Special Session: Phase Importance in Speech Processing Applications
|
|
|
- Hugh Bryant
- 5 years ago
- Views:
Transcription
1 Special Session: Phase Importance in Speech Processing Applications Pejman Mowlaee, Rahim Saeidi, Yannis Stylianou Signal Processing and Speech Communication (SPSC) Lab, Graz University of Technology Speech and Image Processing Unit, School of Computing, University of Eastern Finland, Finland Computer Science Dept. University of Crete, Crete, Greece September 17, 2014 Pejman Mowlaee, Rahim Saeidi, Yannis Stylianou September 17, 2014 page 1/9
2 Special Session Programme at a Glance 1. Introductory talk (Special session aim and scope) 2. Shot-gun presentations 3. Poster presentation (poster boards: between 201 and 206) Vkog" Rcrgt"pq0" Cwvjqtu" Rcrgt"vkvng" Qtcn" 37"*okp+" Ujqv/iwp" :"z"7"?"62" *okp+" 87"okp" ;97" 3:9" 43:" 43;" 929" 93;" 95:" :;8" ;26" Rglocp"Oqyncgg."Tcjko" Ucgkfk."[cpkpu"Uv{nkcpqw" Guvghcpkc"Ecpq."Octm" Rnwodng{"cpf"Ejtkuvkcp" Fkvvoct" Iknngu"Fgiqvvgz"cpf"Pkeqncu" Qdkp" Iknngu"Fgiqvvgz"cpf"Fcpkgn" Gttq" Gooc"Lqmkpgp."Octmq" Vcmcpgp."Jcppw"Rwncmmc"cpf" Rccxq"Cnmw" U"Cuykp"Ujcpowico"cpf" Jgoc"Owtvj{" Octkc"Mqwvuqikcppcmk." Qn{orkc"Ukocpvktcmk."Iknngu" Fgiqvvgz"cpf"[cppku" Uv{nkcpqw" Mctvjkmc"Xklc{cp."Xkpc{" Mwoct"cpf"M0"Utk"Tcoc" Owtv{" Lqp"Ucpejg."Kdqp"Uctcvzcic." Kpoc"Jgtpcg."Gxc"Pcxcu"cpf" Fcpkgn"Gttq" KPVGTURGGEJ"4236"Urgekcn"Uguukqp"qp"Rjcug"Korqtvcpeg"kp" Urggej"Rtqeguukpi"Crrnkecvkqpu" Rjcug/dcugf"jctoqpke1rgtewuukxg"ugrctcvkqp" Rjcug"Fkuvqtvkqp"Uvcvkuvkeu"cu"c"Tgrtgugpvcvkqp"qh"vjg"Inqvvcn" Uqwteg<"Crrnkecvkqp"vq"vjg"Encuukhkecvkqp"qh"Xqkeg"Swcnkvkgu" C"ogcuwtg"qh"rjcug"tcpfqopguu"hqt"vjg"jctoqpke"oqfgn"kp" urggej"u{pvjguku" Gpjcpegogpv"qh"urggej"kpvgnnkikdknkv{"kp"pgct/gpf"pqkug" eqpfkvkqpu"ykvj"rjcug"oqfkhkecvkqp" C"J{dtkf"Crrtqcej"vq"Ugiogpvcvkqp"qh"Urggej"Wukpi"Itqwr" Fgnc{"Rtqeguukpi"cpf"JOO"Dcugf"Godgffgf"Tgguvkocvkqp" Vjg"Korqtvcpeg"qh"Rjcug"qp"Xqkeg"Swcnkv{"Cuuguuogpv" Hgcvwtg"Gzvtcevkqp"htqo"Cpcn{vke"Rjcug"qh"Urggej"Ukipcnu"hqt" Urgcmgt"Xgtkhkecvkqp" C"Etquu/xqeqfgt"Uvwf{"qh"Urgcmgt"Kpfgrgpfgpv"U{pvjgvke" Urggej"Fgvgevkqp"wukpi"Rjcug"Kphqtocvkqp" Rquvgt"Rtgugpvcvkqp" P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 2/9
3 General Outline: Goal and Scope Demonstrating the importance of phase in different applications Consider the latest progress in phase-based speech processing Establish a new community of researchers working on phase Overview on Phase Importance in Speech Applications 1. Source Separation and Speech Enhancement 2. Speech Analysis and Synthesis 3. Automatic Recognition Systems Source Separation Speech Enhancement Automatic Recognition Systems Speech Analysis and Synthesis P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 3/9
4 Introduction to Speech Signal Enhancement & Research Questions Speech signals are impaired by additive noise, interfering speaker and reverberation Is phase important? ½,¾ yes in both amplitude estimation, and signal reconstruction. Impact on quality/intelligibility? How to estimate clean phase? Is phase-aware signal enhancement possible?,, ½ D. Wang, J. Lim, The unimportance of phase in speech enhancement, TASL 30(4), pp , ¾ K. K. Paliwal et al., The importance of phase in speech enhancement Speech Communication, 53(4), pp , P. Mowlaee, R. Saiedi, R. Martin, Phase estimation for signal reconstruction in single-channel speech separation INTERSPEECH T. Gerkmann and M. Krawczyk, MMSE-optimal spectral amplitude estimation given the STFT-phase, IEEE SPL, P. Mowlaee, R. Martin, On phase importance in parameter estimation for single-channel source separation, IWAENC C. Chacon, P. Mowlaee, Least Squares Phase Estimation of Mixed Signals INTERSPEECH, P. Mowlaee, R. Saeidi, Iterative closed-loop phase-aware single-channel speech enhancement IEEE SPL, K. Nathwani et al., Group Delay Based Methods for Speaker Segregation and its Application in Multimedia Information Retrieval, TASL, 15(6) P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 4/9
