Nature of Noise source. soundsc (noise, 10000);

Size: px
Start display at page:

Download "Nature of Noise source. soundsc (noise, 10000);"

Transcription

1 Noise Sources Voiceless aspiration can be produced with a noise source at the glottis. (also for voiceless sonorants, including vowels) Noise source that is filtered through VT cascade, so some resonance information will be maintained in output. Breathy voice can be approximated by combining (adding) a noise source and the voiced source Not true breathy voice, which has a different vibrational cycle than modal voice. Different glottal filter would be required.

2 Nature of Noise source Gaussian noise noise = randn(1,4000) soundsc (noise, 10000); hist (noise,20) spectrum (noise,10000)

3 Combined Source Model Noise Voiced + noise * AH_interp * AH_gain pulse * AV_interp * AV_gain =

4 syn3 function signal = syn3 (srate,frame_dur,nf,ftable) % synthesize.m % Louis Goldstein % November 2009 % formant synthesizer % usage: % [out, t] = syn3 (srate,frame_dur,ftable) % % input arguments: % srate sampling rate (in Hz) % f0 fundamental frequency (in Hz) % frame_dur duration of each frame in milliseconds % Ftable character string containing filename of F table % Row 1: AV % Row 2: f0 % Row 3: AH % Row 4 to Row 4+nf-1: formant frequencies % Row 4+nf to Row 4+2*nf-1: formant bandwiths % %returned arguments: % signal vector with synthesized waveform samples % location of parameters in table iav = 1; if0 = 2; iah =3; if1 = 4; ib1 = if1+nf;

5 % location of parameters in table iav = 1; if0 = 2; iah =3; if1 = 4; ib1 = if1+nf; AV_gain = 100; AH_gain =.05; % voiced gain factor % voiceless gain factor FBW = get_fbw(ftable); nframes = size(fbw,2); dur = nframes * (frame_dur / 1000 );% duration in seconds samps_per_frame = floor(srate * (frame_dur / 1000));

6 % generate sources % voiced source f0 = FBW(iF0,:); AV = FBW(iAV,:)*AV_gain; voiced = make_impulse_av(f0, srate, frame_dur, AV); nframes = min ([floor(length(voiced)./ samps_per_frame) nframes]); RG = 0; % RG is the frequency of the Glottal Resonator BWG = 100; % BWG is the bandwidth of the Glottal Resonator [b_glo,a_glo]=resonance(srate,rg,bwg); % filter impulse train thru low-pass filter % to get approximation to shape of glottal pulse voiced=filter(b_glo, a_glo, voiced); % noise source AH = FBW(iAH,1:nframes)*AH_gain; noise = randn(1, length(voiced)); % Gaussian noise % take derivative (calculate velocity source from pressure source) noise = filter ([.5.5], 1, noise); AH_int = interp(ah, samps_per_frame); % compute composite source in = voiced + (noise.* AH_int);

7 function pulses = make_impulse(f0, srate, frame_dur,av); % Input parameters % f0 vector of f0 values % srate sampling rate (Hz) % frame_dur duration of each f0 frame (corresponds to slide in get_f0) % AV vector of voicing amplitudes frame_length = floor(frame_dur * srate / 1000); % frame length in samples length_f0 = length(f0); % interpolate f0 so it has a value for every sample and scale in cycles/ sample cont_freq = interp(f0/srate, frame_length); cont_av = interp(av,frame_length); % calculate elapsed cycles for every sample elapsed_cycles = cumsum(cont_freq); %calculate percentage way through current cycle cycle_percent = rem(elapsed_cycles,1); shift = [0 cycle_percent(1:end-1)]; % set pulses (1s) and 0s elsewhere pulses = cycle_percent<shift; % will be true only when cycle boundary is crossed pulses = cont_av.* double(pulses);

8 New FBW file: aba3.txt Arbitrary Char Arbitrary nos. Must be longest line F AV F Pairs: frame val AH F F F F F B B B B B Make sure each line specifies value for frame 1, and for last frame

9 Stages in modeling VCV (1) Set vowel formants and bandwidths: use constant values for entire sequence (2) Find location of silence (or low amplitude closure voicing) and set low value of AV there. (3) Find values of F1-F3 at closure onset and release. Interpolate between vowel F values and these values over the frames where you see transition (4) For aspirated stops, add AH and remove AV during release transitions. Also increase B1 during this interval.

