Energy-based multi-speaker voice activity detection with an ad hoc microphone array
|
|
- Cecilia Shields
- 7 years ago
- Views:
Transcription
1 Energy-based multi-speaker voice activity detection with an ad hoc microphone array Alexander Bertrand Marc Moonen Department of Electrical Engineering (ESAT) Katholieke Universiteit Leuven ICASSP 2010 A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
2 Outline 1 Motivation Problem statement Data model 2 Solving non-negative BSS (NBSS) NBSS with well-grounded sources M-NICA 3 Results A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
3 Outline Motivation Problem statement 1 Motivation Problem statement Data model 2 Solving non-negative BSS (NBSS) NBSS with well-grounded sources M-NICA 3 Results A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
4 Problem statement Motivation Problem statement : speech source : microphone Goal: individual voice activity detection (VAD) for multiple simultaneous speakers Ad-hoc microphone array Assumptions: Speakers in near-field (speech power varies over microphones) Speakers mutually independent Limited noise, and limited reverberance A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
5 Problem statement Motivation Problem statement : speech source : microphone Goal: individual voice activity detection (VAD) for multiple simultaneous speakers Ad-hoc microphone array Assumptions: Speakers in near-field (speech power varies over microphones) Speakers mutually independent Limited noise, and limited reverberance A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
6 Problem statement Motivation Problem statement : speech source : microphone Goal: individual voice activity detection (VAD) for multiple simultaneous speakers Ad-hoc microphone array Assumptions: Speakers in near-field (speech power varies over microphones) Speakers mutually independent Limited noise, and limited reverberance A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
7 Problem statement Motivation Problem statement Advantages: Array geometry unknown Speaker positions unknown Energy-based low data rate synchronization sampling clocks not crucial By-product: power of each speaker at each microphone Applications: Binaural hearing aids (head shadow) Video conferencing Ad hoc acoustic sensor networks... A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
8 Problem statement Motivation Problem statement Advantages: Array geometry unknown Speaker positions unknown Energy-based low data rate synchronization sampling clocks not crucial By-product: power of each speaker at each microphone Applications: Binaural hearing aids (head shadow) Video conferencing Ad hoc acoustic sensor networks... A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
9 Outline Motivation Data model 1 Motivation Problem statement Data model 2 Solving non-negative BSS (NBSS) NBSS with well-grounded sources M-NICA 3 Results A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
10 Data model Motivation Data model N speakers, J microphones, J N Speech signal n: s n [t] Microphone signal j: ỹ j [t] Instantaneous speech power (L=block length, k =frame index): s n [k] = 1 L 1 s n [kl + l] 2 L l=0 Instantaneous microphone signal power: y j [k] = 1 L 1 ỹ j [kl + l] 2 L l=0 Stack s n [k] and y j [k] in s[k] and y[k] resp. Data model: y[k] As[k], k N A is a J N mixing matrix A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
11 Data model Motivation Data model N speakers, J microphones, J N Speech signal n: s n [t] Microphone signal j: ỹ j [t] Instantaneous speech power (L=block length, k =frame index): s n [k] = 1 L 1 s n [kl + l] 2 L l=0 Instantaneous microphone signal power: y j [k] = 1 L 1 ỹ j [kl + l] 2 L l=0 Stack s n [k] and y j [k] in s[k] and y[k] resp. Data model: y[k] As[k], k N A is a J N mixing matrix A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
12 Data model Motivation Data model N speakers, J microphones, J N Speech signal n: s n [t] Microphone signal j: ỹ j [t] Instantaneous speech power (L=block length, k =frame index): s n [k] = 1 L 1 s n [kl + l] 2 L l=0 Instantaneous microphone signal power: y j [k] = 1 L 1 ỹ j [kl + l] 2 L l=0 Stack s n [k] and y j [k] in s[k] and y[k] resp. Data model: y[k] As[k], k N A is a J N mixing matrix A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
13 Data model Motivation Data model N speakers, J microphones, J N Speech signal n: s n [t] Microphone signal j: ỹ j [t] Instantaneous speech power (L=block length, k =frame index): s n [k] = 1 L 1 s n [kl + l] 2 L l=0 Instantaneous microphone signal power: y j [k] = 1 L 1 ỹ j [kl + l] 2 L l=0 Stack s n [k] and y j [k] in s[k] and y[k] resp. Data model: y[k] As[k], k N A is a J N mixing matrix A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
14 Data model Motivation Data model y[k] As[k], k N Remarks: Assumes independence of sources and no reverberation good choice of L Trade-off: size of L (time resolution vs. model mismatch) Noise (incorporate in s or subtract) Goal: find s (and A) track power of each source = blind source separation problem with non-negative source signals (NBSS) A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
15 Data model Motivation Data model y[k] As[k], k N Remarks: Assumes independence of sources and no reverberation good choice of L Trade-off: size of L (time resolution vs. model mismatch) Noise (incorporate in s or subtract) Goal: find s (and A) track power of each source = blind source separation problem with non-negative source signals (NBSS) A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