5 Phase/Amplitude Estimation From Noisy Speech Griffin & Lim based methods ¾, Mowlaee et al. Geometry-based: using TF Constraints,, Least Squares Phase Estimation Gerkmann et al. phase-aware amplitude estimator Mowlaee et al. Temporal smoothing of unwrapped phase Sugiyama et al. Phase randomization Mowlaee et al. Iterative closed-loop phase-aware ½¼ ¾ D. Griffin, J. Lim, Signal Estimation from Modified short-time Fourier transform TASL, P. Mowlaee et al., Partial phase reconstruction using sinusoidal model in single-channel speech separation INTERSPEECH, P. Mowlaee, R. Saeidi, R. Martin; Phase Estimation for signal reconstruction in single-channel source separation INTERSPEECH, P. Mowlaee, R. Saeidi; Time-Frequency Constraints for Phase Estimation in Single-Channel Speech Enhancement IWAENC, C. Chacon, P. Mowlaee, Least Squares Phase Estimation of Mixed Signals INTERSPEECH, J. Kulmer, P. Mowlaee, M. Watanabe, A Probabilistic Approach for Phase Estimation in Single-Channel Speech Enhancement Using von Mises Phase Priors MLSP, M. Krawczyk, T. Gerkmann, STFT Phase Improvement for Single Channel Speech Enhancement IWAENC, A. Sugiyama, R. Miyahara, Phase randomization - a new paradigm for single-channel signal enhancement ICASSP, ½¼ P. Mowlaee, R. Saeidi, Iterative closed-loop phase-aware single-channel speech enhancement IEEE SPL, P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 5/9
6 Phase Importance in Speech Analysis/Synthesis Not used in an explicit way in Unit selection based TTS. Remove linear phase mismatches in concatenative speech synthesis using centre of gravity ½ Using minimum phase ¾, leading to buzzness in synthesized quality esp. at fricatives. Introduce mixed phase in HMM-based TTS by suggesting complex cepstrum ¾,. Requires phase unwrapping: high dimension Fourier transform or linear phase removal. Possibility to include phase information without constraints in HMM? Estimate linear phase using center of gravity Misaligned frames Aligned frames ½ Y. Stylianou, Removing linear phase mismatches in concatenative speech synthesis TASL 9(3), ¾ R. Maia, M. Akamine, M.J.F. Gales, Complex cepstrum as phase information in statistical parametric speech synthesis ICASSP, R. Maia, Y. Stylianou, Complex cepstrum factorization for statistical parametric synthesis ICASSP, 2014 P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 6/9
7 Phase-Based Processing for Recognition Applications Challenges The starting point of a frame Phase wrapping Phase information as for complementing amplitude information Feature-level fusion of amplitude and phase features Score-level fusion of recognition systems built on separate amplitude and phase features Special application of phase information Detection of nasalized vowels from the mixture of oral and nasalized vowels Synthetic speech detection P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 7/9
8 Phase-Based Feature Extraction and Modeling Phase Evolution - R. Schlutel, and H. Ney Phase-Sensitive Model - L. Deng, J. Droppo, and A. Acero Phase AutoCorrelation - S. Ikbal, H. Hermansky, and H. Bourlard Residual Phase - K. Sri Rama Murty and B. Yegnanarayana (Modified) Group delay - R. M. Hegde, H. A. Murthy, and V. R. Gadde Relative Phase - L. Wang, S. Ohtsuka, S. Nakagawa Delta-Phase Spectrum (IF) - I. McCowan, D. Dean, M. McLaren, R. Vogt, S. Sridharan Teager Energy Operator (TEO) Phase - H. A. Patil, and K. Parhi... P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 8/9
9 Conclusion Phase-based processing is a challenging topic still largely unsolved! 19 submissions (9 accepted) Future outlook Special Issue in Speech Communication Any Questions/Feedback? Pejman Mowlaee ( pejman.mowlaee@tugraz.at) Signal Processing and Speech Communication (SPSC) Lab, Graz, Austria Rahim Saeidi ( rahim.saeidi@uef.fi) Speech and Image Processing Unit, School of Computing, University of Eastern Finland, Finland Yannis Stylianou ( yannis@csd.uoc.gr) Computer Science Dept. University of Crete, Crete, Greece Show and Tell: Iterative Refinement of Amplitude and Phase in Single-channel Speech Enhancement By: Pejman Mowlaee, Mario Watanabe, Rahim Saeidi When: Today 13:30-15:30 at Show and Tell Session 2 P. Mowlaee, R. Saeidi, Y. Stylianou September 17, 2014 page 9/9
TIME-FREQUENCY CONSTRAINTS FOR PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT. Pejman Mowlaee, Rahim Saeidi
th International Workshop on Acoustic Signal Enhancement (IWAENC) TIME-FREQUENCY CONSTRAINTS FOR PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT Pejman Mowlaee, Rahim Saeidi Signal Processing and
Phase-Processing For Voice Activity Detection: A Statistical Approach
