作者:
John MaedaSteven HellerTim HooverHas been a design partner at Kleiner Perkins Caufield & Byers since January 2014
helping entrepreneurs and portfolio companies to build design into their company cultures. He served as the president of Rhode Island School of Design for six years through 2013 and also served as an associate director of research at the MIT Media Lab. Maeda has been a practicing designer since 1990 and his work is in the permanent collections of the Museum of Modern Art San Francisco Museum of Modern Art and the Cartier Foundation. He serves on the boards of several corporations including Sonos and Wieden + Kennedy and is chair of eBay's Design Advisory Board. His four published books include The Laws of Simplicity and Redesigning Leadership. Maeda is the recipient of a variety of international awards for his creative work including induction into the Art Director's Club Hall of Fame. In 2001 he received the White House National Design Award in 2002
he received the Mainichi Design Prize in Japan and in 2005
he was awarded the Raymond Loewy Foundation Prize in Germany. Maeda earned a BS and an MS from MIT in computer science and electrical engineering. He received a PhD in design science from the University of Tsukuba Institute of Art and Design in Japan as well as an MBA from Arizona State University. Co‐chair and co‐founder of the MFA Designer as Author and Entrepreneur program at the School of Visual Arts
in New York. He was the art director of The New York Times Book Review and now writes the VISUALS column for the NYTBR. He also writes “The Daily Heller” at *** a weekly online design column for Atlantic magazine. He is author co‐author and editor of more than 165 books on the history and practice of graphic design illustration and satiric art. His forthcoming books include 100 Classic Graphic Design Magazines (Laurence King Publishers) and Raw Data: The Process Behind Information Graphics (Thames + Hudson). Heller was the 2011 recipient of the Smithsonian National Design Award for Design Mind. He has also writ
John Maeda is a past president of Rhode Island School of Design and an internationally known designer. He's now involved in helping entrepreneurs build design into their company cultures. Writer and educator Steve...
John Maeda is a past president of Rhode Island School of Design and an internationally known designer. He's now involved in helping entrepreneurs build design into their company cultures. Writer and educator Steven Heller pairs up with Tim Hoover, head of product and design at Canary, to talk about designer CEOs, start‐ups, and cutting out the middleman.
On October 8-10,2009, an interdisciplinary group met in Beverley, Massachusetts, to evaluate the state of the art in the computational modeling of narrative. Three important findings emerged: (1) current work in compu...
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This article provides an overview of dictionary-based methods (DBMs), and reviews recent work in the application of such methods to working with audio and music signals. As Fourier analysis is to additive synthesis, D...
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This article provides an overview of dictionary-based methods (DBMs), and reviews recent work in the application of such methods to working with audio and music signals. As Fourier analysis is to additive synthesis, DBMs can be seen as the analytical counterpart to a generalized granular synthesis, where a sound is built by combining heterogeneous atoms selected from a user-defined dictionary. As such, DBMs provide novel ways for analyzing and visualizing audio signals, creating multiresolution descriptions of their contents, and designing sound transformations unique to a description of audio in terms of atoms.
In this paper, we propose a video-based full-body gesture recognition system independent of the view angle of the cameras. We performed multilinear analysis on the silhouette images of the static poses making up the g...
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We compare the characteristics and performance of joint (single-step) and sequential (two-step) approaches for creating sparse and structured acoustic signal representations derived using overcomplete methods (OMs). A...
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We compare the characteristics and performance of joint (single-step) and sequential (two-step) approaches for creating sparse and structured acoustic signal representations derived using overcomplete methods (OMs). A joint approach, such as molecular matching pursuit (MMP), attempts to find coherent structures in a signal as part of the decomposition process, while a sequential approach, such as agglomerative clustering (AC), attempts to find coherent structures after the signal decomposition. We review each approach, and examine their performance using real audio and music signals.
In this paper, we tackle robust human pose recognition using unlabelled markers obtained from an optical marker-based motion capture system. A coarse-to-fine fast pose matching algorithm is presented with the followin...
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In this paper, we tackle robust human pose recognition using unlabelled markers obtained from an optical marker-based motion capture system. A coarse-to-fine fast pose matching algorithm is presented with the following three steps. Given a query pose, firstly, the majority of the non-matching poses are rejected according to marker distributions along the radius and height dimensions. Secondly, relative rotation angles between the query pose and the remaining candidate poses are estimated using a fast histogram matching method based on circular convolution implemented using the fast Fourier transform. Finally, rotation angle estimates are refined using nonlinear least square minimization through the Levenberg-Marquardt minimization. In the presence of multiple solutions, false poses can be effectively removed by thresholding the minimized matching scores. The proposed framework can handle missing markers caused by occlusion. Experimental results using real motion capture data show the efficacy of the proposed approach.
The authors investigate the characteristics and performance of joint (single‐step) and sequential (two‐step) approaches to creating sparse and structured multiresolution representations of audio and music signals de...
The authors investigate the characteristics and performance of joint (single‐step) and sequential (two‐step) approaches to creating sparse and structured multiresolution representations of audio and music signals derived using sparse overcomplete methods. A joint approach, such as molecular matching pursuit, attempts to find structures in a signal as part of the decomposition process, while a sequential approach, such as agglomerative clustering, attempts to find structures in the completed decomposition of a signal. Each of these approaches have different benefits and drawbacks. For a joint approach, it is computationally convenient that the decomposition and structuring are done simultaneously, but usually only simple structural relations are possible. For a sequential approach, one is working in a parameter space of much smaller dimension than the original signal, but the computation is higher since the decomposition and the structure building are two separate processes. Results from these approaches using real audio and music signals will be compared and contrasted, and will contribute to our goal of creating an enhanced interface between the content of audio and music signals, e.g., onsets, notes, voices, and their multiresolution sparse atomic decompositions.
We present the Multimodal Music Stand (MMMS) for the untethered sensing of performance gestures and the interactive control of music. Using e-field sensing, audio analysis, and computer vision, the MMMS captures a per...
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ISBN:
(纸本)9781450378376
We present the Multimodal Music Stand (MMMS) for the untethered sensing of performance gestures and the interactive control of music. Using e-field sensing, audio analysis, and computer vision, the MMMS captures a performer's continuous expressive gestures and robustly identifies discrete cues in a musical performance. Continuous and discrete gestures are sent to an interactive music system featuring custom designed software that performs real-time spectral transformation of audio.
We define the research fields of experiential signal processing (ESP) and experiential telecommunications (ET), which are concerned with sensing, communicating, and presenting an Environment, Event, or Experience at a...
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We define the research fields of experiential signal processing (ESP) and experiential telecommunications (ET), which are concerned with sensing, communicating, and presenting an Environment, Event, or Experience at a distance. We develop our vision of ESP and ET and describe key components and research fields. We highlight the challenges of presenting multichannel, multimedia information and present an example for panoramic video using the Allosphere, a 3-story sphere housed in an anechoic chamber, that has been constructed at UCSB.
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