16 Data model Motivation Data model y[k] As[k], k N Remarks: Assumes independence of sources and no reverberation good choice of L Trade-off: size of L (time resolution vs. model mismatch) Noise (incorporate in s or subtract) Goal: find s (and A) track power of each source = blind source separation problem with non-negative source signals (NBSS) A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
17 Outline Solving non-negative BSS (NBSS) NBSS with well-grounded sources 1 Motivation Problem statement Data model 2 Solving non-negative BSS (NBSS) NBSS with well-grounded sources M-NICA 3 Results A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
18 Solving non-negative BSS (NBSS) NBSS with well-grounded sources NBSS with well-grounded sources Exploit non-negativity simpler algorithms (compared to classic ICA) Exploit well-groundedness of source signals (non-vanishing pdf at zero) s: well-grounded due to on-off behavior of speech Possible choice of algorithm: Non-negative PCA (NPCA) 1 Avoid step size search: Multiplicative non-negative ICA (M-NICA) 2 1 E. Oja and M. Plumbley, Blind separation of positive sources using non-negative PCA, in Proc. of the 4th international Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, A. Bertrand and M. Moonen, Blind separation of non-negative source signals using multiplicative updates and subspace projection, accepted for publication in Signal Processing. A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
19 Solving non-negative BSS (NBSS) NBSS with well-grounded sources NBSS with well-grounded sources Exploit non-negativity simpler algorithms (compared to classic ICA) Exploit well-groundedness of source signals (non-vanishing pdf at zero) s: well-grounded due to on-off behavior of speech Possible choice of algorithm: Non-negative PCA (NPCA) 1 Avoid step size search: Multiplicative non-negative ICA (M-NICA) 2 1 E. Oja and M. Plumbley, Blind separation of positive sources using non-negative PCA, in Proc. of the 4th international Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, A. Bertrand and M. Moonen, Blind separation of non-negative source signals using multiplicative updates and subspace projection, accepted for publication in Signal Processing. A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
20 Solving non-negative BSS (NBSS) NBSS with well-grounded sources NBSS with well-grounded sources Exploit non-negativity simpler algorithms (compared to classic ICA) Exploit well-groundedness of source signals (non-vanishing pdf at zero) s: well-grounded due to on-off behavior of speech Possible choice of algorithm: Non-negative PCA (NPCA) 1 Avoid step size search: Multiplicative non-negative ICA (M-NICA) 2 1 E. Oja and M. Plumbley, Blind separation of positive sources using non-negative PCA, in Proc. of the 4th international Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, A. Bertrand and M. Moonen, Blind separation of non-negative source signals using multiplicative updates and subspace projection, accepted for publication in Signal Processing. A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
21 Solving non-negative BSS (NBSS) NBSS with well-grounded sources NBSS with well-grounded sources Exploit non-negativity simpler algorithms (compared to classic ICA) Exploit well-groundedness of source signals (non-vanishing pdf at zero) s: well-grounded due to on-off behavior of speech Possible choice of algorithm: Non-negative PCA (NPCA) 1 Avoid step size search: Multiplicative non-negative ICA (M-NICA) 2 1 E. Oja and M. Plumbley, Blind separation of positive sources using non-negative PCA, in Proc. of the 4th international Symposium on Independent Component Analysis and Blind Signal Separation (ICA2003), Nara, Japan, A. Bertrand and M. Moonen, Blind separation of non-negative source signals using multiplicative updates and subspace projection, accepted for publication in Signal Processing. A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
22 Outline Solving non-negative BSS (NBSS) M-NICA 1 Motivation Problem statement Data model 2 Solving non-negative BSS (NBSS) NBSS with well-grounded sources M-NICA 3 Results A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
23 M-NICA Solving non-negative BSS (NBSS) M-NICA Main idea An orthogonal mixture of non-negative, well-grounded, independent signals that preserves non-negativity, is a permutation of the original signals [M. Plumbley, 2002] M-NICA: Idea: 1 decorrelate + preserve non-negativity 2 restore signal subspace (projection step) Multiplicative updating: preserves non-negativity no user-defined learning rate Notation: S, Y: M samples of s[k], y[k] in columns, i.e. Y = AS S: mean of rows of S, i.e. S = 1 M S 1 1T A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
24 M-NICA Solving non-negative BSS (NBSS) M-NICA Main idea An orthogonal mixture of non-negative, well-grounded, independent signals that preserves non-negativity, is a permutation of the original signals [M. Plumbley, 2002] M-NICA: Idea: 1 decorrelate + preserve non-negativity 2 restore signal subspace (projection step) Multiplicative updating: preserves non-negativity no user-defined learning rate Notation: S, Y: M samples of s[k], y[k] in columns, i.e. Y = AS S: mean of rows of S, i.e. S = 1 M S 1 1T A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
25 M-NICA Solving non-negative BSS (NBSS) M-NICA Main idea An orthogonal mixture of non-negative, well-grounded, independent signals that preserves non-negativity, is a permutation of the original signals [M. Plumbley, 2002] M-NICA: Idea: 1 decorrelate + preserve non-negativity 2 restore signal subspace (projection step) Multiplicative updating: preserves non-negativity no user-defined learning rate Notation: S, Y: M samples of s[k], y[k] in columns, i.e. Y = AS S: mean of rows of S, i.e. S = 1 M S 1 1T A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
