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2013 38th IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2013

作者： Altaf, M. Umair Bin Butko, Taras Juang, Biing-Hwang Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States

ISBN: (纸本)9781479903566

Real world sounds are ubiquitous and form an important part of the edifice of our cognitive abilities. Their perception combines signatures from spectral and temporal domains, among others, yet traditionally their analysis is focused on the frame based spectral properties. We consider the problem of sound analysis from perceptual perspective and investigate the temporal properties of a 'footsteps' sound, which is a particularly challenging from the time-frequency analysis viewpoint. We identify the irregular repetition of the self similarity and the sense of duration as significant to its perceptual quality and extract features using the Teager-Kaiser energy operator. We build an acoustic event detection system for 'footsteps' which shows promising results for detection in cross-environmental conditions when compared with conventional approach. © 2013 IEEE.

关键词： acoustic analysis acoustic signal detection acoustics feature extraction time-frequency analysis

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Person identification using biometric markers from footsteps sound

Person identification using biometric markers from footsteps...

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14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013

Human identification using unobtrusive methods is a challenging problem that has many applications in surveillance tasks. In this work we propose a set of biometric features extracted from a footstep audio signal that can be used to identify a person. Instead of using short-time spectral domain, Teager-Kaiser energy operator is employed to transform a time-domain signal into a representational domain where the intrinsic properties of the walking style of each individual become apparent. We show that these features are biometrically significant as they are physical correlates of human gait. The experimental results obtained using a recently recorded publicly available database show prominent results in a human identification task. Copyright © 2013 ISCA.

关键词： Biometrics

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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation

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Scientific reports 2025年第1期15卷 17791页

作者： Sajed Rakhshani Amirali Arbab Aref Habibi Mohsen Pourazizi Mahnoosh Tajmirriahi Farnaz Sedighin Hossein Rabbani Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran. Department of Ophthalmology Isfahan Eye Research Center Isfahan University of Medical Sciences Isfahan Iran. Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. rabbani.h@***.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet's adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows.

关键词： Denoising Low-SNR OCT Model-aware deep learning Self-supervised learning Super-resolution

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Melody Spotting Using Hidden Markov Models 2

Melody Spotting Using Hidden Markov Models

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2nd Annual International Symposium on Music Information Retrieval, ISMIR 2001

作者： Durey, Adriane Swalm Clements, Mark A. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States

As we acquire more digitized copies of musical recordings, it becomes increasingly necessary to have the assistance of a computer in sorting through the information that it stores. In this paper, we propose a new system for melody-based retrieval of a song from a musical database which adapts wordspotting techniques from automatic speech recognition to create a melody spotting system in the musical domain. This system is tested using a variety of feature sets derived from raw audio data. It results in a successful proof of the melody spotting concept which offers great potential for development into a musical database capable of being queried by melody. © ISMIR 2001 - 2nd Annual International Symposium on Music Information Retrieval. All rights reserved.

关键词： Hidden Markov models

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Hardware-efficient distributed arithmetic architecture for high-order digital filters

Hardware-efficient distributed arithmetic architecture for h...

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2005 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP '05

作者： Yoo, Heejong Anderson, David V. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)0780388747

This paper presents a new memory-efficient distributed arithmetic (DA) architecture for high-order FIR filters. The proposed architecture is based on a memory reduction technique for DA look-up-tables (LUTs);it requires fewer transistors for high-order filters than original LUT-based DA, DA-offset binary coding (DA-OBC), and the LUT-less DA-OBC. Recursive iteration of the memory reduction technique significantly increases the maximum number of filter order implementable on an FPGA platform by not only saving transistor counts, but also balancing hardware usage between logic element (LE) and memory. FPGA implementation results confirm that the proposed DA architecture can implement a 1024-tap FIR filter with significantly smaller area usage (< 50%) than the original LUT-based DA and the LUT-less DA-OBC. © 2005 IEEE.

关键词： Distributed computer systems

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Enhanced speech coding based on phonetic class segmentation

Enhanced speech coding based on phonetic class segmentation

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9th European Conference on Speech Communication and Technology

作者： Durey, Adriane Swalm Krishnan, Venkatesh Barnwell III, Thomas P. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

Given a baseline speech coder and speech with an available phonetic class segmentation, a number of potential enhancements to that coder become possible. While the quality of speech segmentation by phoneme and phonetic class is constantly improving, we use TIMIT to generate phonetic class segmentation as a basis for initial testing of these techniques. Using coders drawn from the MELP family, we explore specialized phonetic codebooks, phonetically-driven superframing, and improved modeling of specific phonetic classes and the transitions between them. We compare the reconstructed speech from these enhancements against the base coder using the metrics of computational cost, transmission cost, and the quality of the reconstructed speech. In most cases, we find that segmentation-based coders can produce speech with quality comparable to that of MELP, using fewer transmitted bits and at no additional computational cost. With phonetic codebooks and transition modeling, CCR tests show these segmentation-based coders produce speech of better quality than is produced by MELP.

关键词： Speech coding

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Using dynamic codebook re-ordering to exploit inter-frame correlation in MELP coders

Using dynamic codebook re-ordering to exploit inter-frame co...