216 24th European Signal Processing Conference (EUSIPCO) Phase-Processing For Voice Activity Detection: A Statistical Approach Johannes Stahl, Pejman Mowlaee, and Josef Kulmer Signal Processing and Speech
HARMONIC PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING VON MISES DISTRIBUTION AND PRIOR SNR. Josef Kulmer and Pejman Mowlaee
HARMONIC PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING VON MISES DISTRIBUTION AND PRIOR SNR Josef Kulmer and Pejman Mowlaee Signal Processing and Speech Communication Lab Graz University
DNN-based Amplitude and Phase Feature Enhancement for Noise Robust Speaker Identification
INTERSPEECH 2016 September 8 12, 2016, San Francisco, USA DNN-based Amplitude and Phase Feature Enhancement for Noise Robust Speaker Identification Zeyan Oo 1, Yuta Kawakami 1, Longbiao Wang 1, Seiichi
Impact Noise Suppression Using Spectral Phase Estimation
Proceedings of APSIPA Annual Summit and Conference 2015 16-19 December 2015 Impact oise Suppression Using Spectral Phase Estimation Kohei FUJIKURA, Arata KAWAMURA, and Youji IIGUI Graduate School of Engineering
SYNTHETIC SPEECH DETECTION USING TEMPORAL MODULATION FEATURE
SYNTHETIC SPEECH DETECTION USING TEMPORAL MODULATION FEATURE Zhizheng Wu 1,2, Xiong Xiao 2, Eng Siong Chng 1,2, Haizhou Li 1,2,3 1 School of Computer Engineering, Nanyang Technological University (NTU),
Relative phase information for detecting human speech and spoofed speech
Relative phase information for detecting human speech and spoofed speech Longbiao Wang 1, Yohei Yoshida 1, Yuta Kawakami 1 and Seiichi Nakagawa 2 1 Nagaoka University of Technology, Japan 2 Toyohashi University
Single Channel Speaker Segregation using Sinusoidal Residual Modeling
NCC 2009, January 16-18, IIT Guwahati 294 Single Channel Speaker Segregation using Sinusoidal Residual Modeling Rajesh M Hegde and A. Srinivas Dept. of Electrical Engineering Indian Institute of Technology
L19: Prosodic modification of speech
L19: Prosodic modification of speech Time-domain pitch synchronous overlap add (TD-PSOLA) Linear-prediction PSOLA Frequency-domain PSOLA Sinusoidal models Harmonic + noise models STRAIGHT This lecture
A Full-Band Adaptive Harmonic Representation of Speech
A Full-Band Adaptive Harmonic Representation of Speech Gilles Degottex and Yannis Stylianou {degottex,yannis}@csd.uoc.gr University of Crete - FORTH - Swiss National Science Foundation G. Degottex & Y.
Complex Ratio Masking for Monaural Speech Separation Donald S. Williamson, Student Member, IEEE, Yuxuan Wang, and DeLiang Wang, Fellow, IEEE
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 24, NO. 3, MARCH 2016 483 Complex Ratio Masking for Monaural Speech Separation Donald S. Williamson, Student Member, IEEE, Yuxuan Wang,
Hungarian Speech Synthesis Using a Phase Exact HNM Approach
Hungarian Speech Synthesis Using a Phase Exact HNM Approach Kornél Kovács 1, András Kocsor 2, and László Tóth 3 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University
Temporally Weighted Linear Prediction Features for Speaker Verification in Additive Noise
Temporally Weighted Linear Prediction Features for Speaker Verification in Additive Noise Rahim Saeidi 1, Jouni Pohjalainen 2, Tomi Kinnunen 1 and Paavo Alku 2 1 School of Computing, University of Eastern
Speech Synthesis using Mel-Cepstral Coefficient Feature
Speech Synthesis using Mel-Cepstral Coefficient Feature By Lu Wang Senior Thesis in Electrical Engineering University of Illinois at Urbana-Champaign Advisor: Professor Mark Hasegawa-Johnson May 2018 Abstract
ScienceDirect. Unsupervised Speech Segregation Using Pitch Information and Time Frequency Masking
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 46 (2015 ) 122 126 International Conference on Information and Communication Technologies (ICICT 2014) Unsupervised Speech
Epoch Extraction From Emotional Speech
Epoch Extraction From al Speech D Govind and S R M Prasanna Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati Email:{dgovind,prasanna}@iitg.ernet.in Abstract
Speech Synthesis; Pitch Detection and Vocoders
Speech Synthesis; Pitch Detection and Vocoders Tai-Shih Chi ( 冀泰石 ) Department of Communication Engineering National Chiao Tung University May. 29, 2008 Speech Synthesis Basic components of the text-to-speech
Phase estimation in speech enhancement unimportant, important, or impossible?