26 Solving non-negative BSS (NBSS) M-NICA The M-NICA algorithm (batch) 1 Initialize S Y 1:N,: 2 Decorrelation step: (preserves non-negativity) n = 1... N, m = 1... M : [ ] SS T D 1 [S 1 S + SST D 1 1 S + D 2S nm ] nm [S] nm [ ] SS T D 1 1 S + SST D 1 1 S + D 2S nm ERRATUM: switch nominator and denominator in paper! 3 Signal subspace projection step: 4 Return to step 2. n = 1... N, m = 1... M : ( [Prowspan{Y} [S] nm max S ] ) nm, 0 (PS: batch mode) A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
27 Results Results : speech source : microphone cubical room: 5m x 5m x 5m L = 480 (i.e. 30ms) Sliding window with length K = Assessment by signal-to-error ratio (SER): SER = 1 k 10 log ([A] jn s n[k]) 2 JN 10 ] j,n k [Â ( ŝ n [k] [A] jn s n [k]) 2 A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20 jn
28 Results Results 10 8 Original source energy Estimated source energy by M NICA time [s] SER [db] M NICA NPCA η=0.5 NPCA η=1 NPCA η=1.5 NPCA η= time [s] A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
29 Results Results 10 8 Original source energy (source 1) Estimated source energy by M NICA Original source energy (source 2) Estimated source energy by M NICA Original source energy (source 3) Estimated source energy by M NICA A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
30 Results Effect of reverberation L=480 L= SER [db] Reflection coefficient A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
31 Results Effect of residual noise SER [db] SNR in best microphone [db] A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
32 Results Reconstruction (limited reverberation, limited noise) 10 5 no reverberance, no residual noise Original source energy (source 1) Estimated source energy by M NICA reflection coefficient = 0.7 Original source energy (source 1) Estimated source energy by M NICA residual noise with SNR of 5 db in best microphone Original source energy (source 1) Estimated source energy by M NICA A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
33 Summary Summary Track power of simultaneous speakers Ad-hoc microphone array (unknown geometry) Energy based: near-field low data rate weak synchronization constraints Solve as Non-negative BSS algorithm: M-NICA A. Bertrand, M.Moonen (K.U.Leuven) Multi-speaker VAD with ad hoc array ICASSP / 20
BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION
BLIND SOURCE SEPARATION OF SPEECH AND BACKGROUND MUSIC FOR IMPROVED SPEECH RECOGNITION P. Vanroose Katholieke Universiteit Leuven, div. ESAT/PSI Kasteelpark Arenberg 10, B 3001 Heverlee, Belgium Peter.Vanroose@esat.kuleuven.ac.be
More informationThese axioms must hold for all vectors ū, v, and w in V and all scalars c and d.
DEFINITION: A vector space is a nonempty set V of objects, called vectors, on which are defined two operations, called addition and multiplication by scalars (real numbers), subject to the following axioms
More informationAdvanced Speech-Audio Processing in Mobile Phones and Hearing Aids
Advanced Speech-Audio Processing in Mobile Phones and Hearing Aids Synergies and Distinctions Peter Vary RWTH Aachen University Institute of Communication Systems WASPAA, October 23, 2013 Mohonk Mountain
More informationSTUDY OF MUTUAL INFORMATION IN PERCEPTUAL CODING WITH APPLICATION FOR LOW BIT-RATE COMPRESSION
STUDY OF MUTUAL INFORMATION IN PERCEPTUAL CODING WITH APPLICATION FOR LOW BIT-RATE COMPRESSION Adiel Ben-Shalom, Michael Werman School of Computer Science Hebrew University Jerusalem, Israel. {chopin,werman}@cs.huji.ac.il
More informationChapter 6. Linear Programming: The Simplex Method. Introduction to the Big M Method. Section 4 Maximization and Minimization with Problem Constraints
Chapter 6 Linear Programming: The Simplex Method Introduction to the Big M Method In this section, we will present a generalized version of the simplex method that t will solve both maximization i and
More informationHow to Improve the Sound Quality of Your Microphone
An Extension to the Sammon Mapping for the Robust Visualization of Speaker Dependencies Andreas Maier, Julian Exner, Stefan Steidl, Anton Batliner, Tino Haderlein, and Elmar Nöth Universität Erlangen-Nürnberg,
More informationWavelet analysis. Wavelet requirements. Example signals. Stationary signal 2 Hz + 10 Hz + 20Hz. Zero mean, oscillatory (wave) Fast decay (let)
Wavelet analysis In the case of Fourier series, the orthonormal basis is generated by integral dilation of a single function e jx Every 2π-periodic square-integrable function is generated by a superposition
More informationRECORDING AND CAPTURING SOUND
12 RECORDING AND CAPTURING SOUND 12.1 INTRODUCTION Recording and capturing sound is a complex process with a lot of considerations to be made prior to the recording itself. For example, there is the need
More informationNon-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning
Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step
More informationRecent advances in Digital Music Processing and Indexing
Recent advances in Digital Music Processing and Indexing Acoustics 08 warm-up TELECOM ParisTech Gaël RICHARD Telecom ParisTech (ENST) www.enst.fr/~grichard/ Content Introduction and Applications Components
More informationGroup Testing a tool of protecting Network Security
Group Testing a tool of protecting Network Security Hung-Lin Fu 傅 恆 霖 Department of Applied Mathematics, National Chiao Tung University, Hsin Chu, Taiwan Group testing (General Model) Consider a set N
More informationBlind Source Separation for Robot Audition using Fixed Beamforming with HRTFs
Blind Source Separation for Robot Audition using Fixed Beamforming with HRTFs Mounira Maazaoui, Yves Grenier, Karim Abed-Meraim To cite this version: Mounira Maazaoui, Yves Grenier, Karim Abed-Meraim.