10 aba

11 aba3.txt F AV F AH F F F F F B B B B B

12 apa.txt F AV F AH F F F F F B B B B B

Location of sound source and transfer functions

Location of sound source and transfer functions Location of sound source and transfer functions Sounds produced with source at the larynx either voiced or voiceless (aspiration) sound is filtered by entire vocal tract Transfer function is well modeled

More information

COMP 546, Winter 2017 lecture 20 - sound 2

COMP 546, Winter 2017 lecture 20 - sound 2 Today we will examine two types of sounds that are of great interest: music and speech. We will see how a frequency domain analysis is fundamental to both. Musical sounds Let s begin by briefly considering

More information

Linguistic Phonetics. Spectral Analysis

Linguistic Phonetics. Spectral Analysis 24.963 Linguistic Phonetics Spectral Analysis 4 4 Frequency (Hz) 1 Reading for next week: Liljencrants & Lindblom 1972. Assignment: Lip-rounding assignment, due 1/15. 2 Spectral analysis techniques There

More information

EE 225D LECTURE ON SPEECH SYNTHESIS. University of California Berkeley

EE 225D LECTURE ON SPEECH SYNTHESIS. University of California Berkeley University of California Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Professors : N.Morgan / B.Gold EE225D Speech Synthesis Spring,1999 Lecture 23 N.MORGAN

More information

The source-filter model of speech production"

The source-filter model of speech production 24.915/24.963! Linguistic Phonetics! The source-filter model of speech production" Glottal airflow Output from lips 400 200 0.1 0.2 0.3 Time (in secs) 30 20 10 0 0 1000 2000 3000 Frequency (Hz) Source

More information

WaveSurfer. Basic acoustics part 2 Spectrograms, resonance, vowels. Spectrogram. See Rogers chapter 7 8

WaveSurfer. Basic acoustics part 2 Spectrograms, resonance, vowels. Spectrogram. See Rogers chapter 7 8 WaveSurfer. Basic acoustics part 2 Spectrograms, resonance, vowels See Rogers chapter 7 8 Allows us to see Waveform Spectrogram (color or gray) Spectral section short-time spectrum = spectrum of a brief

More information

INTRODUCTION TO ACOUSTIC PHONETICS 2 Hilary Term, week 6 22 February 2006

INTRODUCTION TO ACOUSTIC PHONETICS 2 Hilary Term, week 6 22 February 2006 1. Resonators and Filters INTRODUCTION TO ACOUSTIC PHONETICS 2 Hilary Term, week 6 22 February 2006 Different vibrating objects are tuned to specific frequencies; these frequencies at which a particular

More information

Source-filter Analysis of Consonants: Nasals and Laterals

Source-filter Analysis of Consonants: Nasals and Laterals L105/205 Phonetics Scarborough Handout 11 Nov. 3, 2005 reading: Johnson Ch. 9 (today); Pickett Ch. 5 (Tues.) Source-filter Analysis of Consonants: Nasals and Laterals 1. Both nasals and laterals have voicing

More information

Digitized signals. Notes on the perils of low sample resolution and inappropriate sampling rates.

Digitized signals. Notes on the perils of low sample resolution and inappropriate sampling rates. Digitized signals Notes on the perils of low sample resolution and inappropriate sampling rates. 1 Analog to Digital Conversion Sampling an analog waveform Sample = measurement of waveform amplitude at

More information

SPEECH AND SPECTRAL ANALYSIS

SPEECH AND SPECTRAL ANALYSIS SPEECH AND SPECTRAL ANALYSIS 1 Sound waves: production in general: acoustic interference vibration (carried by some propagation medium) variations in air pressure speech: actions of the articulatory organs

More information

INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) Proceedings of the 2 nd International Conference on Current Trends in Engineering and Management ICCTEM -214 ISSN

More information

About waves. Sounds of English. Different types of waves. Ever done the wave?? Why do we care? Tuning forks and pendulums

About waves. Sounds of English. Different types of waves. Ever done the wave?? Why do we care? Tuning forks and pendulums bout waves Sounds of English Topic 7 The acoustics of speech: Sound Waves Lots of examples in the world around us! an take all sorts of different forms Definition: disturbance that travels through a medium

More information

On the glottal flow derivative waveform and its properties

On the glottal flow derivative waveform and its properties COMPUTER SCIENCE DEPARTMENT UNIVERSITY OF CRETE On the glottal flow derivative waveform and its properties A time/frequency study George P. Kafentzis Bachelor s Dissertation 29/2/2008 Supervisor: Yannis

More information

IMPROVING QUALITY OF SPEECH SYNTHESIS IN INDIAN LANGUAGES. P. K. Lehana and P. C. Pandey

IMPROVING QUALITY OF SPEECH SYNTHESIS IN INDIAN LANGUAGES. P. K. Lehana and P. C. Pandey Workshop on Spoken Language Processing - 2003, TIFR, Mumbai, India, January 9-11, 2003 149 IMPROVING QUALITY OF SPEECH SYNTHESIS IN INDIAN LANGUAGES P. K. Lehana and P. C. Pandey Department of Electrical

