin reveals the role of music in human evolution, shows how our musical preferences begin to form even before we are born and explains why music can offer such an emotional experience. In This Is Your Brain On Music Levitin offers nothing less than a new way to understand music, and what it can teach us about ourselves. ***** 'Music seems to have an almost wilful, evasive quality, defying simple explanation, so that the more we find out, the more there is to know . . . Daniel Levitin's book is an eloquent and poetic exploration of this paradox' Sting 'You'll never hear music in the same way again' Classic FM magazine 'Music, Levitin argues, is not a decadent modern diversion but something of fundamental importance to the history of human development' Literary Review |
| data science and music: Mastering Spark for Data Science Andrew Morgan, Antoine Amend, David George, Matthew Hallett, 2017-03-29 Master the techniques and sophisticated analytics used to construct Spark-based solutions that scale to deliver production-grade data science products About This Book Develop and apply advanced analytical techniques with Spark Learn how to tell a compelling story with data science using Spark's ecosystem Explore data at scale and work with cutting edge data science methods Who This Book Is For This book is for those who have beginner-level familiarity with the Spark architecture and data science applications, especially those who are looking for a challenge and want to learn cutting edge techniques. This book assumes working knowledge of data science, common machine learning methods, and popular data science tools, and assumes you have previously run proof of concept studies and built prototypes. What You Will Learn Learn the design patterns that integrate Spark into industrialized data science pipelines See how commercial data scientists design scalable code and reusable code for data science services Explore cutting edge data science methods so that you can study trends and causality Discover advanced programming techniques using RDD and the DataFrame and Dataset APIs Find out how Spark can be used as a universal ingestion engine tool and as a web scraper Practice the implementation of advanced topics in graph processing, such as community detection and contact chaining Get to know the best practices when performing Extended Exploratory Data Analysis, commonly used in commercial data science teams Study advanced Spark concepts, solution design patterns, and integration architectures Demonstrate powerful data science pipelines In Detail Data science seeks to transform the world using data, and this is typically achieved through disrupting and changing real processes in real industries. In order to operate at this level you need to build data science solutions of substance –solutions that solve real problems. Spark has emerged as the big data platform of choice for data scientists due to its speed, scalability, and easy-to-use APIs. This book deep dives into using Spark to deliver production-grade data science solutions. This process is demonstrated by exploring the construction of a sophisticated global news analysis service that uses Spark to generate continuous geopolitical and current affairs insights.You will learn all about the core Spark APIs and take a comprehensive tour of advanced libraries, including Spark SQL, Spark Streaming, MLlib, and more. You will be introduced to advanced techniques and methods that will help you to construct commercial-grade data products. Focusing on a sequence of tutorials that deliver a working news intelligence service, you will learn about advanced Spark architectures, how to work with geographic data in Spark, and how to tune Spark algorithms so they scale linearly. Style and approach This is an advanced guide for those with beginner-level familiarity with the Spark architecture and working with Data Science applications. Mastering Spark for Data Science is a practical tutorial that uses core Spark APIs and takes a deep dive into advanced libraries including: Spark SQL, visual streaming, and MLlib. This book expands on titles like: Machine Learning with Spark and Learning Spark. It is the next learning curve for those comfortable with Spark and looking to improve their skills. |
| data science and music: Decomposed Kyle Devine, 2019-10-15 The hidden material histories of music. Music is seen as the most immaterial of the arts, and recorded music as a progress of dematerialization—an evolution from physical discs to invisible digits. In Decomposed, Kyle Devine offers another perspective. He shows that recorded music has always been a significant exploiter of both natural and human resources, and that its reliance on these resources is more problematic today than ever before. Devine uncovers the hidden history of recorded music—what recordings are made of and what happens to them when they are disposed of. Devine's story focuses on three forms of materiality. Before 1950, 78 rpm records were made of shellac, a bug-based resin. Between 1950 and 2000, formats such as LPs, cassettes, and CDs were all made of petroleum-based plastic. Today, recordings exist as data-based audio files. Devine describes the people who harvest and process these materials, from women and children in the Global South to scientists and industrialists in the Global North. He reminds us that vinyl records are oil products, and that the so-called vinyl revival is part of petrocapitalism. The supposed immateriality of music as data is belied by the energy required to power the internet and the devices required to access music online. We tend to think of the recordings we buy as finished products. Devine offers an essential backstory. He reveals how a range of apparently peripheral people and processes are actually central to what music is, how it works, and why it matters. |
| data science and music: Dear Data Giorgia Lupi, Stefanie Posavec, 2016-09-13 Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates the infinitesimal, incomplete, imperfect, yet exquisitely human details of life, in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear Data reproduces in pinpoint detail the full year's set of cards, front and back, providing a remarkable portrait of two artists connected by their attention to the details of their lives—including complaints, distractions, phone addictions, physical contact, and desires. These details illuminate the lives of two remarkable young women and also inspire us to map our own lives, including specific suggestions on what data to draw and how. A captivating and unique book for designers, artists, correspondents, friends, and lovers everywhere. |
| data science and music: Computational Intelligence in Data Science Mieczyslaw Lech Owoc, |
| data science and music: Music as a Platform for Political Communication Onyebadi, Uche, 2017-02-14 Artistic expression is a longstanding aspect of mankind and our society. While art can simply be appreciated for aesthetic artistic value, it can be utilized for other various multidisciplinary purposes. Music as a Platform for Political Communication is a comprehensive reference source for the latest scholarly perspectives on delivering political messages to society through musical platforms and venues. Highlighting innovative research topics on an international scale, such as election campaigns, social justice, and protests, this book is ideally designed for academics, professionals, practitioners, graduate students, and researchers interested in discovering how musical expression is shaping the realm of political communication. |
| data science and music: Envisioning the Data Science Discipline National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-03-05 The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education. |
| data science and music: The Power of Music Elena Mannes, 2011-05-31 The award-winning creator of the documentary The Music Instinct traces the efforts of visionary researchers and musicians to understand the biological foundations of music and its relationship to the brain and the physical world. 35,000 first printing. |
| data science and music: Beginning Data Science in R Thomas Mailund, 2017-03-09 Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language. |
| data science and music: Quantifying Music H.F. Cohen, 2013-11-11 The soul rejoices in perceiving harmonious sound; when the sound is not harmonious it is grieved. From these affects of the soul are derived the name of consonances for the harmonic proportions, and the name of dissonances for the unharmonic proportions. When to this is added the other harmonie proportion whieh consists of the longer or shorter duration of musical sound, then the soul stirs the body to jumping dance, the tongue to inspired speech, according to the same laws. The artisans accommodate to these harmonies the blows of their hammers, the soldiers their pace. As long as the harmonies endure, everything is alive; everything stiffens, when they are disturbed.! Thus the German astronomer, Johannes Kepler, evokes the power of music. Where does this power come from? What properties of music enable it to stir up emotions which may go far beyond just feeling generally pleased, and which may express themselves, for instance, in weeping; in laughing; in trembling over the whole body; in a marked acceleration of breathing and heartbeat; in participating in the rhythm with the head, the hands, the arms, and the feet? From the beginning of musical theory the answer to this question has been sought in two different directions. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
