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2 edition of Low-power hardware implementation of sound localization and speech separation. found in the catalog.

Low-power hardware implementation of sound localization and speech separation.

David Halupka

Low-power hardware implementation of sound localization and speech separation.

by David Halupka

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Published .
Written in English


About the Edition

This thesis presents a low-power (3.45 mW) hardware implementation of a sound localization and speech separation system. Sound localization is the process of estimating the location of a sound source using information gathered by two or more microphones. Speech separation is the process of extracting a speech signal of interest from a noisy recording.For the sound localization sub-system, low power consumption is achieved through a power analysis of an earlier design. This analysis shows that almost 75% (21.65 mW) of power is spent by the repetitive evaluation of cos(o) during the sound localization search. We have reduced this power consumption by quantizing the cosine function to seven levels; a power savings of 98.75% is achieved with insignificant change in localization error.For the speech separation sub-system, low power consumption is achieved by sharing common processing blocks with the proposed sound localization sub-system. Moreover, by taking advantage of the leniency in the chosen speech separation algorithm (time-frequency masking) significant power savings are achieved through the use of a relatively coarse approximation of this algorithm. The proposed speech separation architecture performs similarly to the ideal (algorithmic floating point) implementation, in both noise reduction and speech recognition experiments.

The Physical Object
Pagination100 leaves.
Number of Pages100
ID Numbers
Open LibraryOL20238257M
ISBN 100494022043

ple, low-power implementation such that the power consump-tion of the end-to-end system, which includes a voice activity detector, feature extraction front-end, and back-end decision Speech signals, denoted by s(t), can be modeled as a time-domain convolution between the excitation signal e(t) and the. Udit Gupta, Samuel Hsia, Vikram Saraph, Xiaodong Wang, Brandon Reagen, Gu-Yeon Wei, Hsien-Hsin S. Lee, David Brooks, Carole-Jean Wu DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference Conference Forthcoming. The 47th IEEE/ACM International Symposium on Computer Architecture (ISCA ), Forthcoming. Abstract | Links | BibTeX.

The authors' goal in writing this book is set out in the preface: “ the fundamental goal of the book would be to provide a theoretically sound, technically accurate, and reasonably complete description of the basic knowledge and ideas that constitute a modern system for speech recognition by machine” (p. () Efficient and Flexible Low-Power NTT for Lattice-Based Cryptography. IEEE International Symposium on Hardware Oriented Security and Trust (HOST), Cited by:

Embedded Systems: A Contemporary Design Tool, Second Edition introduces you to the theoretical hardware and software foundations of these systems and expands into the areas of signal integrity, system security, low power, and hardware-software co-design. The text builds upon earlier material to show you how to apply reliable, robust solutions. In the localization of sound sources, both the FOA-tracked binaural and the FOA-2D speaker array exhibited higher spatial acoustic fidelity than FOA-static binaural. Regarding the environment-related spatial quality attributes, the 2D speaker array reproduction was perceived as more immersive and realistic than other reproduction methods.


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Low-power hardware implementation of sound localization and speech separation by David Halupka Download PDF EPUB FB2

This paper proposes an integrated sound localization and classification system based on the human auditory system and a respective compact hardware implementation. Binaural localization and separation techniques but at the expense of degrading the ability of the system to separate the speech from the other sound sources.

low power consumption) and Author: Harald Viste. implement speech recognition in hardware. These efforts, however, are either quite dated [2], limited in performance or scope [3,4], or do not consider power consumption [5]. In this paper we therefore propose a low-power hardware search architecture to achieve high-performance speech recognition in silicon.

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human by: Stephanie Seneff's health-related publications can be found by visiting her Computer Science and Artificial Intelligence Laboratory home page here.

Drexler and J. Glass, "Explicit Alignment of Text and Speech Encodings for Attention-Based End-to-End Speech Recognition," Proc. ASRU, pp.Sentosa, Singapore, December CPU resources, with possibly no degradation of speech recognition quality when compared to standard floating-point implementations.

The ETSI distributed speech recognition front-end standard is implemented on an ultra low-power miniature DSP system.

The efficient implementation of the ETSI algorithm components, i.e. Human auditory system uses such localization cues for estimating the sound direction. HRTF are used in two ways.

One way is for synthesizing binaural sound (virtual audio 3-D). The second way is for analyzing binaural sounds in order to estimate the localization of sound sources. Therefore HRTF are important and useful data for researchers. Aarabi P () The fusion of distributed microphone arrays for sound localization, EURASIP Journal on Advances in Signal Processing,(), Online publication date: 1-Jan Horner S and Holls W () An effective technique for enhancing an intrauterine catheter fetal electrocardiogram, EURASIP Journal on Advances in Signal.

