1,458 research outputs found
NASA JSC neural network survey results
A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc
Introducing Latent Timbre Synthesis
We present the Latent Timbre Synthesis (LTS), a new audio synthesis method
using Deep Learning. The synthesis method allows composers and sound designers
to interpolate and extrapolate between the timbre of multiple sounds using the
latent space of audio frames. We provide the details of two Variational
Autoencoder architectures for LTS, and compare their advantages and drawbacks.
The implementation includes a fully working application with graphical user
interface, called \textit{interpolate\_two}, which enables practitioners to
explore the timbre between two audio excerpts of their selection using
interpolation and extrapolation in the latent space of audio frames. Our
implementation is open-source, and we aim to improve the accessibility of this
technology by providing a guide for users with any technical background
Segmentation ART: A Neural Network for Word Recognition from Continuous Speech
The Segmentation ATIT (Adaptive Resonance Theory) network for word recognition from a continuous speech stream is introduced. An input sequeuce represents phonemes detected at a preproccesing stage. Segmentation ATIT is trained rapidly, and uses a fast-learning fuzzy ART modules, top-down expectation, and a spatial representation of temporal order. The network performs on-line identification of word boundaries, correcting an initial hypothesis if subsequent phonemes are incompatible with a previous partition. Simulations show that the system's segmentation perfonnance is comparable to that of TRACE, and the ability to segment a number of difficult phrases is also demonstrated.National Science Foundation (NSF-IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0G57
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
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