More informationEnhancing Wireless Security with Physical Layer Network Cooperation
Enhancing Wireless Security with Physical Layer Network Cooperation Amitav Mukherjee, Ali Fakoorian, A. Lee Swindlehurst University of California Irvine The Physical Layer Outline Background Game Theory
More informationSpeech Signal Processing: An Overview
Speech Signal Processing: An Overview S. R. M. Prasanna Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati December, 2012 Prasanna (EMST Lab, EEE, IITG) Speech
More informationTRAFFIC MONITORING WITH AD-HOC MICROPHONE ARRAY
4 4th International Workshop on Acoustic Signal Enhancement (IWAENC) TRAFFIC MONITORING WITH AD-HOC MICROPHONE ARRAY Takuya Toyoda, Nobutaka Ono,3, Shigeki Miyabe, Takeshi Yamada, Shoji Makino University
More informationLog-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network
Recent Advances in Electrical Engineering and Electronic Devices Log-Likelihood Ratio-based Relay Selection Algorithm in Wireless Network Ahmed El-Mahdy and Ahmed Walid Faculty of Information Engineering
More informationComponent Ordering in Independent Component Analysis Based on Data Power
Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals
More informationEXPLOIT THE SCALE OF BIG DATA FOR DATA PRIVACY: AN EFFICIENT SCHEME BASED ON DISTANCE-PRESERVING ARTIFICIAL NOISE AND SECRET MATRIX TRANSFORM
EXPLOIT THE SCALE OF BIG DATA FOR DATA PRIVACY: AN EFFICIENT SCHEME BASED ON DISTANCE-PRESERVING ARTIFICIAL NOISE AND SECRET MATRIX TRANSFORM Xiaohua Li and Zifan Zhang Department of Electrical and Computer
More informationA STUDY OF ECHO IN VOIP SYSTEMS AND SYNCHRONOUS CONVERGENCE OF
A STUDY OF ECHO IN VOIP SYSTEMS AND SYNCHRONOUS CONVERGENCE OF THE µ-law PNLMS ALGORITHM Laura Mintandjian and Patrick A. Naylor 2 TSS Departement, Nortel Parc d activites de Chateaufort, 78 Chateaufort-France
More informationA secure face tracking system
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 10 (2014), pp. 959-964 International Research Publications House http://www. irphouse.com A secure face tracking
More informationEvolutionary denoising based on an estimation of Hölder exponents with oscillations.
Evolutionary denoising based on an estimation of Hölder exponents with oscillations. Pierrick Legrand,, Evelyne Lutton and Gustavo Olague CICESE, Research Center, Applied Physics Division Centro de Investigación
More informationWhat Audio Engineers Should Know About Human Sound Perception. Part 2. Binaural Effects and Spatial Hearing
What Audio Engineers Should Know About Human Sound Perception Part 2. Binaural Effects and Spatial Hearing AES 112 th Convention, Munich AES 113 th Convention, Los Angeles Durand R. Begault Human Factors
More informationL9: Cepstral analysis
L9: Cepstral analysis The cepstrum Homomorphic filtering The cepstrum and voicing/pitch detection Linear prediction cepstral coefficients Mel frequency cepstral coefficients This lecture is based on [Taylor,
More informationWater Leakage Detection in Dikes by Fiber Optic
Water Leakage Detection in Dikes by Fiber Optic Jerome Mars, Amir Ali Khan, Valeriu Vrabie, Alexandre Girard, Guy D Urso To cite this version: Jerome Mars, Amir Ali Khan, Valeriu Vrabie, Alexandre Girard,
More informationGrasshopper3 U3. Point Grey Research Inc. 12051 Riverside Way Richmond, BC Canada V6W 1K7 T (604) 242-9937 www.ptgrey.com
Grasshopper3 U3 USB 3.0 Camera Imaging Performance Specification Version 12.0 Point Grey Research Inc. 12051 Riverside Way Richmond, BC Canada V6W 1K7 T (604) 242-9937 www.ptgrey.com Copyright 2012-2015
More informationCommunication on the Grassmann Manifold: A Geometric Approach to the Noncoherent Multiple-Antenna Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 48, NO. 2, FEBRUARY 2002 359 Communication on the Grassmann Manifold: A Geometric Approach to the Noncoherent Multiple-Antenna Channel Lizhong Zheng, Student
More informationSearch keywords: Connect, Meeting, Collaboration, Voice over IP, VoIP, Acoustic Magic, audio, web conferencing, microphone, best practices
Title: Acoustic Magic Voice Tracker II array microphone improves operation with VoIP based Adobe Connect Meeting URL: www.acousticmagic.com By: Bob Feingold, President, Acoustic Magic Inc. Search keywords:
More information1 Introduction to Matrices
1 Introduction to Matrices In this section, important definitions and results from matrix algebra that are useful in regression analysis are introduced. While all statements below regarding the columns
More informationAudio Engineering Society. Convention Paper. Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA
Audio Engineering Society Convention Paper Presented at the 129th Convention 2010 November 4 7 San Francisco, CA, USA The papers at this Convention have been selected on the basis of a submitted abstract
More informationLuigi Piroddi Active Noise Control course notes (January 2015)
Active Noise Control course notes (January 2015) 9. On-line secondary path modeling techniques Luigi Piroddi piroddi@elet.polimi.it Introduction In the feedforward ANC scheme the primary noise is canceled
More informationControl 2004, University of Bath, UK, September 2004
Control, University of Bath, UK, September ID- IMPACT OF DEPENDENCY AND LOAD BALANCING IN MULTITHREADING REAL-TIME CONTROL ALGORITHMS M A Hossain and M O Tokhi Department of Computing, The University of