"A Learning Parallel Analog-to-Digital Vector Quantizer," J. Lubkin and G. Cauwenberghs, Journal of Circuits, Systems and Computers (special issue on analog and digital arrays), vol. 8 (), pp."An Analog VLSI Chip with Asynchronous Interface for Auditory Feature Extraction," N.

Kumar, W. Himmelbauer, G. Cauwenberghs and A. Andreou, IEEE Trans. Circuits and Systems II: Analog.

GBIT/S W HYPERSPECTRAL IMAGE ENCODERS ON A LOW-POWER PARALLEL HETEROGENEOUS PROCESSING PLATFORM: 2D-to-2D Mask Estimation for Speech Enhancement based on Fully Convolutional Neural Network: 3-D ACOUSTIC MODELING FOR FAR-FIELD MULTI-CHANNEL SPEECH RECOGNITION: 3D DEFORMATION SIGNATURE.

In recent years, advances in Micro ElectroMechanical Systems (MEMS) microphone technology and acoustic beamforming techniques allow for enhanced sound source localization in both acoustic and ultrasound frequency range [1,2,3].Sound source localization based on microphone arrays have emerged and are used in various applications; ranging from ultrasound source localization [], speech Author: Laurent Segers, Jurgen Vandendriessche, Thibaut Vandervelden, Benjamin Johan Lapauw, Bruno da Silva.

Sound processing applications such as sound source mapping, source separation and localization have several important characteristics that the system must meet – such as array spatial resolution, low reverberation and real-time data acquisition and by: 3.

An efficient algorithm and its corresponding VLSI architecture for the critical-band transform (CBT) are developed to approximate the critical-band filtering of the human ear.

The CBT consists of a constant-bandwidth transform in the lower frequency range and a Brown constant- transform (CQT) in the higher frequency range. The corresponding VLSI architecture is proposed to achieve Cited by: 1.

Sound processing applications such as sound source mapping, source separation, and localization have several important characteristics that the system must meet, such as array spatial resolution, low reverberation, and real-time data acquisition and by: 3. "Gradient Flow Adaptive Beamforming and Signal Separation in a Miniature Microphone Array," M.

Stanacevic, G. Cauwenberghs and G. Zweig, Proc. IEEE Int. Conf. Acoustics Speech and Signal Processing (ICASSP'), Orlando FL, MayChapter 12 of the book "Multimedia Signal Processing: Theory and Applications in Speech, Music and Communications" - Musical Instruments - A Review of Basic Physics of Sound - Music Signal Features and Models - Ear: Hearing of Sounds - Psychoacoustics of Hearing - Music Compression - High Quality Music Coding: MPEG - Stereo Music - Music Recognition.

International Journal of Engineering and Advanced Technology (IJEAT) covers topics in the field of Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences.

I and II). In Block I, after the speech segmentation described by Fig. 1, if the processed frame () contains speech (the assigned value from the VAD is equal to 1), the speech signal is Hanning-windowed and this frame is used for the fea-ture extraction being further processed in Block II.

Anne F. Keller, Jean-Marie C. Bouteiller, Theodore W. Berger. Computational exploration of NMDA receptors. Book Chapter in the book Methods in Molecular Biology: NMDA receptors, Springer. Calcium Hypothesis of Alzheimer’s disease and brain aging: A framework for integrating new evidence into a comprehensive theory of pathogenesis.

Lyon's auditory model inversion: a tool for sound separation and speech enhancement, in Proceedings of the ESCA Workshop on the Auditory Basis of Speech Perception (Keele:). Fairhall A, L., Lewen G. D., Bialek W., de Ruyter van Steveninck R. Efficiency and ambiguity in an adaptive neural by: 5.

Applications of DFT/FFT. Spectral estimation. P. T. Gough, “A fast spectral estimation algorithm based on FFT,” IEEE Trans. SP, vol. 42, pp.June Masking threshold for the psychoacoustic model is derived from an estimate of the power density spectrum obtained from a point FFT.

Used in MPEG Audio Coding. CD See.Julián P, Andreou A, Mandolesi P, Goldberg D (). A low-power CMOS integrated circuit for bearing estimation.

Proceedings - IEEE International Symposium on Circuits and Systems. 5. Julián P, Andreou AG, Riddle L, Shamma S, Cauwenberghs G (). A comparison of algorithms for sound localization.Publications: Douglas L.

Jones's Group This page is still under construction. Our publications on Joint-Source Channel Coding are available at