E : Lecture 8 Source-Filter Processing. E : Lecture 8 Source-Filter Processing / 21
|
|
- Frederick Crawford
- 5 years ago
- Views:
Transcription
1 E85.267: Lecture 8 Source-Filter Processing E85.267: Lecture 8 Source-Filter Processing / 21
2 Source-filter analysis/synthesis n f Spectral envelope Spectral envelope Analysis Source signal n 1 n 2 n Transformation n Synthesis Separate Source/excitation fine time/frequency structure (e.g. pitch) Filter broad spectral shape (resonances) Similar to subtractive synthesis Satisfying physical interpretation for real-world signals Easier to make sense of than e.g. phase n E85.267: Lecture 8 Source-Filter Processing / 21
3 Human speech production Reasonable approximation to speech signals: Source is oscillation of vocal chords e.g. normal speech (varying pitches) vs whispering Filtered by vocal tract (throat + tongue + lips) e.g. oooh vs aaah resonances = formants Both are time-varying E85.267: Lecture 8 Source-Filter Processing / 21
4 Source filter model Excitation source Resonance filter t t f x 1 3 time signal of pred. error e(n) magnitude spectra X(f) and G" H(f) in db n! f/khz! E85.267: Lecture 8 Source-Filter Processing / 21
5 Formants in speech h ε z has e a w t cl c ^ θ I n I watch thin as a dime z I d a y m E85.267: Lecture 8 Source-Filter Processing / 21
6 How to separate the source and filter? Source Signal x(n) 1 H 1(z) e (n) 1 Source Signal Processing H (z) 2 y(n) Chan. Voc. LPC Cepstrum Spectral Envelope Estimation Spectral Envelope Transformation Short-time analysis For each frame, estimate spectral envelope (filter response) 1 Channel vocoder (frequency-domain) 2 Linear Predictive Coding (LPC) (time-domain) 3 Cepstral analysis Source signal is whats left over (residual) after whitening E85.267: Lecture 8 Source-Filter Processing / 21
7 Channel vocoder (a) BP 1 2 x BP1 (n) ( ) 2 LP x (n) RMS1 Wideband STFT filterbank but using relatively few filters Linearly spaced with equal bandwidth (STFT) Logarithmically spaced (constant-q filter bank) Take RMS energy in each frequency band x(n) (b) BP 1 2 x BP2 (n) BP 2 ( ) 2 LP x (n) RMS2 2 x BPk (n) BP k ( ) 2 LP x (n) RMSk Octave-spaced channel stacking BP 2 BP k Equally-spaced channel stacking f BP 1 BP 2 BP k f E85.267: Lecture 8 Source-Filter Processing / 21
8 Channel vocoder using FFT Short time spectrum and spectral envelope X(f)/dB f/hz! Lowpass filter magnitude of each STFT frame i.e. filter columns of the spectrogram E85.267: Lecture 8 Source-Filter Processing / 21
9 Linear predictive coding Predict next input sample as linear combination of previous samples _ e(n) x(n) z -1 z -1 z -1 a 1 a 2 a p Filter is described by a few filter coefficients for each frame p x m [n] ˆx[n] = a k x[n k] k=1 Excitation is whats left after filtering (residual aka prediction error) p e[n] = x[n] ˆx[n] = x[n] a k x[n k] k=1 ^x(n) E85.267: Lecture 8 Source-Filter Processing / 21
10 LPC analysis/synthesis x(n) e(n) ~ e(n) y(n) _ P(z) ^x(n) P(z) (a) (a) LPC analysis (b) (b) LPC synthesis P(z) is just an FIR filter: P(z) = p k=1 a kz k Excitation is still a filtered version of the input: E(x) = X (z) (1 P(z)) For synthesis, pass (approximate) excitation through the inverse filter: Y (z) = Ẽ(z)H(z) 1 H(z) = 1 P(z) all-pole autoregressive (AR) modeling E85.267: Lecture 8 Source-Filter Processing / 21
11 LPC - varying filter order LPC filter H(z) models the spectrum of x[n] Minimizing the energy of the residual e[n] gives optimal coefficients ( {a k } = argmin x[n] ) 2 a k x[m k] a k n k The approximation improves with increasing filter order p 1 X(f) /db spectra of original and LPC filters 5 5 p=1 p=2 p=4 p=6 p=8 p= f/khz! E85.267: Lecture 8 Source-Filter Processing / 21
12 Estimating LPC parameters Set derivative of n e2 [n] w.r.t. a k zero and solve for a k : e 2 [n] = a k End up with p linear equations involving autocorrelations of x: x[m]x[m k] = a k x[m i]x[m k] m i m Solve using Levinson-Durbin recursion n E85.267: Lecture 8 Source-Filter Processing / 21