More information

Synthesis Algorithms and Validation

Synthesis Algorithms and Validation Chapter 5 Synthesis Algorithms and Validation An essential step in the study of pathological voices is re-synthesis; clear and immediate evidence of the success and accuracy of modeling efforts is provided

More information

Assignment 8: Tube Resonances

Assignment 8: Tube Resonances Linguistics 582 Basics of Digital Signal Processing Assignment 8: Tube Resonances Reading: Stevens, K. (1989). On the quantal nature of speech. Journal of Phonetics, 17, 3-45. Read pp. 3-20. ONLY. Johnson,

More information

CS 188: Artificial Intelligence Spring Speech in an Hour

CS 188: Artificial Intelligence Spring Speech in an Hour CS 188: Artificial Intelligence Spring 2006 Lecture 19: Speech Recognition 3/23/2006 Dan Klein UC Berkeley Many slides from Dan Jurafsky Speech in an Hour Speech input is an acoustic wave form s p ee ch

More information

Speech Perception Speech Analysis Project. Record 3 tokens of each of the 15 vowels of American English in bvd or hvd context.

Speech Perception Speech Analysis Project. Record 3 tokens of each of the 15 vowels of American English in bvd or hvd context. Speech Perception Map your vowel space. Record tokens of the 15 vowels of English. Using LPC and measurements on the waveform and spectrum, determine F0, F1, F2, F3, and F4 at 3 points in each token plus

More information

Assignment 7: Tube Resonances

Assignment 7: Tube Resonances Linguistics 582 Basics of Digital Signal Processing Reading: Assignment 7: Tube Resonances Stevens, K. (1989). On the quantal nature of speech. Journal of Phonetics, 17, 3-45. Read pp. 3-20. ONLY. Johnson,

More information

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday.

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday. L105/205 Phonetics Scarborough Handout 7 10/18/05 Reading: Johnson Ch.2.3.3-2.3.6, Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday Spectral Analysis 1. There are

More information

Quantification of glottal and voiced speech harmonicsto-noise ratios using cepstral-based estimation

Quantification of glottal and voiced speech harmonicsto-noise ratios using cepstral-based estimation Quantification of glottal and voiced speech harmonicsto-noise ratios using cepstral-based estimation Peter J. Murphy and Olatunji O. Akande, Department of Electronic and Computer Engineering University

More information

Foundations of Language Science and Technology. Acoustic Phonetics 1: Resonances and formants

Foundations of Language Science and Technology. Acoustic Phonetics 1: Resonances and formants Foundations of Language Science and Technology Acoustic Phonetics 1: Resonances and formants Jan 19, 2015 Bernd Möbius FR 4.7, Phonetics Saarland University Speech waveforms and spectrograms A f t Formants

More information

Digital Speech Processing and Coding

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/

More information

Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels

Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels A complex sound with particular frequency can be analyzed and quantified by its Fourier spectrum: the relative amplitudes

More information

Subtractive Synthesis & Formant Synthesis

Subtractive Synthesis & Formant Synthesis Subtractive Synthesis & Formant Synthesis Prof Eduardo R Miranda Varèse-Gastprofessor eduardo.miranda@btinternet.com Electronic Music Studio TU Berlin Institute of Communications Research http://www.kgw.tu-berlin.de/

More information

Epoch Extraction From Speech Signals K. Sri Rama Murty and B. Yegnanarayana, Senior Member, IEEE

Epoch Extraction From Speech Signals K. Sri Rama Murty and B. Yegnanarayana, Senior Member, IEEE 1602 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 16, NO. 8, NOVEMBER 2008 Epoch Extraction From Speech Signals K. Sri Rama Murty and B. Yegnanarayana, Senior Member, IEEE Abstract

More information

Experimental evaluation of inverse filtering using physical systems with known glottal flow and tract characteristics

Experimental evaluation of inverse filtering using physical systems with known glottal flow and tract characteristics Experimental evaluation of inverse filtering using physical systems with known glottal flow and tract characteristics Derek Tze Wei Chu and Kaiwen Li School of Physics, University of New South Wales, Sydney,

More information

Filters. Signals are sequences of numbers. Simple algebraic operations on signals can perform useful functions: shifting multiplication addition

Filters. Signals are sequences of numbers. Simple algebraic operations on signals can perform useful functions: shifting multiplication addition Filters Signals are sequences of numbers. Simple algebraic operations on signals can perform useful functions: shifting multiplication addition Simple Example... Smooth points to better reveal trend X

More information

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 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,

More information

Linguistics 401 LECTURE #2. BASIC ACOUSTIC CONCEPTS (A review)

Linguistics 401 LECTURE #2. BASIC ACOUSTIC CONCEPTS (A review) Linguistics 401 LECTURE #2 BASIC ACOUSTIC CONCEPTS (A review) Unit of wave: CYCLE one complete wave (=one complete crest and trough) The number of cycles per second: FREQUENCY cycles per second (cps) =