More informationA NOVEL DETERMINISTIC METHOD FOR LARGE-SCALE BLIND SOURCE SEPARATION
A NOVEL DETERMINISTIC METHOD FOR LARGE-SCALE BLIND SOURCE SEPARATION Martijn Boussé Otto Debals Lieven De Lathauwer Department of Electrical Engineering (ESAT, KU Leuven, Kasteelpark Arenberg 10, 3001
More informationUNIVERSAL SPEECH MODELS FOR SPEAKER INDEPENDENT SINGLE CHANNEL SOURCE SEPARATION
UNIVERSAL SPEECH MODELS FOR SPEAKER INDEPENDENT SINGLE CHANNEL SOURCE SEPARATION Dennis L. Sun Department of Statistics Stanford University Gautham J. Mysore Adobe Research ABSTRACT Supervised and semi-supervised
More informationApplication Notes. Contents. Overview. Introduction. Echo in Voice over IP Systems VoIP Performance Management
Application Notes Title Series Echo in Voice over IP Systems VoIP Performance Management Date January 2006 Overview This application note describes why echo occurs, what effects it has on voice quality,
More informationON THE DEGREES OF FREEDOM OF SIGNALS ON GRAPHS. Mikhail Tsitsvero and Sergio Barbarossa
ON THE DEGREES OF FREEDOM OF SIGNALS ON GRAPHS Mikhail Tsitsvero and Sergio Barbarossa Sapienza Univ. of Rome, DIET Dept., Via Eudossiana 18, 00184 Rome, Italy E-mail: tsitsvero@gmail.com, sergio.barbarossa@uniroma1.it
More informationInstalling and Configuring TrueConf Client Application for OS X
Installing and Configuring TrueConf Client Application for OS X 1. How to install the client application? 2. How to authorize? 3. How to configure the application? «TrueConf Client» Menu «About TrueConf
More information8. Linear least-squares
8. Linear least-squares EE13 (Fall 211-12) definition examples and applications solution of a least-squares problem, normal equations 8-1 Definition overdetermined linear equations if b range(a), cannot
More informationVoice Communication Package v7.0 of front-end voice processing software technologies General description and technical specification
Voice Communication Package v7.0 of front-end voice processing software technologies General description and technical specification (Revision 1.0, May 2012) General VCP information Voice Communication
More informationLecture 5 Least-squares
EE263 Autumn 2007-08 Stephen Boyd Lecture 5 Least-squares least-squares (approximate) solution of overdetermined equations projection and orthogonality principle least-squares estimation BLUE property
More information4.3 Least Squares Approximations
18 Chapter. Orthogonality.3 Least Squares Approximations It often happens that Ax D b has no solution. The usual reason is: too many equations. The matrix has more rows than columns. There are more equations
More informationConference interpreting with information and communication technologies experiences from the European Commission DG Interpretation
Jose Esteban Causo, European Commission Conference interpreting with information and communication technologies experiences from the European Commission DG Interpretation 1 Introduction In the European
More informationEricsson T18s Voice Dialing Simulator
Ericsson T18s Voice Dialing Simulator Mauricio Aracena Kovacevic, Anna Dehlbom, Jakob Ekeberg, Guillaume Gariazzo, Eric Lästh and Vanessa Troncoso Dept. of Signals Sensors and Systems Royal Institute of
More informationPERCENTAGE ARTICULATION LOSS OF CONSONANTS IN THE ELEMENTARY SCHOOL CLASSROOMS
The 21 st International Congress on Sound and Vibration 13-17 July, 2014, Beijing/China PERCENTAGE ARTICULATION LOSS OF CONSONANTS IN THE ELEMENTARY SCHOOL CLASSROOMS Dan Wang, Nanjie Yan and Jianxin Peng*
More informationVPAT Summary. VPAT Details. Section 1194.22 Web-based Internet information and applications - Detail
Date: October 8, 2014 Name of Product: System x3755 M3 VPAT Summary Criteria Status Remarks and Explanations Section 1194.21 Software Applications and Operating Systems Section 1194.22 Web-based Internet
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS Systems of Equations and Matrices Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a
More informationUser Manual. Please read this manual carefully before using the Phoenix Octopus
User Manual Please read this manual carefully before using the Phoenix Octopus For additional help and updates, refer to our website To contact Phoenix Audio for support, please send a detailed e-mail
More informationRevision of Lecture Eighteen
Revision of Lecture Eighteen Previous lecture has discussed equalisation using Viterbi algorithm: Note similarity with channel decoding using maximum likelihood sequence estimation principle It also discusses
More informationAnalyzing Mission Critical Voice over IP Networks. Michael Todd Gardner
Analyzing Mission Critical Voice over IP Networks Michael Todd Gardner Organization What is Mission Critical Voice? Why Study Mission Critical Voice over IP? Approach to Analyze Mission Critical Voice
More informationReduced echelon form: Add the following conditions to conditions 1, 2, and 3 above:
Section 1.2: Row Reduction and Echelon Forms Echelon form (or row echelon form): 1. All nonzero rows are above any rows of all zeros. 2. Each leading entry (i.e. left most nonzero entry) of a row is in
More informationAccessibility-MiVoice-Business.docx Page 1
Accessibility Standards MiVoice Business Mitel products are designed with the highest standards of accessibility. Below is a table that outlines how the MiVoice Business conforms to section 508 of the