13 LPC example windowed original -.2 LPC residual db original spectrum LPC spectrum -2 time / samp -4 residual spectrum freq / Hz Filter poles z-plane E85.267: Lecture 8 Source-Filter Processing / 21
14 Short-Time LP Analysis Short-time LPC analysis Solve LPC for each ~2 ms frame freq / khz Imaginary Part freq / khz Real Part E85.267: Lecture 8 Source-Filter Processing time / s 14 / 21
15 Cepstral analysis cepstrum = String.reverse( spec ) + trum Entire lexicon of funny anagrams Insight: source and filter add in the log spectral domain Makes them easy to separate X (z) = E(z)H(z) log X (z) = log E(z) + log H(z) Real Cepstrum Spectral Envelope y(n)=x(n) * h(n) FFT Y(k) log Y(k) Y^ (k) R IFFT c(n) c (n) h FFT C h(k)= log H(k) w(n) w LP(n) Source Envelope c (n) x FFT C x(k)= log X(k) w HP(n) E85.267: Lecture 8 Source-Filter Processing / 21
16 Liftering example By low-pass liftering the cepstrum we obtain the spectral envelope of the signal E85.267: Lecture 8 Source-Filter Processing / 21
17 Liftering example 2 Original waveform has excitation fine structure convolved with resonances DFT shows harmonics modulated by resonances Log DFT is sum of harmonic comb and resonant bumps IDFT separates out resonant bumps (low quefrency) and regular, fine structure ( pitch pulse ) Selecting low-n cepstrum separates resonance information (deconvolution / liftering ).2 Waveform and min. phase IR abs(dft) and liftered log(abs(dft)) and liftered db real cepstrum and lifter samps freq / Hz freq / Hz pitch pulse quefrency E85.267: Lecture 8 Source-Filter Processing / 21
18 prediction filter and residual Applications LP recombining analysis - Speech on them coding ~2ms should yield frames perfect gives s[n] prediction coding applications filter further A(z) and compress residual e[n] e[n] recombining Encoder Filter coefficients them {ai} should Decoder yield perfect s[n] 1 /A(e j! ) coding applications Represent further compress e[n] Input s[n] Input s[n] Encoder LPC f & encode analysis Filter coefficients {ai} Represent Residual & encode 1 /A(e e[n] ) t f LPC Represent & encode e[n] ^ Excitation Decoder generator All-pole filter H(z) = "a i z -i e.g. analysis simple pitch tracker! buzz-hiss encoding e[n] Low bitrate speech codec used Represent in cell phonesexcitation is based ^ All-pole on LPC Pitch period Residual values & encode16 ms frame boundaries generator filter e[n] Quantize LPC filter 1 parameters, use crude approximation to residual time / s E4896 Music Signal Processing Pitch period (Dan values Ellis) 16 ms frame boundaries /16 1 Output s[n] ^ t H(z) = 1 Many different 5 ways to represent filter params: 1 - "a i z -i -5 e.g. simple pitch tracker! buzz-hiss encoding Output s[n] ^ prediction coefficients {a k }, roots of 1 P(z), line spectral frequencies Switch between noise and pulse train for excitation time / s 896 Music Signal UseProcessing codebook(dan of excitations Ellis) (CELP: Code Excited Linear Prediction) E85.267: Lecture 8 Source-Filter Processing / 21
19 Applications - Cross-synthesis/Vocoding freq / Hz freq / Hz Original (mpgr1_sx419) Noise-excited LPC resynthesis with pole freqs time / s Reconstruct using excitation from one sound and filter from another Whisperization: replace excitation with white noise E85.267: Lecture 8 Source-Filter Processing / 21
20 arps Still more frequencies applications but not magnitudes αz +1 8 Original Frequency = ^ Frequency Time Warped LPC resynth, = Time Process formants independent of pitch Pitch-shifting while preserving formants Processing (Dan Ellis) /16 Shift formants while preserving pitch dpwe/resources/matlab/polewarp/ Voice transformation Pitch-analysis E85.267: Lecture 8 Source-Filter Processing / 21
21 Reading DAFX Source-Filter Processing E85.267: Lecture 8 Source-Filter Processing / 21
Lecture 6: Speech modeling and synthesis
EE E682: Speech & Audio Processing & Recognition Lecture 6: Speech modeling and synthesis 1 2 3 4 5 Modeling speech signals Spectral and cepstral models Linear Predictive models (LPC) Other signal models