IEEE 7-th Convention of Electrical and Electronics Engineers in Israel Phase estimation in speech enhancement unimportant, important, or impossible? Timo Gerkmann, Martin Krawczyk, and Robert Rehr Speech
I D I A P. On Factorizing Spectral Dynamics for Robust Speech Recognition R E S E A R C H R E P O R T. Iain McCowan a Hemant Misra a,b
R E S E A R C H R E P O R T I D I A P On Factorizing Spectral Dynamics for Robust Speech Recognition a Vivek Tyagi Hervé Bourlard a,b IDIAP RR 3-33 June 23 Iain McCowan a Hemant Misra a,b to appear in
SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN. Yu Wang and Mike Brookes
SPEECH ENHANCEMENT USING A ROBUST KALMAN FILTER POST-PROCESSOR IN THE MODULATION DOMAIN Yu Wang and Mike Brookes Department of Electrical and Electronic Engineering, Exhibition Road, Imperial College London,
ROBUST SOURCE-FILTER SEPARATION OF SPEECH SIGNAL IN THE PHASE DOMAIN. Erfan Loweimi, Jon Barker, Oscar Saz Torralba and Thomas Hain
ROBUST SOURCE-FILTER SEPARATION OF SPEECH SIGNAL IN THE PHASE DOMAIN Erfan Loweimi, Jon Barker, Oscar Saz Torralba and Thomas Hain Speech and Hearing Research Group (SPandH), University of Sheffield, Sheffield,
Time-Frequency Distributions for Automatic Speech Recognition
196 IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 9, NO. 3, MARCH 2001 Time-Frequency Distributions for Automatic Speech Recognition Alexandros Potamianos, Member, IEEE, and Petros Maragos, Fellow,
Enhanced Waveform Interpolative Coding at 4 kbps
Enhanced Waveform Interpolative Coding at 4 kbps Oded Gottesman, and Allen Gersho Signal Compression Lab. University of California, Santa Barbara E-mail: [oded, gersho]@scl.ece.ucsb.edu Signal Compression
Non-stationary Analysis/Synthesis using Spectrum Peak Shape Distortion, Phase and Reassignment
Non-stationary Analysis/Synthesis using Spectrum Peak Shape Distortion, Phase Reassignment Geoffroy Peeters, Xavier Rodet Ircam - Centre Georges-Pompidou, Analysis/Synthesis Team, 1, pl. Igor Stravinsky,
Dominant Voiced Speech Segregation Using Onset Offset Detection and IBM Based Segmentation
Dominant Voiced Speech Segregation Using Onset Offset Detection and IBM Based Segmentation Shibani.H 1, Lekshmi M S 2 M. Tech Student, Ilahia college of Engineering and Technology, Muvattupuzha, Kerala,
CHiME Challenge: Approaches to Robustness using Beamforming and Uncertainty-of-Observation Techniques
CHiME Challenge: Approaches to Robustness using Beamforming and Uncertainty-of-Observation Techniques Dorothea Kolossa 1, Ramón Fernandez Astudillo 2, Alberto Abad 2, Steffen Zeiler 1, Rahim Saeidi 3,
SINOLA: A New Analysis/Synthesis Method using Spectrum Peak Shape Distortion, Phase and Reassigned Spectrum
SINOLA: A New Analysis/Synthesis Method using Spectrum Peak Shape Distortion, Phase Reassigned Spectrum Geoffroy Peeters, Xavier Rodet Ircam - Centre Georges-Pompidou Analysis/Synthesis Team, 1, pl. Igor
Harmonics Enhancement for Determined Blind Sources Separation using Source s Excitation Characteristics
Harmonics Enhancement for Determined Blind Sources Separation using Source s Excitation Characteristics Mariem Bouafif LSTS-SIFI Laboratory National Engineering School of Tunis Tunis, Tunisia mariem.bouafif@gmail.com
MODELING SPEECH WITH SUM-PRODUCT NETWORKS: APPLICATION TO BANDWIDTH EXTENSION
MODELING SPEECH WITH SUM-PRODUCT NETWORKS: APPLICATION TO BANDWIDTH EXTENSION Robert Peharz, Georg Kapeller, Pejman Mowlaee and Franz Pernkopf Signal Processing and Speech Communication Lab Graz University
High-speed Noise Cancellation with Microphone Array
Noise Cancellation a Posteriori Probability, Maximum Criteria Independent Component Analysis High-speed Noise Cancellation with Microphone Array We propose the use of a microphone array based on independent
PHASE RECONSTRUCTION FROM AMPLITUDE SPECTROGRAMS BASED ON VON-MISES-DISTRIBUTION DEEP NEURAL NETWORK
PHASE RECONSTRUCTION FROM AMPLITUDE SPECTROGRAMS BASED ON VON-MISES-DISTRIBUTION DEEP NEURAL NETWORK Shinnosuke Takamichi, Yuki Saito, Norihiro Takamune, Daichi Kitamura, and Hiroshi Saruwatari Graduate
ADAPTIVE NOISE LEVEL ESTIMATION
Proc. of the 9 th Int. Conference on Digital Audio Effects (DAFx-6), Montreal, Canada, September 18-2, 26 ADAPTIVE NOISE LEVEL ESTIMATION Chunghsin Yeh Analysis/Synthesis team IRCAM/CNRS-STMS, Paris, France
HIGH RESOLUTION SIGNAL RECONSTRUCTION
HIGH RESOLUTION SIGNAL RECONSTRUCTION Trausti Kristjansson Machine Learning and Applied Statistics Microsoft Research traustik@microsoft.com John Hershey University of California, San Diego Machine Perception
ACCURATE SPEECH DECOMPOSITION INTO PERIODIC AND APERIODIC COMPONENTS BASED ON DISCRETE HARMONIC TRANSFORM
5th European Signal Processing Conference (EUSIPCO 007), Poznan, Poland, September 3-7, 007, copyright by EURASIP ACCURATE SPEECH DECOMPOSITIO ITO PERIODIC AD APERIODIC COMPOETS BASED O DISCRETE HARMOIC
Lecture 9: Time & Pitch Scaling
ELEN E4896 MUSIC SIGNAL PROCESSING Lecture 9: Time & Pitch Scaling 1. Time Scale Modification (TSM) 2. Time-Domain Approaches 3. The Phase Vocoder 4. Sinusoidal Approach Dan Ellis Dept. Electrical Engineering,
TEXT-INFORMED SPEECH INPAINTING VIA VOICE CONVERSION. Pierre Prablanc, Alexey Ozerov, Ngoc Q. K. Duong and Patrick Pérez