More information

An Implementation of the Klatt Speech Synthesiser*

An Implementation of the Klatt Speech Synthesiser* REVISTA DO DETUA, VOL. 2, Nº 1, SETEMBRO 1997 1 An Implementation of the Klatt Speech Synthesiser* Luis Miguel Teixeira de Jesus, Francisco Vaz, José Carlos Principe Resumo - Neste trabalho descreve-se

More information

HST.582J / 6.555J / J Biomedical Signal and Image Processing Spring 2007

HST.582J / 6.555J / J Biomedical Signal and Image Processing Spring 2007 MIT OpenCourseWare http://ocw.mit.edu HST.582J / 6.555J / 16.456J Biomedical Signal and Image Processing Spring 2007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.

More information

Project 0: Part 2 A second hands-on lab on Speech Processing Frequency-domain processing

Project 0: Part 2 A second hands-on lab on Speech Processing Frequency-domain processing Project : Part 2 A second hands-on lab on Speech Processing Frequency-domain processing February 24, 217 During this lab, you will have a first contact on frequency domain analysis of speech signals. You

More information

Source-filter analysis of fricatives

Source-filter analysis of fricatives 24.915/24.963 Linguistic Phonetics Source-filter analysis of fricatives Figure removed due to copyright restrictions. Readings: Johnson chapter 5 (speech perception) 24.963: Fujimura et al (1978) Noise

More information

Digital Signal Processing

Digital Signal Processing COMP ENG 4TL4: Digital Signal Processing Notes for Lecture #27 Tuesday, November 11, 23 6. SPECTRAL ANALYSIS AND ESTIMATION 6.1 Introduction to Spectral Analysis and Estimation The discrete-time Fourier

More information

Statistical NLP Spring Unsupervised Tagging?

Statistical NLP Spring Unsupervised Tagging? Statistical NLP Spring 2008 Lecture 9: Speech Signal Dan Klein UC Berkeley Unsupervised Tagging? AKA part-of-speech induction Task: Raw sentences in Tagged sentences out Obvious thing to do: Start with

More information

ASPIRATION NOISE DURING PHONATION: SYNTHESIS, ANALYSIS, AND PITCH-SCALE MODIFICATION DARYUSH MEHTA

ASPIRATION NOISE DURING PHONATION: SYNTHESIS, ANALYSIS, AND PITCH-SCALE MODIFICATION DARYUSH MEHTA ASPIRATION NOISE DURING PHONATION: SYNTHESIS, ANALYSIS, AND PITCH-SCALE MODIFICATION by DARYUSH MEHTA B.S., Electrical Engineering (23) University of Florida SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING

More information

Acoustics and Fourier Transform Physics Advanced Physics Lab - Summer 2018 Don Heiman, Northeastern University, 1/12/2018

Acoustics and Fourier Transform Physics Advanced Physics Lab - Summer 2018 Don Heiman, Northeastern University, 1/12/2018 1 Acoustics and Fourier Transform Physics 3600 - Advanced Physics Lab - Summer 2018 Don Heiman, Northeastern University, 1/12/2018 I. INTRODUCTION Time is fundamental in our everyday life in the 4-dimensional

More information

EE 5410 Signal Processing

EE 5410 Signal Processing EE 54 Signal Processing MATLAB Exercise Telephone Touch-Tone Signal Encoding and Decoding Intended Learning Outcomes: On completion of this MATLAB laboratory exercise, you should be able to Generate and

More information

Speech Processing. Undergraduate course code: LASC10061 Postgraduate course code: LASC11065

Speech Processing. Undergraduate course code: LASC10061 Postgraduate course code: LASC11065 Speech Processing Undergraduate course code: LASC10061 Postgraduate course code: LASC11065 All course materials and handouts are the same for both versions. Differences: credits (20 for UG, 10 for PG);

More information

Analysis and Synthesis of Pathological Vowels

Analysis and Synthesis of Pathological Vowels Analysis and Synthesis of Pathological Vowels Prospectus Brian C. Gabelman 6/13/23 1 OVERVIEW OF PRESENTATION I. Background II. Analysis of pathological voices III. Synthesis of pathological voices IV.

