nils-herrmann/clean_scirepeval huggingface dataset:
A Monaural Cue Sound Localizer

We describe the first single microphone sound localization system and its inspiration from theories of human monaural sound localization. Reflections and diffractions caused by the external ear (pinna) allow humans to estimate sound source elevations using only one ear. Our single microphone localization model relies on a specially shaped reflecting structure that serves the role of the pinna. Specially designed analog VLSI circuitry uses echo-time processing to localize the sound. A CMOS integrated circuit has been designed, fabricated, and successfully demonstrated on actual sounds.
A probabilistic analysis of the ratio spectrum

Recently, Lim and Harris (see IEEE International Symposium on Circuits and Systems, p.277-80, June 1998) developed a novel spectral representation called the ratio spectrum. The ratio spectrum is formed by taking the ratio of the power of a low-pass filtered signal to the power of the original unfiltered signal for all filter cutoff frequencies. Features extracted from the ratio spectrum have proven to be promising for phoneme recognition and speech compression. In this work we have shown that the ratio spectrum feature sets are competitive with standard feature sets used for automatic speech recognition. To understand the underlying properties of the ratio spectrum that contribute to the generation of a useful feature set, we derived the analytic expressions that describe the behavior of the ratio spectrum for a white noise model.
A Low Bandwidth Speech Coder Based on the Ratio Spectrum

We have implemented a low bandwidth speech coder using a new spectral representation called the ratio spectrum. The ratio spectrum is approximated in the digital domain by the normalized cumulative sum of the magnitude of the Fourier spectrum. This cumulative sum is guaranteed to be a monotonic nondecreasing function between zero and one. For each frame, frequency values are extracted by uniformly sampling along the ratio axis. These frequency values are coded and transmitted along with the overall power level, pitch and voicing information. The coder achieves a 1.59Kbps coding rate for intelligible speech. The speech quality of the coder can be improved by increasing the number of samples with subsequently higher bitrates. Introduction V/ UV Spectrum Analysis Formant Extraction
The role of massive color quantization in object recognition

Psychophysical experiments inspire a more complete analysis of the effect of quantization on a modified version of the histogram indexing method of object recognition. We derive an equation that describes how the amount of quantization and number of features kept affects the recognition accuracy. The equation shows that quantization from 2/sup 24/ colors to 15 colors has a negligible effect on accuracy. A simulation shows that large numbers of objects cause a corresponding decrease in accuracy, but that keeping more features can increase the accuracy even for massive quantization. An object recognition experiment with real data shows dramatically better results when quantization is used, indicating that massive color quantization can provide some invariance to lighting conditions.
Loss function for blind source separation-minimum entropy criterion and its generalized anti-Hebbian rules

In adaptive signal processing, the least-mean squares (LMS) algorithm has long been used in signal enhancement and noise cancellation but it cannot overcome the difficulty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a statistical modeling of the signals using statistical measures such as a loss function, which is different from the mutual information. This paper proposes a new loss function based on generalized Gaussian distribution family, and derives new simple adaptive learning rules. Our separator based on the new generalized "anti-Hebbian rules" is also justified by the simulation on both artificial and real data with good performance.
Analog Hardware Implementation of Continuous-Time Adaptive Filter Structures

We have implemented a four-tap adaptive filter in a continuous-time analog VLSI circuit. Since an ideal delay is impossible to implement in continuous-time hardware, we implemented the delay line as a cascade of low-pass filters (called the gamma filter). Since many years of research in our lab has shown that the gamma filter outperforms the ideal delay line for a wide range of applications, the gamma filter should not be considered merely a crude approximation of the ideal delay line. We show measured results from an analog chip that solves the problem of system identification–identifying an unknown linear circuit from its input/output relationship. Furthermore, we believe that a cascade of all-pass filters (called the Laguerre filter) will potentially outperform the gamma. We have built an adaptive Laguerre filter and show that its measured convergence rate is superior to that of the gamma. Finally, rather than perform gradient descent on a multimodal error function to determine a single optimal time constant, we propose multi-scale realizations of these delay line structures.
F&H filter: a novel ultra-low power discrete time filter

A new technique for designing filters with long time constants in the discrete time domain is presented. The F&H (filter and hold) methodology halts the state of a continuous time filter every T seconds resulting in a filter implementation with time constants that can be controlled in three distinct ways: by the sampling period T, the duty cycle k=/spl tau//T or the time constant of the continuous time filter prototype. The final filter can be constructed from a typical Gm-C technique with very low power consumption.
Wavelet denoising of chirp-like signals in the Fourier domain