More informationLecture 5: Speech modeling. The speech signal
EE E68: Speech & Audio Processing & Recognition Lecture 5: Speech modeling 1 3 4 5 Modeling speech signals Spectral and cepstral models Linear Predictive models (LPC) Other signal models Speech synthesis
More informationEE482: 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 informationAdvanced audio analysis. Martin Gasser
Advanced audio analysis Martin Gasser Motivation Which methods are common in MIR research? How can we parameterize audio signals? Interesting dimensions of audio: Spectral/ time/melody structure, high
More informationSignal Processing for Speech Applications - Part 2-1. Signal Processing For Speech Applications - Part 2
Signal Processing for Speech Applications - Part 2-1 Signal Processing For Speech Applications - Part 2 May 14, 2013 Signal Processing for Speech Applications - Part 2-2 References Huang et al., Chapter
More informationLecture 5: Speech modeling
CSC 836: Speech & Audio Understanding Lecture 5: Speech modeling Dan Ellis CUNY Graduate Center, Computer Science Program http://mr-pc.org/t/csc836 With much content from Dan Ellis
More informationOverview of Code Excited Linear Predictive Coder
Overview of Code Excited Linear Predictive Coder Minal Mulye 1, Sonal Jagtap 2 1 PG Student, 2 Assistant Professor, Department of E&TC, Smt. Kashibai Navale College of Engg, Pune, India Abstract Advances
More informationSpeech 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
More informationSpeech 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 informationUniversity of Washington Department of Electrical Engineering Computer Speech Processing EE516 Winter 2005
University of Washington Department of Electrical Engineering Computer Speech Processing EE516 Winter 2005 Lecture 5 Slides Jan 26 th, 2005 Outline of Today s Lecture Announcements Filter-bank analysis
More informationspeech 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
More informationSpeech 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
More informationCepstrum alanysis of speech signals
Cepstrum alanysis of speech signals ELEC-E5520 Speech and language processing methods Spring 2016 Mikko Kurimo 1 /48 Contents Literature and other material Idea and history of cepstrum Cepstrum and LP
More informationSpeech Compression Using Voice Excited Linear Predictive Coding
Speech Compression Using Voice Excited Linear Predictive Coding Ms.Tosha Sen, Ms.Kruti Jay Pancholi PG Student, Asst. Professor, L J I E T, Ahmedabad Abstract : The aim of the thesis is design good quality
More informationEC 6501 DIGITAL COMMUNICATION UNIT - II PART A
EC 6501 DIGITAL COMMUNICATION 1.What is the need of prediction filtering? UNIT - II PART A [N/D-16] Prediction filtering is used mostly in audio signal processing and speech processing for representing
More informationLinguistic 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 informationThe 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 informationSignal Analysis. Peak Detection. Envelope Follower (Amplitude detection) Music 270a: Signal Analysis
Signal Analysis Music 27a: Signal Analysis Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD November 23, 215 Some tools we may want to use to automate analysis
More informationEE 225D LECTURE ON MEDIUM AND HIGH RATE CODING. University of California Berkeley
University of California Berkeley College of Engineering Department of Electrical Engineering and Computer Sciences Professors : N.Morgan / B.Gold EE225D Spring,1999 Medium & High Rate Coding Lecture 26
More informationQuantification 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 informationVocoder (LPC) Analysis by Variation of Input Parameters and Signals
ISCA Journal of Engineering Sciences ISCA J. Engineering Sci. Vocoder (LPC) Analysis by Variation of Input Parameters and Signals Abstract Gupta Rajani, Mehta Alok K. and Tiwari Vebhav Truba College of