More informationSummary Table Voluntary Product Accessibility Template
PLANTRONICS VPAT 7 Product: Call Center Hearing Aid Compatible (HAC) Polaris Headsets Over the Head Noise Canceling: P161N, P91N, P51N, P251N Over the Head Voice Tube: P161, P91, P51, P251 Over the Ear
More informationSubspace Analysis and Optimization for AAM Based Face Alignment
Subspace Analysis and Optimization for AAM Based Face Alignment Ming Zhao Chun Chen College of Computer Science Zhejiang University Hangzhou, 310027, P.R.China zhaoming1999@zju.edu.cn Stan Z. Li Microsoft
More informationIndependent Component Analysis: Algorithms and Applications
Independent Component Analysis: Algorithms and Applications Aapo Hyvärinen and Erkki Oja Neural Networks Research Centre Helsinki University of Technology P.O. Box 5400, FIN-02015 HUT, Finland Neural Networks,
More informationAPPLYING MFCC-BASED AUTOMATIC SPEAKER RECOGNITION TO GSM AND FORENSIC DATA
APPLYING MFCC-BASED AUTOMATIC SPEAKER RECOGNITION TO GSM AND FORENSIC DATA Tuija Niemi-Laitinen*, Juhani Saastamoinen**, Tomi Kinnunen**, Pasi Fränti** *Crime Laboratory, NBI, Finland **Dept. of Computer
More informationSensitivity Analysis 3.1 AN EXAMPLE FOR ANALYSIS
Sensitivity Analysis 3 We have already been introduced to sensitivity analysis in Chapter via the geometry of a simple example. We saw that the values of the decision variables and those of the slack and
More informationMath Review. for the Quantitative Reasoning Measure of the GRE revised General Test
Math Review for the Quantitative Reasoning Measure of the GRE revised General Test www.ets.org Overview This Math Review will familiarize you with the mathematical skills and concepts that are important
More informationSTATISTICS AND DATA ANALYSIS IN GEOLOGY, 3rd ed. Clarificationof zonationprocedure described onpp. 238-239
STATISTICS AND DATA ANALYSIS IN GEOLOGY, 3rd ed. by John C. Davis Clarificationof zonationprocedure described onpp. 38-39 Because the notation used in this section (Eqs. 4.8 through 4.84) is inconsistent
More informationhp calculators HP 35s Using the formula solver part 1 What is a solution? Practice Example: Finding roots of polynomials
What is a solution? Practice Example: Finding roots of polynomials Practice Example: Finding the root of a log equation Practice Example: Where there is no solution What the solver can and can not find
More informationAN1200.04. Application Note: FCC Regulations for ISM Band Devices: 902-928 MHz. FCC Regulations for ISM Band Devices: 902-928 MHz
AN1200.04 Application Note: FCC Regulations for ISM Band Devices: Copyright Semtech 2006 1 of 15 www.semtech.com 1 Table of Contents 1 Table of Contents...2 1.1 Index of Figures...2 1.2 Index of Tables...2
More informationHow an electronic shutter works in a CMOS camera. First, let s review how shutters work in film cameras.
How an electronic shutter works in a CMOS camera I have been asked many times how an electronic shutter works in a CMOS camera and how it affects the camera s performance. Here s a description of the way
More informationForce/position control of a robotic system for transcranial magnetic stimulation
Force/position control of a robotic system for transcranial magnetic stimulation W.N. Wan Zakaria School of Mechanical and System Engineering Newcastle University Abstract To develop a force control scheme
More informationEcho Troubleshooting Guide How to identify, troubleshoot and remove echoes in installed-room AV systems
Echo Troubleshooting Guide How to identify, troubleshoot and remove echoes in installed-room AV systems Application Note Polycom Installed Voice Business Group September 2004 TABLE OF CONTENTS TABLE OF
More informationSound Level Meters Nor131 & Nor132
Product Data Sound Level Meters Nor131 & Nor132 Applications Noise hazards in the workplace Prescription of hearing protection Environmental noise investigations Product noise testing Noise labeling Features
More informationOn Quality of Monitoring for Multi-channel Wireless Infrastructure Networks
On Quality of Monitoring for Multi-channel Wireless Infrastructure Networks Arun Chhetri, Huy Nguyen, Gabriel Scalosub*, and Rong Zheng Department of Computer Science University of Houston, TX, USA *Department
More informationLOW COST HARDWARE IMPLEMENTATION FOR DIGITAL HEARING AID USING
LOW COST HARDWARE IMPLEMENTATION FOR DIGITAL HEARING AID USING RasPi Kaveri Ratanpara 1, Priyan Shah 2 1 Student, M.E Biomedical Engineering, Government Engineering college, Sector-28, Gandhinagar (Gujarat)-382028,
More informationUnknown n sensors x(t)
Appeared in journal: Neural Network World Vol.6, No.4, 1996, pp.515{523. Published by: IDG Co., Prague, Czech Republic. LOCAL ADAPTIVE LEARNING ALGORITHMS FOR BLIND SEPARATION OF NATURAL IMAGES Andrzej
More informationOverview of the research results on Voice over IP
Overview of the research results on Voice over IP F. Beritelli (Università di Catania) C. Casetti (Politecnico di Torino) S. Giordano (Università di Pisa) R. Lo Cigno (Politecnico di Torino) 1. Introduction
More informationMicrosoft Skype for Business/Lync
Quick Glance: } Skype for Business/Lync is a text, voice, and video conference application. } Skype for Business is available for Windows computers. } Lync is available for Mac, ios, and Android devices.