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9th European Conference on Speech Communication and Technology

作者： Krishnan, Venkatesh Barnwell III, Thomas P. Anderson, David V. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

Model based speech coders such as the mixed-excitation linear prediction (MELP) coder encode parameters of the autoregressive model for short-duration frames of the speech signal. Typically, parameters extracted from successive frames by the MELP coder exhibit strong correlation. Reduction in the transmitted data-rates can be achieved if the encoders for these parameters effectively exploit this inter-frame correlation. In this paper, we apply a procedure, called dynamic codebook re-ordering (DCR) to reduce the entropy in the distribution of the symbols generated by the vector quantization encoders used in coding the MELP parameters. The entropy reduction is achieved by exploiting the correlation between the vectors of MELP parameters derived from successive speech frames. The advantages of the DCR procedure over other techniques that exploit inter-frame correlation stem from the fact that it significantly reduces the data-rates without introducing any additional coding delays or increasing the distortion and it is simple and elegant.

关键词： Speech coding

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Parsed and fixed block representations of visual information for image retrieval

Parsed and fixed block representations of visual information...

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Human Vision and Electronic Imaging XIV

作者： Bae, Soo Hyun Juang, Biing-Hwang Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

The theory of linguistics teaches us the existence of a hierarchical structure in linguistic expressions, from letter to word root, and on to word and sentences. By applying syntax and semantics beyond words, one can further recognize the grammatical relationship between among words and the meaning of a sequence of words. This layered view of a spoken language is useful for effective analysis and automated processing. Thus, it is interesting to ask if a similar hierarchy of representation of visual information does exist. A class of techniques that have a similar nature to the linguistic parsing is found in the Lempel-Ziv incremental parsing scheme. Based on a new class of multidimensional incremental parsing algorithms extended from the Lempel-Ziv incremental parsing, a new framework for image retrieval, which takes advantage of the source characterization property of the incremental parsing algorithm, was proposed recently. With the incremental parsing technique, a given image is decomposed into a number of patches, called a parsed representation. This representation can be thought of as a morphological interface between elementary pixel and a higher level representation. In this work, we examine the properties of two-dimensional parsed representation in the context of imagery information retrieval and in contrast to vector quantization;i.e. fixed square-block representations and minimum average distortion criteria. We implemented four image retrieval systems for the comparative study;three, called IPSILON image retrieval systems, use parsed representation with different perceptual distortion thresholds and one uses the convectional vector quantization for visual pattern analysis. We observe that different perceptual distortion in visual pattern matching does not have serious effects on the retrieval precision although allowing looser perceptual thresholds in image compression result poor reconstruction fidelity. We compare the effectiveness of the use of the pars

关键词： image retrieval

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Hidden Markov modelling for SAR automatic target recognition

Hidden Markov modelling for SAR automatic target recognition

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1998 23rd IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 1998

作者： Nilubol, Chanin Pham, Quoc H. Mersereau, Russell M. Smith, Mark J. T. Clements, Mark A. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)0780344286

This paper discusses the application of hidden Markov models (HMMs) to solve the translational and rotational invariant automatic target recognition (TRIATR) problem associated with SAR imagery. This approach is based on a cascade of these stages: preprocessing, feature extraction and selection, and classification. Preprocessing and feature extraction and selection involve successive applications of extraction operations from measurements of the Radon transform of target chips. The features which are invariant to changes in rotation, position and shifts, although not to changes in scale are optimized through the use of feature selection techniques. The classification stage successively takes as its inputs the multidimensional multiple observation sequences, parameterizes them statistically using continuous density models to capture target and background appearance variability, and thus results in the TRIATR-HMMs. Experimental results have demonstrated that the recognition rate is as high as 99% over both the training set and the testing set. © 1998 IEEE.

关键词： Automatic target recognition

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Application of a two-stage algorithm for adaptive detection in hyperspectral imaging

Application of a two-stage algorithm for adaptive detection ...

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2005 IEEE/SP 13th Workshop on Statistical signal processing

作者： Farrell Jr., Michael D. Mersereau, Russell M. Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)0780394046

Adaptive detection has a rich history in the radar community, and a number of other areas have borrowed heavily from constructs developed in this field. The task of target detection in hyperspectral imaging (HSI) is one such area that has recently begun to take advantage of parallels from the field of radar array processing. While there are key differences between the physical setup and data collected by radar and hyperspectral systems, the formulation of the adaptive detection problem is remarkably similar. In this paper we apply a two-stage detection approach originally developed for STAP in airborne radar to adaptive target detection in hyperspectral imaging. Touching first on the component algorithms involved, and then on their similarities and differences, we show that this two-stage approach has an interesting conceptual interpretation for HSI that comes from the multi-dimensional Euclidean geometry of the spectral measurement space. However, due to the nature of hyperspectral target signatures and background statistics, we propose that there are many scenarios where HSI detection may be better served by reversing the order of stages applied to the data as laid out in previous formulations. Detection results for ground targets are presented to illustrate the potential of this new two-stage approach in hyperspectral adaptive detection. ©2005 IEEE.

关键词： Adaptive algorithms

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