More informationLecture 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,
More informationDigital 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 informationModule 9: Multirate Digital Signal Processing Prof. Eliathamby Ambikairajah Dr. Tharmarajah Thiruvaran School of Electrical Engineering &
odule 9: ultirate Digital Signal Processing Prof. Eliathamby Ambikairajah Dr. Tharmarajah Thiruvaran School of Electrical Engineering & Telecommunications The University of New South Wales Australia ultirate
More informationSPEECH 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)
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationTopic. Spectrogram Chromagram Cesptrogram. Bryan Pardo, 2008, Northwestern University EECS 352: Machine Perception of Music and Audio
Topic Spectrogram Chromagram Cesptrogram Short time Fourier Transform Break signal into windows Calculate DFT of each window The Spectrogram spectrogram(y,1024,512,1024,fs,'yaxis'); A series of short term
More informationAudio Signal Compression using DCT and LPC Techniques
Audio Signal Compression using DCT and LPC Techniques P. Sandhya Rani#1, D.Nanaji#2, V.Ramesh#3,K.V.S. Kiran#4 #Student, Department of ECE, Lendi Institute Of Engineering And Technology, Vizianagaram,
More informationDigital 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 informationChapter 7. Frequency-Domain Representations 语音信号的频域表征
Chapter 7 Frequency-Domain Representations 语音信号的频域表征 1 General Discrete-Time Model of Speech Production Voiced Speech: A V P(z)G(z)V(z)R(z) Unvoiced Speech: A N N(z)V(z)R(z) 2 DTFT and DFT of Speech The
More informationResonator Factoring. Julius Smith and Nelson Lee
Resonator Factoring Julius Smith and Nelson Lee RealSimple Project Center for Computer Research in Music and Acoustics (CCRMA) Department of Music, Stanford University Stanford, California 9435 March 13,
More informationEE482: 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 14 Quiz 04 Review 14/04/07 http://www.ee.unlv.edu/~b1morris/ee482/
More informationL19: 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
More informationWaveSurfer. 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 informationSpeech Coding Technique And Analysis Of Speech Codec Using CS-ACELP
Speech Coding Technique And Analysis Of Speech Codec Using CS-ACELP Monika S.Yadav Vidarbha Institute of Technology Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur, India monika.yadav@rediffmail.com
More informationFormant Synthesis of Haegeum: A Sound Analysis/Synthesis System using Cpestral Envelope
Formant Synthesis of Haegeum: A Sound Analysis/Synthesis System using Cpestral Envelope Myeongsu Kang School of Computer Engineering and Information Technology Ulsan, South Korea ilmareboy@ulsan.ac.kr
More informationRobust Algorithms For Speech Reconstruction On Mobile Devices
Robust Algorithms For Speech Reconstruction On Mobile Devices XU SHAO A Thesis presented for the degree of Doctor of Philosophy Speech Group School of Computing Sciences University of East Anglia England
More informationLecture 6: Nonspeech and Music
EE E682: Speech & Audio Processing & Recognition Lecture 6: Nonspeech and Music 1 2 3 4 5 Music and nonspeech Environmental sounds Music synthesis techniques Sinewave synthesis Music analysis Dan Ellis
More informationLecture 6: Nonspeech and Music. Music & nonspeech
EE E682: Speech & Audio Processing & Recognition Lecture 6: Nonspeech and Music 2 3 4 5 Music and nonspeech Environmental sounds Music synthesis techniques Sinewave synthesis Music analysis Dan Ellis
More informationRotating Machinery Fault Diagnosis Techniques Envelope and Cepstrum Analyses
Rotating Machinery Fault Diagnosis Techniques Envelope and Cepstrum Analyses Spectra Quest, Inc. 8205 Hermitage Road, Richmond, VA 23228, USA Tel: (804) 261-3300 www.spectraquest.com October 2006 ABSTRACT
More informationAPPLICATIONS OF DSP OBJECTIVES