More informationDeployment Of Multi-Network Video And Voice Conferencing On A Single Platform
Deployment Of Multi-Network Video And Voice Conferencing On A Single Platform Technical White Paper Document Overview This document provides an overview of the issues, capabilities and benefits to be expected
More informationDynamic sound source for simulating the Lombard effect in room acoustic modeling software
Dynamic sound source for simulating the Lombard effect in room acoustic modeling software Jens Holger Rindel a) Claus Lynge Christensen b) Odeon A/S, Scion-DTU, Diplomvej 381, DK-2800 Kgs. Lynby, Denmark
More informationSummary Table Voluntary Product Accessibility Template. Criteria Supporting Features Remarks and explanations
Plantronics VPAT 1 Product: Call Center Hearing Aid Compatible (HAC) Headsets Operated with Amplifier Models M12, MX10, P10, or SQD: Over the Head Noise Canceling: H161N, H91N, H51N, H251N Over the Head
More informationXerox DocuMate 3125 Document Scanner
Xerox DocuMate 3125 Document Scanner Voluntary Product Accessibility Template (VPAT) Submitted by Visioneer, Inc., November 30, 2011 Date: 11/30/2011 Name of Product: Xerox DocuMate 3125 Contact for more
More informationConference Phone Buyer s Guide
Conference Phone Buyer s Guide Conference Phones are essential in most organizations. Almost every business, large or small, uses their conference phone regularly. Such regular use means choosing one is
More informationUSER MANUAL DUET EXECUTIVE USB DESKTOP SPEAKERPHONE
USER MANUAL DUET EXECUTIVE USB DESKTOP SPEAKERPHONE DUET EXE OVERVIEW Control Button Panel Connector Panel Loudspeaker Microphone The Duet is a high performance speakerphone for desktop use that can cover
More information1.5 Oneway Analysis of Variance
Statistics: Rosie Cornish. 200. 1.5 Oneway Analysis of Variance 1 Introduction Oneway analysis of variance (ANOVA) is used to compare several means. This method is often used in scientific or medical experiments
More informationClarify Some Issues on the Sparse Bayesian Learning for Sparse Signal Recovery
Clarify Some Issues on the Sparse Bayesian Learning for Sparse Signal Recovery Zhilin Zhang and Bhaskar D. Rao Technical Report University of California at San Diego September, Abstract Sparse Bayesian
More informationUsing Your Fitting Software This guide provides comprehensive, task-based information about all the fitting software features.
Gravity Fitting Software User's Manual part #: S0273-01 Rev A Using Your Fitting Software This guide provides comprehensive, task-based information about all the fitting software features. You may access
More informationSummary Table Voluntary Product Accessibility Template
PLANTRONICS VPAT 8 Product: Call Center Polaris Headsets (Non-HAC) Over the Head Noise Canceling: P101N, P61N Over the Head Voice Tube: P101, P61 Over the Ear Voice Tube: P41 In the Ear Noise Canceling:
More informationMATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 2. x n. a 11 a 12 a 1n b 1 a 21 a 22 a 2n b 2 a 31 a 32 a 3n b 3. a m1 a m2 a mn b m
MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS 1. SYSTEMS OF EQUATIONS AND MATRICES 1.1. Representation of a linear system. The general system of m equations in n unknowns can be written a 11 x 1 + a 12 x 2 +
More informationPROJECT WORKPLACE DEVELOPMENT
CISCO PROJECT WORKPLACE DEVELOPMENT New York Room Basic Meeting Room Integration Price $15,000.00 Annual Service Contract $2,250.00 Zdi is the valued Cisco partner that integrates this room s technology
More informationAn Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network
Proceedings of the 8th WSEAS Int. Conf. on ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING & DATA BASES (AIKED '9) ISSN: 179-519 435 ISBN: 978-96-474-51-2 An Energy-Based Vehicle Tracking System using Principal
More informationHow to organize and run audio/ videoconference
How to organize and run audio/ videoconference Zlatko Jelačić CARNet Room Videoconferencing System Zlatko.Jelacic@CARNet.hr 0 Q&A!Do you really need VC?!What kind of VC do you need?!what kind of TCR do
More informationUser Manual. For additional help please send a detailed e-mail to Support@phnxaudio.com. - 1 Phoenix Audio Technologies www.phnxaudio.