6 th European Signal Processing Conference (EUSIPCO) TEXT-INFORMED SPEECH INPAINTING VIA VOICE CONVERSION Pierre Prablanc, Alexey Ozerov, Ngoc Q. K. Duong and Patrick Pérez Technicolor 97 avenue des Champs
Speech Emotion Recognition by Combining Amplitude and Phase Information Using Convolutional Neural Network
Interspeech 2018 2-6 September 2018, yderabad Speech Emotion Recognition by Combining Amplitude and Phase Information sing Convolutional Neural Network Lili Guo 1, Longbiao ang 1,, Jianwu Dang 1,2,, Linjuan
Modulation Domain Spectral Subtraction for Speech Enhancement
Modulation Domain Spectral Subtraction for Speech Enhancement Author Paliwal, Kuldip, Schwerin, Belinda, Wojcicki, Kamil Published 9 Conference Title Proceedings of Interspeech 9 Copyright Statement 9
Timbral Distortion in Inverse FFT Synthesis
Timbral Distortion in Inverse FFT Synthesis Mark Zadel Introduction Inverse FFT synthesis (FFT ) is a computationally efficient technique for performing additive synthesis []. Instead of summing partials
Speech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Speech Enhancement Based On Spectral Subtraction For Speech Recognition System With Dpcm A.T. Rajamanickam, N.P.Subiramaniyam, A.Balamurugan*,
A Parametric Model for Spectral Sound Synthesis of Musical Sounds
A Parametric Model for Spectral Sound Synthesis of Musical Sounds Cornelia Kreutzer University of Limerick ECE Department Limerick, Ireland cornelia.kreutzer@ul.ie Jacqueline Walker University of Limerick
Voiced/nonvoiced detection based on robustness of voiced epochs
Voiced/nonvoiced detection based on robustness of voiced epochs by N. Dhananjaya, B.Yegnanarayana in IEEE Signal Processing Letters, 17, 3 : 273-276 Report No: IIIT/TR/2010/50 Centre for Language Technologies
A Comparative Study of Formant Frequencies Estimation Techniques
A Comparative Study of Formant Frequencies Estimation Techniques DORRA GARGOURI, Med ALI KAMMOUN and AHMED BEN HAMIDA Unité de traitement de l information et électronique médicale, ENIS University of Sfax
I D I A P. Mel-Cepstrum Modulation Spectrum (MCMS) Features for Robust ASR R E S E A R C H R E P O R T. Iain McCowan a Hemant Misra a,b
R E S E A R C H R E P O R T I D I A P Mel-Cepstrum Modulation Spectrum (MCMS) Features for Robust ASR a Vivek Tyagi Hervé Bourlard a,b IDIAP RR 3-47 September 23 Iain McCowan a Hemant Misra a,b to appear
Mel Spectrum Analysis of Speech Recognition using Single Microphone
International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree
Unsupervised birdcall activity detection using source and system features
Unsupervised birdcall activity detection using source and system features Anshul Thakur School of Computing and Electrical Engineering Indian Institute of Technology Mandi Himachal Pradesh Email: anshul
Adaptive noise level estimation
Adaptive noise level estimation Chunghsin Yeh, Axel Roebel To cite this version: Chunghsin Yeh, Axel Roebel. Adaptive noise level estimation. Workshop on Computer Music and Audio Technology (WOCMAT 6),
Converting Speaking Voice into Singing Voice
Converting Speaking Voice into Singing Voice 1 st place of the Synthesis of Singing Challenge 2007: Vocal Conversion from Speaking to Singing Voice using STRAIGHT by Takeshi Saitou et al. 1 STRAIGHT Speech
Improving Sound Quality by Bandwidth Extension
International Journal of Scientific & Engineering Research, Volume 3, Issue 9, September-212 Improving Sound Quality by Bandwidth Extension M. Pradeepa, M.Tech, Assistant Professor Abstract - In recent
Research Article Significance of Joint Features Derived from the Modified Group Delay Function in Speech Processing
Hindawi Publishing Corporation EURASIP Journal on Audio, Speech, and Music Processing Volume 27, Article ID 7932, 3 pages doi:.55/27/7932 Research Article Significance of Joint Features Derived from the
Preeti Rao 2 nd CompMusicWorkshop, Istanbul 2012
Preeti Rao 2 nd CompMusicWorkshop, Istanbul 2012 o Music signal characteristics o Perceptual attributes and acoustic properties o Signal representations for pitch detection o STFT o Sinusoidal model o
Book Chapters. Refereed Journal Publications J11
Book Chapters B2 B1 A. Mouchtaris and P. Tsakalides, Low Bitrate Coding of Spot Audio Signals for Interactive and Immersive Audio Applications, in New Directions in Intelligent Interactive Multimedia,
Comparison of Spectral Analysis Methods for Automatic Speech Recognition
INTERSPEECH 2013 Comparison of Spectral Analysis Methods for Automatic Speech Recognition Venkata Neelima Parinam, Chandra Vootkuri, Stephen A. Zahorian Department of Electrical and Computer Engineering
Dimension Reduction of the Modulation Spectrogram for Speaker Verification
Dimension Reduction of the Modulation Spectrogram for Speaker Verification Tomi Kinnunen Speech and Image Processing Unit Department of Computer Science University of Joensuu, Finland Kong Aik Lee and
VQ Source Models: Perceptual & Phase Issues
VQ Source Models: Perceptual & Phase Issues Dan Ellis & Ron Weiss Laboratory for Recognition and Organization of Speech and Audio Dept. Electrical Eng., Columbia Univ., NY USA {dpwe,ronw}@ee.columbia.edu
Audio Imputation Using the Non-negative Hidden Markov Model
Audio Imputation Using the Non-negative Hidden Markov Model Jinyu Han 1,, Gautham J. Mysore 2, and Bryan Pardo 1 1 EECS Department, Northwestern University 2 Advanced Technology Labs, Adobe Systems Inc.
