Normalizing Non-Linear Speech Speed for Maintaining Listener Comprehension at Increased Playback Speeds

This publication describes methods of normalizing the speed of non-linear speech by applying an algorithm to allow for improved listener comprehension at increased playback speeds. The algorithm computes an amount of tension for a given audio file and subsequently computes a running average of the tension. A high-pass filter is then applied to the tension to remove the average tension. The resulting audio file allows a listener to increase playback speed or maintain a desired average speed while retaining comprehension

A Unified System for Chord Transcription and Key Extraction Using Hidden Markov Models.

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Imaging with Diffraction Tomography

The problem of cross sectional (tomographic) imaging bf objects with diffracting sources is addressed. Specifically the area of investigation is the effect of multiple scattering and attenuation phenomena in diffraction imaging. This work reviews the theory and limits of first order diffraction tomography and studies iterative techniques that can be used to improve the quality of tomographic imaging with diffracting sources. Conventional (straight-ray) tomographic algorithms are not valid when used with acoustic or microwave energy. Thus more sophisticated algorithms are needed; First order diffraction tomography uses a linearized version of the wave equation and gives an especially simple reconstruction algorithm. This work reviews first order approximations to the scattered field and studies the quality of the reconstructions when the assumptions behind these approximations are violated. It will be shown that the Born approximation is valid when the phase change across the object is less than it and the Rytov approximation is valid when the refractive index changes by less than two or three percent. Better reconstructions will be based on higher order approximations to the scattered field. This work describes two fixed point algorithms (the Born and the Rytov approximations) and an algebraic approach to more accurately calculate the scattered fields. The limits of each of these approaches is discussed and simulated results are shown. Finally a review of higher order inversion techniques is presented. Each of these techniques is reviewed and some of their limitations are discussed

The CRC Plotting Package

The CRC Plotting Package is a device independent graphics system. Subroutines for generating graphics exist for programs written in FORTRAN or C. A program called Qplot exists to plot binary vectors generated as the output of any program

Social network visualizations of streaming data: Design and use considerations

Abstract. Understanding networks of people linked by some common factor is an important task in many domains. Most commonly, a user creates a visualization of social interactions to see the patterns of interactions between individuals, and then used to find and identify important groups. Networks of individuals and links between them form graphs that vary with time and importance. Visualizing the changes in social networks over time is a non-trivial design task, imposing interesting demands on the visualization and interaction model. In this paper we briefly analyze the user requirements for interactive visualizations of streaming social network data. We find that these continuously updated, dynamic displays need: (1) controls that permit time-based control of the visualization, including pausing, restarting and variable speed playback of the data, (2) the ability to continue importing and processing streamed information even the display is paused, (3) a visually represented method to track changes in the displays over time, (4) interaction methods to allow drilldown from the visualization to original source data, and (5) information extraction from the displayed social network. We describe our visualization tool, SSNV, showing how it embodies these interaction requirements

Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals

Deep neural networks have been recently shown to capture intricate information transformation of signals from the sensory profiles to semantic representations that facilitate recognition or discrimination of complex stimuli. In this vein, convolutional neural networks (CNNs) have been used very successfully in image and audio classification. Designed to imitate the hierarchical structure of the nervous system, CNNs reflect activation with increasing degrees of complexity that transform the incoming signal onto object-level representations. In this work, we employ a CNN trained for large-scale audio object classification to gain insights about the contribution of various audio representations that guide sound perception. The analysis contrasts activation of different layers of a CNN with acoustic features extracted directly from the scenes, perceptual salience obtained from behavioral responses of human listeners, as well as neural oscillations recorded by electroencephalography (EEG) in response to the same natural scenes. All three measures are tightly linked quantities believed to guide percepts of salience and object formation when listening to complex scenes. The results paint a picture of the intricate interplay between low-level and object-level representations in guiding auditory salience that is very much dependent on context and sound category