More information

Acoustic Phonetics. How speech sounds are physically represented. Chapters 12 and 13

Acoustic Phonetics. How speech sounds are physically represented. Chapters 12 and 13 Acoustic Phonetics How speech sounds are physically represented Chapters 12 and 13 1 Sound Energy Travels through a medium to reach the ear Compression waves 2 Information from Phonetics for Dummies. William

More information

Complex Sounds. Reading: Yost Ch. 4

Complex Sounds. Reading: Yost Ch. 4 Complex Sounds Reading: Yost Ch. 4 Natural Sounds Most sounds in our everyday lives are not simple sinusoidal sounds, but are complex sounds, consisting of a sum of many sinusoids. The amplitude and frequency

More information

EE482: Digital Signal Processing Applications

EE482: Digital Signal Processing Applications Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 12 Speech Signal Processing 14/03/25 http://www.ee.unlv.edu/~b1morris/ee482/

More information

CMPT 468: Frequency Modulation (FM) Synthesis

CMPT 468: Frequency Modulation (FM) Synthesis CMPT 468: Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University October 6, 23 Linear Frequency Modulation (FM) Till now we ve seen signals

More information

Basic Characteristics of Speech Signal Analysis

Basic Characteristics of Speech Signal Analysis www.ijird.com March, 2016 Vol 5 Issue 4 ISSN 2278 0211 (Online) Basic Characteristics of Speech Signal Analysis S. Poornima Assistant Professor, VlbJanakiammal College of Arts and Science, Coimbatore,

More information

OFDM Signal Modulation Application Plug-in Programmer Manual

OFDM Signal Modulation Application Plug-in Programmer Manual xx ZZZ OFDM Signal Modulation Application Plug-in Programmer Manual *P077134900* 077-1349-00 xx ZZZ OFDM Signal Modulation Application Plug-in Programmer Manual www.tek.com 077-1349-00 Copyright Tektronix.

More information

A Physiologically Produced Impulsive UWB signal: Speech

A Physiologically Produced Impulsive UWB signal: Speech A Physiologically Produced Impulsive UWB signal: Speech Maria-Gabriella Di Benedetto University of Rome La Sapienza Faculty of Engineering Rome, Italy gaby@acts.ing.uniroma1.it http://acts.ing.uniroma1.it

More information

VOICE QUALITY SYNTHESIS WITH THE BANDWIDTH ENHANCED SINUSOIDAL MODEL

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

More information

SPEECH TO SINGING SYNTHESIS SYSTEM. Mingqing Yun, Yoon mo Yang, Yufei Zhang. Department of Electrical and Computer Engineering University of Rochester

SPEECH TO SINGING SYNTHESIS SYSTEM. Mingqing Yun, Yoon mo Yang, Yufei Zhang. Department of Electrical and Computer Engineering University of Rochester SPEECH TO SINGING SYNTHESIS SYSTEM Mingqing Yun, Yoon mo Yang, Yufei Zhang Department of Electrical and Computer Engineering University of Rochester ABSTRACT This paper describes a speech-to-singing synthesis

More information

Parameterization of the glottal source with the phase plane plot

Parameterization of the glottal source with the phase plane plot INTERSPEECH 2014 Parameterization of the glottal source with the phase plane plot Manu Airaksinen, Paavo Alku Department of Signal Processing and Acoustics, Aalto University, Finland manu.airaksinen@aalto.fi,

More information

Communications Theory and Engineering

Communications Theory and Engineering Communications Theory and Engineering Master's Degree in Electronic Engineering Sapienza University of Rome A.A. 2018-2019 Speech and telephone speech Based on a voice production model Parametric representation

More information

STANFORD UNIVERSITY. DEPARTMENT of ELECTRICAL ENGINEERING. EE 102B Spring 2013 Lab #05: Generating DTMF Signals

STANFORD UNIVERSITY. DEPARTMENT of ELECTRICAL ENGINEERING. EE 102B Spring 2013 Lab #05: Generating DTMF Signals STANFORD UNIVERSITY DEPARTMENT of ELECTRICAL ENGINEERING EE 102B Spring 2013 Lab #05: Generating DTMF Signals Assigned: May 3, 2013 Due Date: May 17, 2013 Remember that you are bound by the Stanford University

More information

Voiced/nonvoiced detection based on robustness of voiced epochs

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

More information

Glottal source model selection for stationary singing-voice by low-band envelope matching

Glottal source model selection for stationary singing-voice by low-band envelope matching Glottal source model selection for stationary singing-voice by low-band envelope matching Fernando Villavicencio Yamaha Corporation, Corporate Research & Development Center, 3 Matsunokijima, Iwata, Shizuoka,

More information

Block diagram of proposed general approach to automatic reduction of speech wave to lowinformation-rate signals.