User Manual Please read the instructions in this manual before using the Duet Please refer to our website www.phnxaudio.com for more information, specifically to our Q&A section in our Support page. For
More informationSolution Components: REALPRESENCE IMMERSIVE TELEPRESENCE STUDIO TECHNICAL SPECIFICATIONS
REALPRESENCE IMMERSIVE TELEPRESENCE STUDIO TECHNICAL SPECIFICATIONS Solution Components: 9-Seat 21-Seat Displays (3) 84 Ultra HD LCD w/led backlight Display Resolution 3840 x 2160 (UD) Aspect Ratio 16:9
More informationFREE TV AUSTRALIA OPERATIONAL PRACTICE OP 60 Multi-Channel Sound Track Down-Mix and Up-Mix Draft Issue 1 April 2012 Page 1 of 6
Page 1 of 6 1. Scope. This operational practice sets out the requirements for downmixing 5.1 and 5.0 channel surround sound audio mixes to 2 channel stereo. This operational practice recommends a number
More informationMUSICAL INSTRUMENT FAMILY CLASSIFICATION
MUSICAL INSTRUMENT FAMILY CLASSIFICATION Ricardo A. Garcia Media Lab, Massachusetts Institute of Technology 0 Ames Street Room E5-40, Cambridge, MA 039 USA PH: 67-53-0 FAX: 67-58-664 e-mail: rago @ media.
More informationA Microphone Array for Hearing Aids
A Microphone Array for Hearing Aids by Bernard Widrow 1531-636X/06/$10.00 2001IEEE 0.00 26 Abstract A directional acoustic receiving system is constructed in the form of a necklace including an array of
More informationLecture 8: Signal Detection and Noise Assumption
ECE 83 Fall Statistical Signal Processing instructor: R. Nowak, scribe: Feng Ju Lecture 8: Signal Detection and Noise Assumption Signal Detection : X = W H : X = S + W where W N(, σ I n n and S = [s, s,...,
More informationUnsupervised Data Mining (Clustering)
Unsupervised Data Mining (Clustering) Javier Béjar KEMLG December 01 Javier Béjar (KEMLG) Unsupervised Data Mining (Clustering) December 01 1 / 51 Introduction Clustering in KDD One of the main tasks in
More informationVideo Conferencing. Femi Alabi UNC-CH - Comp 523 November 22, 2010
Video Conferencing Femi Alabi UNC-CH - Comp 523 November 22, 2010 Introduction Videoconferencing What Is It? Videoconferencing is a method of communicating between two or more locations where sound, vision
More informationvcenter Operations Manager Administration 5.0 Online Help VPAT
Administration 5.0 Online Help VPAT Product Name: Administration 5.0 Online Help VPAT Since the VPAT must be comprehensive, all Section 508 issues on all pages must be corrected to sustain compliance.
More informationSubspace intersection tracking using the Signed URV algorithm
Subspace intersection tracking using the Signed URV algorithm Mu Zhou and Alle-Jan van der Veen TU Delft, The Netherlands 1 Outline Part I: Application 1. AIS ship transponder signal separation 2. Algorithm
More informationBandwidth Adaptation for MPEG-4 Video Streaming over the Internet
DICTA2002: Digital Image Computing Techniques and Applications, 21--22 January 2002, Melbourne, Australia Bandwidth Adaptation for MPEG-4 Video Streaming over the Internet K. Ramkishor James. P. Mammen
More informationMultivariate normal distribution and testing for means (see MKB Ch 3)
Multivariate normal distribution and testing for means (see MKB Ch 3) Where are we going? 2 One-sample t-test (univariate).................................................. 3 Two-sample t-test (univariate).................................................
More informationSummary Table. Voluntary Product Accessibility Template
Date: October 4, 2011 Name of Product: ActiveData For Excel Version 5 Contact for more information: John West, 613-569-4675 ext 175, jwest@informationactive.com Note, ActiveData For Excel / Microsoft Office
More informationLow-resolution Character Recognition by Video-based Super-resolution
2009 10th International Conference on Document Analysis and Recognition Low-resolution Character Recognition by Video-based Super-resolution Ataru Ohkura 1, Daisuke Deguchi 1, Tomokazu Takahashi 2, Ichiro
More informationSPEECH INTELLIGIBILITY and Fire Alarm Voice Communication Systems
SPEECH INTELLIGIBILITY and Fire Alarm Voice Communication Systems WILLIAM KUFFNER, M.A. Sc., P.Eng, PMP Senior Fire Protection Engineer Director Fire Protection Engineering October 30, 2013 Code Reference
More informationActive Monitoring of Voice over IP Services with Malden
Active Monitoring of Voice over IP Services with Malden Introduction Active Monitoring describes the process of evaluating telecommunications system performance with intrusive tests. It differs from passive
More informationnot a Web- based application. not self-contained, closed products. Please refer to the attached VPAT Please refer to the attached VPAT
Apple Cinema Display Standards Subpart 1194.21 Software applications and operating systems. 1194.22 Web-based intranet and internet information and applications. 1194.23 Telecommunications products. 1194.24
More informationMICROPHONE SPECIFICATIONS EXPLAINED
Application Note AN-1112 MICROPHONE SPECIFICATIONS EXPLAINED INTRODUCTION A MEMS microphone IC is unique among InvenSense, Inc., products in that its input is an acoustic pressure wave. For this reason,
More information