APPLICATIONS OF DSP OBJECTIVES This lecture will discuss the following: Introduce analog and digital waveform coding Introduce Pulse Coded Modulation Consider speech-coding principles Introduce the channel
More informationtechniques are means of reducing the bandwidth needed to represent the human voice. In mobile
8 2. LITERATURE SURVEY The available radio spectrum for the wireless radio communication is very limited hence to accommodate maximum number of users the speech is compressed. The speech compression techniques
More informationComparison of CELP speech coder with a wavelet method
University of Kentucky UKnowledge University of Kentucky Master's Theses Graduate School 2006 Comparison of CELP speech coder with a wavelet method Sriram Nagaswamy University of Kentucky, sriramn@gmail.com
More informationSound Synthesis Methods
Sound Synthesis Methods Matti Vihola, mvihola@cs.tut.fi 23rd August 2001 1 Objectives The objective of sound synthesis is to create sounds that are Musically interesting Preferably realistic (sounds like
More informationAnalysis/synthesis coding
TSBK06 speech coding p.1/32 Analysis/synthesis coding Many speech coders are based on a principle called analysis/synthesis coding. Instead of coding a waveform, as is normally done in general audio coders
More informationApplications of Music Processing
Lecture Music Processing Applications of Music Processing Christian Dittmar International Audio Laboratories Erlangen christian.dittmar@audiolabs-erlangen.de Singing Voice Detection Important pre-requisite
More informationHST.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 informationAdaptive Filters Application of Linear Prediction
Adaptive Filters Application of Linear Prediction Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Electrical Engineering and Information Technology Digital Signal Processing
More informationB.Tech III Year II Semester (R13) Regular & Supplementary Examinations May/June 2017 DIGITAL SIGNAL PROCESSING (Common to ECE and EIE)
Code: 13A04602 R13 B.Tech III Year II Semester (R13) Regular & Supplementary Examinations May/June 2017 (Common to ECE and EIE) PART A (Compulsory Question) 1 Answer the following: (10 X 02 = 20 Marks)
More informationPage 0 of 23. MELP Vocoder
Page 0 of 23 MELP Vocoder Outline Introduction MELP Vocoder Features Algorithm Description Parameters & Comparison Page 1 of 23 Introduction Traditional pitched-excited LPC vocoders use either a periodic
More informationAnalysis 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 informationPattern Recognition. Part 6: Bandwidth Extension. Gerhard Schmidt
Pattern Recognition Part 6: Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical and Information Engineering Digital Signal Processing and System Theory
More informationPerformance Analysis of MFCC and LPCC Techniques in Automatic Speech Recognition
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue - 8 August, 2014 Page No. 7727-7732 Performance Analysis of MFCC and LPCC Techniques in Automatic
More informationChapter 9. Chapter 9 275
Chapter 9 Chapter 9: Multirate Digital Signal Processing... 76 9. Decimation... 76 9. Interpolation... 8 9.. Linear Interpolation... 85 9.. Sampling rate conversion by Non-integer factors... 86 9.. Illustration
More informationSinging Voice Detection. Applications of Music Processing. Singing Voice Detection. Singing Voice Detection. Singing Voice Detection
Detection Lecture usic Processing Applications of usic Processing Christian Dittmar International Audio Laboratories Erlangen christian.dittmar@audiolabs-erlangen.de Important pre-requisite for: usic segmentation
More informationSignal processing preliminaries
Signal processing preliminaries ISMIR Graduate School, October 4th-9th, 2004 Contents: Digital audio signals Fourier transform Spectrum estimation Filters Signal Proc. 2 1 Digital signals Advantages of
More informationPR No. 119 DIGITAL SIGNAL PROCESSING XVIII. Academic Research Staff. Prof. Alan V. Oppenheim Prof. James H. McClellan.