Block diagram of proposed general approach to automatic reduction of speech wave to lowinformation-rate signals. XIV. SPEECH COMMUNICATION Prof. M. Halle G. W. Hughes J. M. Heinz Prof. K. N. Stevens Jane B. Arnold C. I. Malme Dr. T. T. Sandel P. T. Brady F. Poza C. G. Bell O. Fujimura G. Rosen A. AUTOMATIC RESOLUTION

More information

Review: Frequency Response Graph. Introduction to Speech and Science. Review: Vowels. Response Graph. Review: Acoustic tube models

Review: Frequency Response Graph. Introduction to Speech and Science. Review: Vowels. Response Graph. Review: Acoustic tube models eview: requency esponse Graph Introduction to Speech and Science Lecture 5 ricatives and Spectrograms requency Domain Description Input Signal System Output Signal Output = Input esponse? eview: requency

More information

Chapter 3. Description of the Cascade/Parallel Formant Synthesizer. 3.1 Overview

Chapter 3. Description of the Cascade/Parallel Formant Synthesizer. 3.1 Overview Chapter 3 Description of the Cascade/Parallel Formant Synthesizer The Klattalk system uses the KLSYN88 cascade-~arallel formant synthesizer that was first described in Klatt and Klatt (1990). This speech

More information

Pitch Period of Speech Signals Preface, Determination and Transformation

Pitch Period of Speech Signals Preface, Determination and Transformation Pitch Period of Speech Signals Preface, Determination and Transformation Mohammad Hossein Saeidinezhad 1, Bahareh Karamsichani 2, Ehsan Movahedi 3 1 Islamic Azad university, Najafabad Branch, Saidinezhad@yahoo.com

More information

Wireless Communication Systems Laboratory Lab#1: An introduction to basic digital baseband communication through MATLAB simulation Objective

Wireless Communication Systems Laboratory Lab#1: An introduction to basic digital baseband communication through MATLAB simulation Objective Wireless Communication Systems Laboratory Lab#1: An introduction to basic digital baseband communication through MATLAB simulation Objective The objective is to teach students a basic digital communication

More information

Speech Synthesis; Pitch Detection and Vocoders

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

More information

Mask-Based Nasometry A New Method for the Measurement of Nasalance

Mask-Based Nasometry A New Method for the Measurement of Nasalance Publications of Dr. Martin Rothenberg: Mask-Based Nasometry A New Method for the Measurement of Nasalance ABSTRACT The term nasalance has been proposed by Fletcher and his associates (Fletcher and Frost,

More information

APPENDIX K PULSE AMPLITUDE MODULATION STANDARDS. Paragraph Title Page

APPENDIX K PULSE AMPLITUDE MODULATION STANDARDS. Paragraph Title Page APPENDIX K PULSE AMPLITUDE MODULATION STANDARDS Paragraph Title Page 1.0 General... K-1 2.0 Frame and Pulse Structure... K-1 3.0 Frame and Pulse Rate... K-3 4.0 Frequency Modulation... K-4 5.0 Premodulation

More information

The Channel Vocoder (analyzer):

The Channel Vocoder (analyzer): Vocoders 1 The Channel Vocoder (analyzer): The channel vocoder employs a bank of bandpass filters, Each having a bandwidth between 100 Hz and 300 Hz. Typically, 16-20 linear phase FIR filter are used.

More information

ScienceDirect. Accuracy of Jitter and Shimmer Measurements

ScienceDirect. Accuracy of Jitter and Shimmer Measurements Available online at www.sciencedirect.com ScienceDirect Procedia Technology 16 (2014 ) 1190 1199 CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on

More information

Transforming High-Effort Voices Into Breathy Voices Using Adaptive Pre-Emphasis Linear Prediction

Transforming High-Effort Voices Into Breathy Voices Using Adaptive Pre-Emphasis Linear Prediction Transforming High-Effort Voices Into Breathy Voices Using Adaptive Pre-Emphasis Linear Prediction by Karl Ingram Nordstrom B.Eng., University of Victoria, 1995 M.A.Sc., University of Victoria, 2000 A Dissertation

More information

Filling in the MIMO Matrix Part 2 Time Waveform Replication Tests Using Field Data

Filling in the MIMO Matrix Part 2 Time Waveform Replication Tests Using Field Data Filling in the MIMO Matrix Part 2 Time Waveform Replication Tests Using Field Data Marcos Underwood, Russ Ayres, and Tony Keller, Spectral Dynamics, Inc., San Jose, California There is currently quite

More information

Acoustic Phonetics. Chapter 8

Acoustic Phonetics. Chapter 8 Acoustic Phonetics Chapter 8 1 1. Sound waves Vocal folds/cords: Frequency: 300 Hz 0 0 0.01 0.02 0.03 2 1.1 Sound waves: The parts of waves We will be considering the parts of a wave with the wave represented

More information

Data and Computer Communications. Chapter 3 Data Transmission

Data and Computer Communications. Chapter 3 Data Transmission Data and Computer Communications Chapter 3 Data Transmission Data Transmission quality of the signal being transmitted The successful transmission of data depends on two factors: characteristics of the

More information

Contents. Sevana Voice Quality Analyzer Copyright (c) 2009 by Sevana Oy, Finland. All rights reserved.