The Partly Preserved Natural Phases in the Concatenative Speech Synthesis Based on the Harmonic/Noise Approach
The Partly Preserved Natural Phases in the Concatenative Speech Synthesis Based on the Harmonic/Noise Approach ZBYNĚ K TYCHTL Department of Cybernetics University of West Bohemia Univerzitní 8, 306 14
An Experimental Study on the Phase Importance in Digital Processing of Speech Signal
Acta Polytechnica Hungarica Vol. 14, No. 8, 017 An Experimental Study on the Phase Importance in Digital Processing of Speech Signal Lazar Tesic, Boban Bondzulic, Milenko Andric, Boban Pavlovic Military
Audio Engineering Society Convention Paper Presented at the 110th Convention 2001 May Amsterdam, The Netherlands
Audio Engineering Society Convention Paper Presented at the th Convention May 5 Amsterdam, The Netherlands This convention paper has been reproduced from the author's advance manuscript, without editing,
Applying the Harmonic Plus Noise Model in Concatenative Speech Synthesis
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 9, NO. 1, JANUARY 2001 21 Applying the Harmonic Plus Noise Model in Concatenative Speech Synthesis Yannis Stylianou, Member, IEEE Abstract This paper
LEVERAGING JOINTLY SPATIAL, TEMPORAL AND MODULATION ENHANCEMENT IN CREATING NOISE-ROBUST FEATURES FOR SPEECH RECOGNITION
LEVERAGING JOINTLY SPATIAL, TEMPORAL AND MODULATION ENHANCEMENT IN CREATING NOISE-ROBUST FEATURES FOR SPEECH RECOGNITION 1 HSIN-JU HSIEH, 2 HAO-TENG FAN, 3 JEIH-WEIH HUNG 1,2,3 Dept of Electrical Engineering,
ROTATIONAL RESET STRATEGY FOR ONLINE SEMI-SUPERVISED NMF-BASED SPEECH ENHANCEMENT FOR LONG RECORDINGS
ROTATIONAL RESET STRATEGY FOR ONLINE SEMI-SUPERVISED NMF-BASED SPEECH ENHANCEMENT FOR LONG RECORDINGS Jun Zhou Southwest University Dept. of Computer Science Beibei, Chongqing 47, China zhouj@swu.edu.cn
AN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS
AN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS Kuldeep Kumar 1, R. K. Aggarwal 1 and Ankita Jain 2 1 Department of Computer Engineering, National Institute
ADDITIVE SYNTHESIS BASED ON THE CONTINUOUS WAVELET TRANSFORM: A SINUSOIDAL PLUS TRANSIENT MODEL
ADDITIVE SYNTHESIS BASED ON THE CONTINUOUS WAVELET TRANSFORM: A SINUSOIDAL PLUS TRANSIENT MODEL José R. Beltrán and Fernando Beltrán Department of Electronic Engineering and Communications University of
Role of modulation magnitude and phase spectrum towards speech intelligibility
Available online at www.sciencedirect.com Speech Communication 53 (2011) 327 339 www.elsevier.com/locate/specom Role of modulation magnitude and phase spectrum towards speech intelligibility Kuldip Paliwal,
arxiv: v1 [eess.as] 13 Mar 2019
![arxiv: v1 [eess.as] 13 Mar 2019](/thumbs/94/122020059.jpg "arxiv: v1 [eess.as] 13 Mar 2019")
LOW-RANKNESS OF COMPLEX-VALUED SPECTROGRAM AND ITS APPLICATION TO PHASE-AWARE AUDIO PROCESSING Yoshiki Masuyama, Kohei Yatabe and Yasuhiro Oikawa Department of Intermedia Art and Science, Waseda University,
A Two-step Technique for MRI Audio Enhancement Using Dictionary Learning and Wavelet Packet Analysis
A Two-step Technique for MRI Audio Enhancement Using Dictionary Learning and Wavelet Packet Analysis Colin Vaz, Vikram Ramanarayanan, and Shrikanth Narayanan USC SAIL Lab INTERSPEECH Articulatory Data
Digital Speech Processing and Coding
ENEE408G Spring 2006 Lecture-2 Digital Speech Processing and Coding Spring 06 Instructor: Shihab Shamma Electrical & Computer Engineering University of Maryland, College Park http://www.ece.umd.edu/class/enee408g/
System Fusion for High-Performance Voice Conversion
System Fusion for High-Performance Voice Conversion Xiaohai Tian 1,2, Zhizheng Wu 3, Siu Wa Lee 4, Nguyen Quy Hy 1,2, Minghui Dong 4, and Eng Siong Chng 1,2 1 School of Computer Engineering, Nanyang Technological
Recent Advances in Acoustic Signal Extraction and Dereverberation
Recent Advances in Acoustic Signal Extraction and Dereverberation Emanuël Habets Erlangen Colloquium 2016 Scenario Spatial Filtering Estimated Desired Signal Undesired sound components: Sensor noise Competing
Applying Spectral Normalisation and Efficient Envelope Estimation and Statistical Transformation for the Voice Conversion Challenge 2016