XVIII. DIGITAL SIGNAL PROCESSING Academic Research Staff Prof. Alan V. Oppenheim Prof. James H. McClellan Graduate Students Bir Bhanu Gary E. Kopec Thomas F. Quatieri, Jr. Patrick W. Bosshart Jae S. Lim
More informationSynthesis Techniques. Juan P Bello
Synthesis Techniques Juan P Bello Synthesis It implies the artificial construction of a complex body by combining its elements. Complex body: acoustic signal (sound) Elements: parameters and/or basic signals
More informationCommunications 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 informationCS 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 informationSpeech/Non-speech detection Rule-based method using log energy and zero crossing rate
Digital Speech Processing- Lecture 14A Algorithms for Speech Processing Speech Processing Algorithms Speech/Non-speech detection Rule-based method using log energy and zero crossing rate Single speech
More informationMUS421/EE367B Applications Lecture 9C: Time Scale Modification (TSM) and Frequency Scaling/Shifting
MUS421/EE367B Applications Lecture 9C: Time Scale Modification (TSM) and Frequency Scaling/Shifting Julius O. Smith III (jos@ccrma.stanford.edu) Center for Computer Research in Music and Acoustics (CCRMA)
More informationENEE408G Multimedia Signal Processing
ENEE408G Multimedia Signal Processing Design Project on Digital Speech Processing Goals: 1. Learn how to use the linear predictive model for speech analysis and synthesis. 2. Implement a linear predictive
More informationSpeech Coding using Linear Prediction
Speech Coding using Linear Prediction Jesper Kjær Nielsen Aalborg University and Bang & Olufsen jkn@es.aau.dk September 10, 2015 1 Background Speech is generated when air is pushed from the lungs through
More informationFX Basics. Filtering STOMPBOX DESIGN WORKSHOP. Esteban Maestre. CCRMA - Stanford University August 2013
FX Basics STOMPBOX DESIGN WORKSHOP Esteban Maestre CCRMA - Stanford University August 2013 effects modify the frequency content of the audio signal, achieving boosting or weakening specific frequency bands
More informationDiscrete Fourier Transform (DFT)
Amplitude Amplitude Discrete Fourier Transform (DFT) DFT transforms the time domain signal samples to the frequency domain components. DFT Signal Spectrum Time Frequency DFT is often used to do frequency
More informationFFT analysis in practice
FFT analysis in practice Perception & Multimedia Computing Lecture 13 Rebecca Fiebrink Lecturer, Department of Computing Goldsmiths, University of London 1 Last Week Review of complex numbers: rectangular
More informationDSP Based Corrections of Analog Components in Digital Receivers
fred harris DSP Based Corrections of Analog Components in Digital Receivers IEEE Communications, Signal Processing, and Vehicular Technology Chapters Coastal Los Angeles Section 24-April 2008 It s all
More informationEnhanced 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
More informationMel 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
More informationSpeech Production. Automatic Speech Recognition handout (1) Jan - Mar 2009 Revision : 1.1. Speech Communication. Spectrogram. Waveform.
Speech Production Automatic Speech Recognition handout () Jan - Mar 29 Revision :. Speech Signal Processing and Feature Extraction lips teeth nasal cavity oral cavity tongue lang S( Ω) pharynx larynx vocal
More informationVoice Excited Lpc for Speech Compression by V/Uv Classification
IOSR Journal of VLSI and Signal Processing (IOSR-JVSP) Volume 6, Issue 3, Ver. II (May. -Jun. 2016), PP 65-69 e-issn: 2319 4200, p-issn No. : 2319 4197 www.iosrjournals.org Voice Excited Lpc for Speech
More informationAcoustics, signals & systems for audiology. Week 4. Signals through Systems
Acoustics, signals & systems for audiology Week 4 Signals through Systems Crucial ideas Any signal can be constructed as a sum of sine waves In a linear time-invariant (LTI) system, the response to a sinusoid
More informationLecture 6: Nonspeech and Music
EE E682: Speech & Audio Processing & Recognition Lecture 6: Nonspeech and Music 1 Music & nonspeech Dan Ellis Michael Mandel 2 Environmental Sounds Columbia
More informationSimulation of Conjugate Structure Algebraic Code Excited Linear Prediction Speech Coder
COMPUSOFT, An international journal of advanced computer technology, 3 (3), March-204 (Volume-III, Issue-III) ISSN:2320-0790 Simulation of Conjugate Structure Algebraic Code Excited Linear Prediction Speech
More informationEE 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 informationProject 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 informationAudio processing methods on marine mammal vocalizations
Audio processing methods on marine mammal vocalizations Xanadu Halkias Laboratory for the Recognition and Organization of Speech and Audio http://labrosa.ee.columbia.edu Sound to Signal sound is pressure
More informationConverting 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
More informationLecture 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,
More informationFundamental Frequency Detection
Fundamental Frequency Detection Jan Černocký, Valentina Hubeika {cernocky ihubeika}@fit.vutbr.cz DCGM FIT BUT Brno Fundamental Frequency Detection Jan Černocký, Valentina Hubeika, DCGM FIT BUT Brno 1/37
More informationSPEech Feature Toolbox (SPEFT) Design and Emotional Speech Feature Extraction
SPEech Feature Toolbox (SPEFT) Design and Emotional Speech Feature Extraction by Xi Li A thesis submitted to the Faculty of Graduate School, Marquette University, in Partial Fulfillment of the Requirements
More informationA 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
More information-voiced. +voiced. /z/ /s/ Last Lecture. Digital Speech Processing. Overview of Speech Processing. Example on Sound Source Feature
ENEE408G Lecture-6 Digital Speech rocessing URL: http://www.ece.umd.edu/class/enee408g/ Slides included here are based on Spring 005 offering in the order of introduction, image, video, speech, and audio.