Contents. Sevana Voice Quality Analyzer Copyright (c) 2009 by Sevana Oy, Finland. All rights reserved. Sevana Voice Quality Analyzer 3.4.10.327 Contents Contents... 1 Introduction... 2 Functionality... 2 Requirements... 2 Generate test signals... 2 Test voice codecs... 2 Compare wav files... 2 Testing parameters...

More information

Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification. Daryush Mehta

Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification. Daryush Mehta Aspiration Noise during Phonation: Synthesis, Analysis, and Pitch-Scale Modification Daryush Mehta SHBT 03 Research Advisor: Thomas F. Quatieri Speech and Hearing Biosciences and Technology 1 Summary Studied

More information

Linear Frequency Modulation (FM) Chirp Signal. Chirp Signal cont. CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis

Linear Frequency Modulation (FM) Chirp Signal. Chirp Signal cont. CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis Linear Frequency Modulation (FM) CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 26, 29 Till now we

More information

The quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission:

The quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission: Data Transmission The successful transmission of data depends upon two factors: The quality of the transmission signal The characteristics of the transmission medium Some type of transmission medium is

More information

Sub-band Envelope Approach to Obtain Instants of Significant Excitation in Speech

Sub-band Envelope Approach to Obtain Instants of Significant Excitation in Speech Sub-band Envelope Approach to Obtain Instants of Significant Excitation in Speech Vikram Ramesh Lakkavalli, K V Vijay Girish, A G Ramakrishnan Medical Intelligence and Language Engineering (MILE) Laboratory

More information

Source-Filter Theory 1

Source-Filter Theory 1 Source-Filter Theory 1 Vocal tract as sound production device Sound production by the vocal tract can be understood by analogy to a wind or brass instrument. sound generation sound shaping (or filtering)

More information

A Manual of TransShiftMex

A Manual of TransShiftMex A Manual of TransShiftMex Shanqing Cai Speech Communication Group, Research Laboratory of Electronics, MIT E-mail: cais@mit.edu January 2009 Section 0. Getting started - Running the demos There are two

More information

Exam 3--PHYS 151--Chapter 4--S14

Exam 3--PHYS 151--Chapter 4--S14 Class: Date: Exam 3--PHYS 151--Chapter 4--S14 Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Which of these statements is not true for a longitudinal

More information

A() I I X=t,~ X=XI, X=O

A() I I X=t,~ X=XI, X=O 6 541J Handout T l - Pert r tt Ofl 11 (fo 2/19/4 A() al -FA ' AF2 \ / +\ X=t,~ X=X, X=O, AF3 n +\ A V V V x=-l x=o Figure 3.19 Curves showing the relative magnitude and direction of the shift AFn in formant

More information

Cellular systems & GSM Wireless Systems, a.a. 2014/2015

Cellular systems & GSM Wireless Systems, a.a. 2014/2015 Cellular systems & GSM Wireless Systems, a.a. 2014/2015 Un. of Rome La Sapienza Chiara Petrioli Department of Computer Science University of Rome Sapienza Italy 2 Voice Coding 3 Speech signals Voice coding:

More information

SOURCE-filter modeling of speech is based on exciting. Glottal Spectral Separation for Speech Synthesis

SOURCE-filter modeling of speech is based on exciting. Glottal Spectral Separation for Speech Synthesis IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 1 Glottal Spectral Separation for Speech Synthesis João P. Cabral, Korin Richmond, Member, IEEE, Junichi Yamagishi, Member, IEEE, and Steve Renals,

More information

3A: PROPERTIES OF WAVES

3A: PROPERTIES OF WAVES 3A: PROPERTIES OF WAVES Int roduct ion Your ear is complicated device that is designed to detect variations in the pressure of the air at your eardrum. The reason this is so useful is that disturbances

More information

Signals A Preliminary Discussion EE442 Analog & Digital Communication Systems Lecture 2

Signals A Preliminary Discussion EE442 Analog & Digital Communication Systems Lecture 2 Signals A Preliminary Discussion EE442 Analog & Digital Communication Systems Lecture 2 The Fourier transform of single pulse is the sinc function. EE 442 Signal Preliminaries 1 Communication Systems and

More information

From Ladefoged EAP, p. 11

From Ladefoged EAP, p. 11 The smooth and regular curve that results from sounding a tuning fork (or from the motion of a pendulum) is a simple sine wave, or a waveform of a single constant frequency and amplitude. From Ladefoged

More information

SGN Audio and Speech Processing

SGN Audio and Speech Processing Introduction 1 Course goals Introduction 2 SGN 14006 Audio and Speech Processing Lectures, Fall 2014 Anssi Klapuri Tampere University of Technology! Learn basics of audio signal processing Basic operations

More information

Lecture Fundamentals of Data and signals

Lecture Fundamentals of Data and signals IT-5301-3 Data Communications and Computer Networks Lecture 05-07 Fundamentals of Data and signals Lecture 05 - Roadmap Analog and Digital Data Analog Signals, Digital Signals Periodic and Aperiodic Signals