Wavelet shrinkage is a simple yet powerful tool for denoising piecewise smooth signals. In this paper we show that this method fails on chirp-like signals. We propose a new strategy to denoise these types of signals. First, we transform the signal to the Fourier domain, and denoise its Fourier transform using conventional wavelet shrinkage. Then, we obtain the resulting time domain signal with the inverse Fourier transform. The results are compared to the well-known wavelet shrinkage method and the ideal Wiener filtering method.
Nonuniformity correction of infrared image sequences using the constant-statistics constraint

Using clues from neurobiological adaptation, we have developed the constant-statistics (CS) algorithm for nonuniformity correction of infrared focal point arrays (IRFPAs) and other imaging arrays. The CS model provides an efficient implementation that can also eliminate much of the ghosting artifact that plagues all scene-based nonuniformity correction (NUC) algorithms. The CS algorithm with deghosting is demonstrated on synthetic and real infrared (IR) sequences and shown to improve the overall accuracy of the correction procedure.
A Neuromorphic Monaural Sound Localizer

We describe the first single microphone sound localization system and its inspiration from theories of human monaural sound localization. Reflections and diffractions caused by the external ear (pinna) allow humans to estimate sound source elevations using only one ear. Our single microphone localization model relies on a specially shaped reflecting structure that serves the role of the pinna. Specially designed analog VLSI circuitry uses echo-time processing to localize the sound. A CMOS integrated circuit has been designed, fabricated, and successfully demonstrated on actual sounds.
Analog implementation of ratio spectrum computation

A novel transform space called the ratio spectrum has been developed. The ratio spectrum is formed by plotting the ratio of the power of a low-pass filtered signal to the power of the original unfiltered signal vs. the filter cutoff frequency. The spectrum is necessarily a monotonic function of frequency that is bounded between zero and one. Though we prove that this transform space is mathematically equivalent to the power spectrum of a signal, the ratio spectrum provides a number of computational advantages. One major advantage is that analog hardware can efficiently compute the ratio spectrum. Measured results from such an analog VLSI implementation are discussed in this paper.
Theory and implementation of a Gaussian decay low-pass filter

We have developed a method for approximating a Gaussian decay low-pass filter using subthreshold CMOS VLSI circuits. The time constant (or /spl sigma/) of the delay is adjustable over several orders of magnitude and the DC gain is guaranteed to be unity. Since the design is compact and straightforward, the proposed circuit can be used as a building block that can replace first-order low-pass filters in many situations. Gaussian decay circuits have applications in temporal preprocessing for neural networks, demodulation in communication, and auditory sound localization. We also believe that the Gaussian decay function may be superior to other time-delay elements in adaptive filter implementations.
Wavelet CFAR detector for mass detection in mammography

We introduce a continuous scale wavelet detector for identifying masses in mammograms. Continuous-scale wavelet algorithms have been discussed in the past, however this is the first reported algorithm that uses a scaled version of the same mother wavelet at each scale of analysis. This single mother wavelet property leads to a simpler implementation and a more direct application of detection theory to recognition problems than traditional multiscale analysis. In addition, we show that a continuous-scale search is necessary for computer aided diagnosis of mammography since traditional solutions using dyadic scales either fail to detect some masses or signal too many false alarms. Our novel wavelet detector combines a wavelet formulation with the classical theory of constant false alarm rates detectors. Finally, we show that our algorithm is able to detect masses in actual mammograms that could not be seen using conventional windowing and leveling or other traditional methods of contrast enhancement.
Minimizing the ghosting artifact in scene-based nonuniformity correction

Current infra-red focal point arrays are limited by their inability to calibrate out component variations. Nonuniformity correction (NUC) techniques have been developed and implemented in off-board digital hardware to perform the necessary calibration for most IR sensing applications. There are two possible types of NUC that can be considered for focal-plane integration: (1) Two-point correction using calibrated images on startup and (2) Scene- based techniques that continually recalibrate the sensor for parameter drifts. The problems with the two-point methods have been well-documented in the literature (parameter drift, expense, etc.) We address the two major problems of scene-based techniques: (1) a more difficult hardware implementation and (2) ghosting artifacts in the corrected images. We have previously addressed the implementation problems by developing and demonstrating special purpose analog hardware as well as an efficient digital algorithm that incorporates the constant statistics model. The ghosting artifact occurs in all scene-based techniques when an object that does not move enough tends to `burn in' and can remain visible for thousands of images after the object has left the field of view. We have improved our model to eliminate much of the ghosting artifact that plagues all scene-based NUC algorithms. By modifying the correction update during ghosting situations, we are able to significantly remove the ghosting artifact and improve the overall accuracy of the correction procedure. We demonstrate these results on real and synthetic image sequences.
Modeling the precedence effect for speech