INTERSPEECH 1 September 8 1, 1, San Francisco, USA Applying Spectral Normalisation and Efficient Envelope Estimation and Statistical Transformation for the Voice Conversion Challenge 1 Fernando Villavicencio
SPEECH ANALYSIS-SYNTHESIS FOR SPEAKER CHARACTERISTIC MODIFICATION
M.Tech. Credit Seminar Report, Electronic Systems Group, EE Dept, IIT Bombay, submitted November 04 SPEECH ANALYSIS-SYNTHESIS FOR SPEAKER CHARACTERISTIC MODIFICATION G. Gidda Reddy (Roll no. 04307046)
Direct modeling of frequency spectra and waveform generation based on phase recovery for DNN-based speech synthesis
INTERSPEECH 17 August 24, 17, Stockholm, Sweden Direct modeling of frequency spectra and waveform generation based on for DNN-based speech synthesis Shinji Takaki 1, Hirokazu Kameoka 2, Junichi Yamagishi
MMSE STSA Based Techniques for Single channel Speech Enhancement Application Simit Shah 1, Roma Patel 2
MMSE STSA Based Techniques for Single channel Speech Enhancement Application Simit Shah 1, Roma Patel 2 1 Electronics and Communication Department, Parul institute of engineering and technology, Vadodara,
The Delta-Phase Spectrum with Application to Voice Activity Detection and Speaker Recognition
1 The Delta-Phase Spectrum with Application to Voice Activity Detection and Speaker Recognition Iain McCowan Member IEEE, David Dean Member IEEE, Mitchell McLaren Student Member IEEE, Robert Vogt Member
Spectral Methods for Single and Multi Channel Speech Enhancement in Multi Source Environment
Spectral Methods for Single and Multi Channel Speech Enhancement in Multi Source Environment A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY by KARAN
Pattern Recognition Part 2: Noise Suppression
Pattern Recognition Part 2: Noise Suppression Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Electrical Engineering and Information Engineering Digital Signal Processing
I D I A P R E S E A R C H R E P O R T. June published in Interspeech 2008
R E S E A R C H R E P O R T I D I A P Spectral Noise Shaping: Improvements in Speech/Audio Codec Based on Linear Prediction in Spectral Domain Sriram Ganapathy a b Petr Motlicek a Hynek Hermansky a b Harinath
T a large number of applications, and as a result has
IEEE TRANSACTIONS ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL. 36, NO. 8, AUGUST 1988 1223 Multiband Excitation Vocoder DANIEL W. GRIFFIN AND JAE S. LIM, FELLOW, IEEE AbstractIn this paper, we present
Robust Voice Activity Detection Based on Discrete Wavelet. Transform
Robust Voice Activity Detection Based on Discrete Wavelet Transform Kun-Ching Wang Department of Information Technology & Communication Shin Chien University kunching@mail.kh.usc.edu.tw Abstract This paper
speech signal S(n). This involves a transformation of S(n) into another signal or a set of signals
16 3. SPEECH ANALYSIS 3.1 INTRODUCTION TO SPEECH ANALYSIS Many speech processing [22] applications exploits speech production and perception to accomplish speech analysis. By speech analysis we extract
NOTICE WARNING CONCERNING COPYRIGHT RESTRICTIONS: The copyright law of the United States (title 17, U.S. Code) governs the making of photocopies or
NOTICE WARNING CONCERNING COPYRIGHT RESTRICTIONS: The copyright law of the United States (title 17, U.S. Code) governs the making of photocopies or other reproductions of copyrighted material. Any copying
HIGH-RESOLUTION SINUSOIDAL MODELING OF UNVOICED SPEECH. George P. Kafentzis and Yannis Stylianou
HIGH-RESOLUTION SINUSOIDAL MODELING OF UNVOICED SPEECH George P. Kafentzis and Yannis Stylianou Multimedia Informatics Lab Department of Computer Science University of Crete, Greece ABSTRACT In this paper,
Frequency Domain Analysis for Noise Suppression Using Spectral Processing Methods for Degraded Speech Signal in Speech Enhancement
Frequency Domain Analysis for Noise Suppression Using Spectral Processing Methods for Degraded Speech Signal in Speech Enhancement 1 Zeeshan Hashmi Khateeb, 2 Gopalaiah 1,2 Department of Instrumentation
Using RASTA in task independent TANDEM feature extraction
R E S E A R C H R E P O R T I D I A P Using RASTA in task independent TANDEM feature extraction Guillermo Aradilla a John Dines a Sunil Sivadas a b IDIAP RR 04-22 April 2004 D a l l e M o l l e I n s t
Prosody Modification using Allpass Residual of Speech Signals