More informationDiscrete Fourier Transform, DFT Input: N time samples
EE445M/EE38L.6 Lecture. Lecture objectives are to: The Discrete Fourier Transform Windowing Use DFT to design a FIR digital filter Discrete Fourier Transform, DFT Input: time samples {a n = {a,a,a 2,,a
More informationEECS 452 Midterm Exam Winter 2012
EECS 452 Midterm Exam Winter 2012 Name: unique name: Sign the honor code: I have neither given nor received aid on this exam nor observed anyone else doing so. Scores: # Points Section I /40 Section II
More informationLinear Predictive Coding *
OpenStax-CNX module: m45345 1 Linear Predictive Coding * Kiefer Forseth This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 1 LPC Implementation Linear
More informationHigh-Pitch Formant Estimation by Exploiting Temporal Change of Pitch
High-Pitch Formant Estimation by Exploiting Temporal Change of Pitch The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation As Published
More informationSPEECH 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 informationAdaptive Filters Linear Prediction
Adaptive Filters Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical and Information Engineering Digital Signal Processing and System Theory Slide 1 Contents
More informationVowel Enhancement in Early Stage Spanish Esophageal Speech Using Natural Glottal Flow Pulse and Vocal Tract Frequency Warping
Vowel Enhancement in Early Stage Spanish Esophageal Speech Using Natural Glottal Flow Pulse and Vocal Tract Frequency Warping Rizwan Ishaq 1, Dhananjaya Gowda 2, Paavo Alku 2, Begoña García Zapirain 1
More informationFinal Exam Practice Questions for Music 421, with Solutions
Final Exam Practice Questions for Music 4, with Solutions Elementary Fourier Relationships. For the window w = [/,,/ ], what is (a) the dc magnitude of the window transform? + (b) the magnitude at half
More informationSubtractive 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 informationCopyright S. K. Mitra
1 In many applications, a discrete-time signal x[n] is split into a number of subband signals by means of an analysis filter bank The subband signals are then processed Finally, the processed subband signals
More informationReading: 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 informationADSP ADSP ADSP ADSP. Advanced Digital Signal Processing (18-792) Spring Fall Semester, Department of Electrical and Computer Engineering
ADSP ADSP ADSP ADSP Advanced Digital Signal Processing (18-792) Spring Fall Semester, 201 2012 Department of Electrical and Computer Engineering PROBLEM SET 5 Issued: 9/27/18 Due: 10/3/18 Reminder: Quiz
More informationTopic 2. Signal Processing Review. (Some slides are adapted from Bryan Pardo s course slides on Machine Perception of Music)
Topic 2 Signal Processing Review (Some slides are adapted from Bryan Pardo s course slides on Machine Perception of Music) Recording Sound Mechanical Vibration Pressure Waves Motion->Voltage Transducer
More informationChapter 4 SPEECH ENHANCEMENT
44 Chapter 4 SPEECH ENHANCEMENT 4.1 INTRODUCTION: Enhancement is defined as improvement in the value or Quality of something. Speech enhancement is defined as the improvement in intelligibility and/or
More informationDigital Signal Processing
Digital Signal Processing Fourth Edition John G. Proakis Department of Electrical and Computer Engineering Northeastern University Boston, Massachusetts Dimitris G. Manolakis MIT Lincoln Laboratory Lexington,
More informationEvaluation of MELP Quality and Principles Marcus Ek Lars Pääjärvi Martin Sehlstedt Lule_a Technical University in cooperation with Ericsson Erisoft AB
Evaluation of MELP Quality and Principles Marcus Ek Lars Pääjärvi Martin Sehlstedt Lule_a Technical University in cooperation with Ericsson Erisoft AB, T/RV 3th May 2 2 Abstract This report presents an
More information