The human auditory system is capable of perceiving the direction of a sound source despite reflections off of walls, ceiling and other interfering objects. This phenomenon, the precedence effect, has been previously modeled only for synthetic click stimuli. This paper presents a biologically plausible computer simulation for speech, that combines a cross-correlation model with an onset enhancement scheme. As part of the complete model, a novel onset enhancement method is described and implemented, based on adaptive prediction. The adaptive method is promising in its ability to enhance onsets and reduce the steady state portion of the speech signal. The model can serve as a tool to understand the precedence effect for complex signals and should be advantageous to engineering applications such as preprocessors for speech recognition systems.
Practical design of single-ended log-domain filter circuits

Performance limitations and the required trade-offs in the design of single-ended log-domain filter circuits are analyzed. Second-order filters based on a new log-domain integrator circuit are adapted for use in a silicon cochlea. The results demonstrate that detailed simulations are essential for accurately predicting log-domain circuit performance. Analysis shows why instantaneous companding does not lead to high dynamic range in single-ended log-domain filters.
Locally recurrent networks with multiple time-scales

We introduce a new generalized feedforward structure that provides for multiple time scales. The gamma, Laguerre and other locally recurrent feedforward structures perform poorly in cases where widely varying time constants are required. By exponentially varying the time-constant along the delay line, a single delay line is able to represent signals that include various time scales. We demonstrate both discrete- and continuous-time versions of this multiple time-scale structure which we call the multi-scale gamma filter. The multi-scale gamma has a very natural implementation in sub-threshold CMOS and measured impulse responses from a continuous-time analog VLSI chip are shown.
996 Spie Symposium on Visual Communications and Image Processing Adaptive Scale Detection

Most image processing and computer vision algorithms assume knowledge of the proper spatial scale in the image. We propose an automatic technique that adaptively sets the proper spatial scale based on the size of local features in the image. We discuss an analog VLSI circuit implementation that uses this method to automatically determine the proper scale based on a dynamical system model that continuously searches scale space. Though this technique is motivated by analog circuit concepts, we show results for a digital implementation for image enhancements of mamograms. Digital implementations must discretizes the scale space and perform an optimization over this nite set.
Nonuniformity correction using the constant-statistics constraint: analog and digital implementations

Current infra-red focal point arrays (IRFPAs) are limited by their inability to calibrate out component variations. Typically, off-board digital calibration is used to correct nonuniformities in these detector arrays; special calibration images are used to calibrate the system at startup. One-time calibration procedures such as these do not take into account other operating points and will fail to recalibrate for any drift in the parameters. Using clues from neurobiological adaptation, we have developed the constant-statistics (CS) algorithm for nonuniformity correction of IRFPAs. Gain and offset variations are successfully calibrated using simple assumptions of the scene under view. We give results for calibration of 1D and 2D images using a digital implementation. We also show that the constant-statistics algorithm compares favorably to an existing LMS-based nonuniformity correction algorithm by Scribner in terms of convergence rate and computational complexity. Finally, we review the results of analog circuitry that was designed and fabricated with a 2 micrometer CMOS technology. Measured results from our test-chip show that the system achieves invariance to gain and offset variations of the input signal. This hardware is targeted for eventual use for in- and behind- the focal plane implementations.
A neuromorphic microphone for sound localization

We propose a localization model which uses monaural spectral cues for localizing a sound source in a 1D plane. A neuromorphic microphone is constructed to implement this model; its operating principles take advantage of biologically-based monaural cues. The motivation is to understand human sound localization better and to build low-cost stereo capabilities into a single microphone. The head-related transfer function (HRTF) plays a critical role for human monaural localization since the shape of the external ear (pinna) spectrally shapes the sound differently for each sound source direction. Using HRTFs, humans can perceive the difference between front and back and the sound source's different elevation positions using one ear. The neuromorphic microphone relies on a specially shaped reflecting structure that allows echo-time processing to localize the sound. Since our recorded signal is composed of the direct sound and its echo, the sound is a simplified version of actual HRTF recordings which are composed of the direct sound and several reflections. The recorded signal is processed using a gamma filter. This generalizes the standard transversal filter by adding the ability to choose an optimal time-scale. The gamma filter solutions require on the order of five parameters while the more typical FIR filter solutions require hundreds of parameters. A multilayer perceptron is then used to learn the elevation angle of the sound, allowing the microphone to correctly localize sounds.
Delay lines using self-adapting time constants