INTERSPEECH 216 September 8 12, 216, San Francisco, USA Prosody Modification using Allpass Residual of Speech Signals Karthika Vijayan and K. Sri Rama Murty Department of Electrical Engineering Indian
Speech Enhancement Using a Mixture-Maximum Model
IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 10, NO. 6, SEPTEMBER 2002 341 Speech Enhancement Using a Mixture-Maximum Model David Burshtein, Senior Member, IEEE, and Sharon Gannot, Member, IEEE
Power Function-Based Power Distribution Normalization Algorithm for Robust Speech Recognition
Power Function-Based Power Distribution Normalization Algorithm for Robust Speech Recognition Chanwoo Kim 1 and Richard M. Stern Department of Electrical and Computer Engineering and Language Technologies
The GlottHMM Entry for Blizzard Challenge 2011: Utilizing Source Unit Selection in HMM-Based Speech Synthesis for Improved Excitation Generation
The GlottHMM ntry for Blizzard Challenge 2011: Utilizing Source Unit Selection in HMM-Based Speech Synthesis for Improved xcitation Generation Antti Suni 1, Tuomo Raitio 2, Martti Vainio 1, Paavo Alku
Learning to Unlearn and Relearn Speech Signal Processing using Neural Networks: current and future perspectives
Learning to Unlearn and Relearn Speech Signal Processing using Neural Networks: current and future perspectives Mathew Magimai Doss Collaborators: Vinayak Abrol, Selen Hande Kabil, Hannah Muckenhirn, Dimitri
Speech Signal Analysis
Speech Signal Analysis Hiroshi Shimodaira and Steve Renals Automatic Speech Recognition ASR Lectures 2&3 14,18 January 216 ASR Lectures 2&3 Speech Signal Analysis 1 Overview Speech Signal Analysis for
TIME DOMAIN ATTACK AND RELEASE MODELING Applied to Spectral Domain Sound Synthesis
TIME DOMAIN ATTACK AND RELEASE MODELING Applied to Spectral Domain Sound Synthesis Cornelia Kreutzer, Jacqueline Walker Department of Electronic and Computer Engineering, University of Limerick, Limerick,
A CONSTRUCTION OF COMPACT MFCC-TYPE FEATURES USING SHORT-TIME STATISTICS FOR APPLICATIONS IN AUDIO SEGMENTATION
17th European Signal Processing Conference (EUSIPCO 2009) Glasgow, Scotland, August 24-28, 2009 A CONSTRUCTION OF COMPACT MFCC-TYPE FEATURES USING SHORT-TIME STATISTICS FOR APPLICATIONS IN AUDIO SEGMENTATION
ADDITIVE synthesis [1] is the original spectrum modeling
![ADDITIVE synthesis [1] is the original spectrum modeling](/thumbs/90/102618859.jpg "ADDITIVE synthesis [1] is the original spectrum modeling")
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 15, NO. 3, MARCH 2007 851 Perceptual Long-Term Variable-Rate Sinusoidal Modeling of Speech Laurent Girin, Member, IEEE, Mohammad Firouzmand,
Sinusoidal Modelling in Speech Synthesis, A Survey.
Sinusoidal Modelling in Speech Synthesis, A Survey. A.S. Visagie, J.A. du Preez Dept. of Electrical and Electronic Engineering University of Stellenbosch, 7600, Stellenbosch avisagie@dsp.sun.ac.za, dupreez@dsp.sun.ac.za
VOICE QUALITY SYNTHESIS WITH THE BANDWIDTH ENHANCED SINUSOIDAL MODEL
VOICE QUALITY SYNTHESIS WITH THE BANDWIDTH ENHANCED SINUSOIDAL MODEL Narsimh Kamath Vishweshwara Rao Preeti Rao NIT Karnataka EE Dept, IIT-Bombay EE Dept, IIT-Bombay narsimh@gmail.com vishu@ee.iitb.ac.in
Lecture 5: Sinusoidal Modeling
ELEN E4896 MUSIC SIGNAL PROCESSING Lecture 5: Sinusoidal Modeling 1. Sinusoidal Modeling 2. Sinusoidal Analysis 3. Sinusoidal Synthesis & Modification 4. Noise Residual Dan Ellis Dept. Electrical Engineering,
Combining Voice Activity Detection Algorithms by Decision Fusion
Combining Voice Activity Detection Algorithms by Decision Fusion Evgeny Karpov, Zaur Nasibov, Tomi Kinnunen, Pasi Fränti Speech and Image Processing Unit, University of Eastern Finland, Joensuu, Finland
Determination of instants of significant excitation in speech using Hilbert envelope and group delay function
Determination of instants of significant excitation in speech using Hilbert envelope and group delay function by K. Sreenivasa Rao, S. R. M. Prasanna, B.Yegnanarayana in IEEE Signal Processing Letters,
Monophony/Polyphony Classification System using Fourier of Fourier Transform
International Journal of Electronics Engineering, 2 (2), 2010, pp. 299 303 Monophony/Polyphony Classification System using Fourier of Fourier Transform Kalyani Akant 1, Rajesh Pande 2, and S.S. Limaye