More information

the 99th Convention 1995 October 6-9 NewYork

the 99th Convention 1995 October 6-9 NewYork Tunable Bandpass Filters in Music Synthesis 4098 (L-2) Robert C. Maher University of Nebraska-Lincoln Lincoln, NE 68588-0511, USA Presented at the 99th Convention 1995 October 6-9 NewYork ^ ud,o Thispreprinthas

More information

CHAPTER 3. ACOUSTIC MEASURES OF GLOTTAL CHARACTERISTICS 39 and from periodic glottal sources (Shadle, 1985; Stevens, 1993). The ratio of the amplitude of the harmonics at 3 khz to the noise amplitude in

More information

Introducing COVAREP: A collaborative voice analysis repository for speech technologies

Introducing COVAREP: A collaborative voice analysis repository for speech technologies Introducing COVAREP: A collaborative voice analysis repository for speech technologies John Kane Wednesday November 27th, 2013 SIGMEDIA-group TCD COVAREP - Open-source speech processing repository 1 Introduction

More information

A Look at Un-Electronic Musical Instruments

A Look at Un-Electronic Musical Instruments A Look at Un-Electronic Musical Instruments A little later in the course we will be looking at the problem of how to construct an electrical model, or analog, of an acoustical musical instrument. To prepare

More information

APPENDIX K. Pulse Amplitude Modulation Standards

APPENDIX K. Pulse Amplitude Modulation Standards APPENDIX K Pulse Amplitude Modulation Standards Acronyms... K-iii 1.0 General... K-1 2.0 Frame and Pulse Structure... K-1 2.1 Commutation Pattern... K-1 2.2 In-Flight Calibration... K-1 2.3 Frame Synchronization

More information

Digital Wireless Measurement Solution

Digital Wireless Measurement Solution Product Introduction Digital Wireless Measurement Solution Signal Analyzer MS2690A/MS2691A/MS2692A/MS2840A/MS2830A Vector Modulation Analysis Software MX269017A Vector Signal Generator MS269xA-020, MS2840A-020/021,

More information

Text Book: Simon Haykin & Michael Moher,

Text Book: Simon Haykin & Michael Moher, Qassim University College of Engineering Electrical Engineering Department Electronics and Communications Course: EE322 Digital Communications Prerequisite: EE320 Text Book: Simon Haykin & Michael Moher,

More information

Acoustic Tremor Measurement: Comparing Two Systems

Acoustic Tremor Measurement: Comparing Two Systems Acoustic Tremor Measurement: Comparing Two Systems Markus Brückl Elvira Ibragimova Silke Bögelein Institute for Language and Communication Technische Universität Berlin 10 th International Workshop on

More information

Epoch Extraction From Emotional 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

More information

ENSEMBLE String Synthesizer

ENSEMBLE String Synthesizer ENSEMBLE String Synthesizer by Max for Cats (+ Chorus Ensemble & Ensemble Phaser) Thank you for purchasing the Ensemble Max for Live String Synthesizer. Ensemble was inspired by the string machines from

More information

Relative occurrences and difference of extrema for detection of transitions between broad phonetic classes

Relative occurrences and difference of extrema for detection of transitions between broad phonetic classes Sådhanå (218) 43:153 Ó Indian Academy of Sciences https://doi.org/1.17/s1246-18-923-xsadhana(123456789().,-volv)ft3 ](123456789().,-volV) Relative occurrences and difference of extrema for detection of

More information

Quarterly Progress and Status Report. Acoustic properties of the Rothenberg mask

Quarterly Progress and Status Report. Acoustic properties of the Rothenberg mask Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Acoustic properties of the Rothenberg mask Hertegård, S. and Gauffin, J. journal: STL-QPSR volume: 33 number: 2-3 year: 1992 pages:

More information

Resonance and resonators

Resonance and resonators Resonance and resonators Dr. Christian DiCanio cdicanio@buffalo.edu University at Buffalo 10/13/15 DiCanio (UB) Resonance 10/13/15 1 / 27 Harmonics Harmonics and Resonance An example... Suppose you are

More information

Plaits. Macro-oscillator

Plaits. Macro-oscillator Plaits Macro-oscillator A B C D E F About Plaits Plaits is a digital voltage-controlled sound source capable of sixteen different synthesis techniques. Plaits reclaims the land between all the fragmented

More information

Airflow visualization in a model of human glottis near the self-oscillating vocal folds model

Airflow visualization in a model of human glottis near the self-oscillating vocal folds model Applied and Computational Mechanics 5 (2011) 21 28 Airflow visualization in a model of human glottis near the self-oscillating vocal folds model J. Horáček a,, V. Uruba a,v.radolf a, J. Veselý a,v.bula

More information