Transversal filters using ideal tap delay lines are a popular form of short-term memory based filtering in adaptive systems. Some applications where these filters have attained considerable success include system identification, linear prediction, channel equalization and echo cancellation. The gamma filter improves on the simple FIR delay line by allowing the system to choose a single optimal time-constant by minimizing the Mean Squared Error ofthe system. However, in practice it is difficult to determine the optimal value of the time constant since the performance surface is nonconvex. Also, many times a single time constant is not sufficient to well represent the input signal. We propose a nonlinear delay line where each stage of the delay line adapts its time constant so that the average power at the output of the stage is a constant fraction of the power at the input to the stage. Since this adaptation is independent of the Mean Square Error, there are no problems with local minima in the search space. Furthermore, since each stage adapts its own time constant, the delay line is able to represent signals that contain a wide variety of time scales. We discuss both discrete- and continuous-time realizations of this method. Finally, we are developing analog VLSI hardware to implement these nonlinear delay lines. Such an implementation will provide fast, inexpensive, and low-power solutions for many adaptive signal processing applications.
Analog hardware implementation of adaptive filter structures

We have implemented a four-tap adaptive filter in a continuous-time analog VLSI circuit. Since an ideal delay is impossible to implement in continuous-time hardware, we implemented the delay line as a cascade of low-pass filters (called the Gamma filter). Since many years of research in our lab has shown that the Gamma filter outperforms the delay line for a wide range of applications, the Gamma filter should not be considered merely a crude approximation of the ideal delay line. We show measured results from an analog chip that solves the problem of system identification-identifying an unknown linear circuit from its input/output relationship. Furthermore, we believe that a cascade of all-pass filters (called the Laguerre filter) may potentially outperform the Gamma filter and we demonstrate a feedforward Laguerre filter still without adaptive weights.
An Analog Integrated Circuit for Continuous-Time Gain and Offset Calibration of Sensor Arrays

Parameter variations cause unavoidable nonuniformitiesin Infra-Red Focal Plane Arrays and other integrated sensors.Since these nonuniformities change slowly with time, calibratingsensors one-time only is not suitable-much more frequentcalibration is required. We have developed an algorithm thatcontinually calibrates an array of sensors that contain gainand offset variations. This algorithm has been mapped to analoghardware and designed and fabricated with a 2um CMOS technology.Measured results from the chip show that the system achievesinvariance to gain and offset variations of the input signal.
Modeling the Precedence Eeect for Speech

The human auditory system is capable of perceiving the direction of a sound source despite reeections oo of walls, ceiling and other interfering objects. This phenomenon, the precedence eeect, has been previously modeled only for synthetic click stimuli. This paper presents a biologically plausible computer simulation for speech, that combines a cross-correlation model with an onset enhancement scheme. As part of the complete model, a novel onset enhancement method is described and implemented, based on adaptive prediction. The adaptive method is promising in its ability to enhance onsets and reduce the steady state portion of the speech signal. The model can serve as a tool to understand the precedence eeect for complex signals and should be advantageous to engineering applications such as preprocessors for speech recognition systems.
Analog VLSI implementations of continuous-time memory structures

The continuous-time implementation of the popular transversal filter is problematic since it is impossible to implement an ideal time delay in continuous-time hardware. We believe that building an ideal time delay would not be worthwhile anyway since the ideal time delay has shown to be inferior to low-pass (Gamma) and all-pass (Laguerre) stages in discrete-time simulations. We demonstrate feedforward analog VLSI implementations of the Gamma and Laguerre memory structures and show measurements of their performance. We also show through simulation of a system identification problem that the Laguerre filter is expected to converge faster and be more robust to the noise and offsets that we expect in an analog VLSI implementation.
Implementations of Continuous-Time Memory Structures

unsrt Abstract| The continuous-time implementation of the popular transversal lter is problematic since it is impossible to implement an ideal time delay in continuoustime hardware. We believe that building an ideal time delay would not be worthwhile anyway since the ideal time delay has shown to be inferior to low-pass (Gamma) and all-pass (Laguerre) stages in discrete-time simulations. We demonstrate feedforward analog VLSI implementations of the Gamma and Laguerre memory structures and show measurements of their performance. We also show through simulation of a system identi cation problem that the Laguerre lter is expected to converge faster and be more robust to the noise and o sets that we expect in an analog VLSI implementation.