- 1.SIMULATION OF RECONSTRUCTION WIDEBAND SPEECH SIGNAL USING
SPECTRAL SHIFTING Fitrie Ratnasari (111080134) 1 Advisor : Dr. Ir.
Bambang Hidayat, DEA st2nd Advisor : Inung Wijayanto S.T, M.T
1
2. Outline Background Purpose Problem Formulation Problem
Limitation Theory System Model Simulation Result Conclusion
Suggestion 2 3. BACKGROUND The transmitting speech narrowband
sounds muffled, thin and far away communication.Wideband speech
coding inquires an increase in the bit rate, bandwidth and
expensive.Various codec in amount of telecommunication technology
need a bridge to equate the speech quality 3 4. PURPOSE To simulate
the reconstruction of wideband speech signal using spectral
shifting and spectral folding method To estimate the wideband
speech envelope using codebook algorithm To analyze performance
system of simulation using objective, subjective measurement and
computational time 4 5. PROBLEM FORMULATION How to simulate the
reconstruction of wideband speech signal using spectral shifting
method with Matlab R.2009.a How to estimate the wideband speech
envelope using codebook algorithmHow to estimate the residual error
of high frequence wideband speech signalHow to analyze and
synthesize linear predictive in a system5 6. PROBLEM LIMITATIONS
System focused on using spectral shifting method for reconstruction
wideband speech signal System doesnt require any communication
system channel or transmission Input speech for simulation is in a
clean speech signal, format .*wav, with sampling frequency 16KHz
Non real-time system 6 7. PROBLEM LIMITATIONS (contd) Data training
of speech signal are in Bahasa without any dialect regionEvaluation
of simulation only tested in a normal hearing Performance
paramaters that analyze using SNR, Cross Correlation, DMOS
(Degraded Mean Opinion Score), and computational time7 8. Theory
Wideband vs Narrowband Narrowband Wideband: 200 3400 Hz : 50 7000
HzThe high-frequency extension from 3400 to 7000 Hz provides better
fricative differentiation, and therefore higher
intelligibility.Wideband SpeechNarrowband SpeechWideband is the
answer to make intelligibility, naturalness of speech, feeling of
transparent communication and facilitates speaker recognition.8 9.
SpectogramWideband SpeechNarrowband Speech 9 10. Wideband
Reconstruction Estimating wideband envelope. This system using
codebook algorithmEstimating missing high frequency. This system
using spectral shifting and folding10 11. Codebook Algorithm11 12.
Spectral Shifting and Spectral Folding Method Spectral shifting or
spectral folding is a method for estimating the missing high
frequency. Spectral shifting methodLPFPitch detectorCosine
generatorXSpectral folding method12 13. MulaiSystem Model Studi
LiteraturPerekaman Suara WidebandData MasukanFilter TelephoniK
Bandlimited speech (Narrowband)LP AnalysisLPC Coefficient of
narrowbandResidual Error of NarrowbandEnvelope EstimationHigh
Frequency RegenerationLPC Coeff estimated of widebandResidual Error
Estimation of WidebandLP SynthesisHPFAddingReconstructed Wideband13
14. Simulation Result (Objective Measurement)0.455.1549Spectral
Folding Spectral Shifting5.155 5.15Spectral Folding Spectral
Shifting0.45 0.4495 0.4490.44855.1455.1370.4480.44690.44755.140.447
5.1350.4465 0.4465.130.4455 0.4455.125 SNR cross corrTesting Result
of Reconstruction Simulation 14 15. Simulation Result (Subjective
Measurement) 1st testing (A) Narrowband signal (B) Original
wideband Preference : BProportion : 95% 2nd testing(A) Narrowband
signal (B) Wideband reconstructed using spectral folding
Preference: AProportion : 100%3rd testing (A) Narrowband signal (B)
Wideband reconstructed using spectral shifting Preference : A /
BProportion : 50 %4th testing (A) Wideband reconstructed using
spectral folding (B) Wideband reconstructed using spectral shifting
Preference : BProportion : 100%Testing Result of Simulation using
A/B preference test15 16. Simulation Result 6 5 44.05 3.653
22Highest DMOS Lowest DMOS Mean DMOS1 0 NarrowbandSpectral
ShiftingSpectral FoldingTesting Result of Simulation using DMOS 16
17. Conclusion 1. Simulation of reconstruction wideband speech
signal using spectral shifting and spectral folding proved to work,
though certain unwanted artefacts were introduced in the
reconstructed speech signal. 2. The best parameter which is used
for this system is Euclidean Distance with K = 1 for knn
classification and Correlation Distance with 256 clusters for kmean
clustering. 3. Maximum of mean SNR of this system toward to 5.3 dB
4. Computational time for this system needs about 0.176 seconds,
and 164 seconds for codebook process. 17 18. Conclusion 5. Wideband
reconstructed signal using spectral shifting method has a better
performance rather than spectral folding method. Mean DMOS for
spectral shifting about 3.65 and for spectral folding 2 6.
Percentage of respondence for choosing both of spectral shifting
method narrowband signal has a same presentage. It means that this
system still need further reaserch to reach higher quality for
implementation18 19. Suggestion 1. System might be implemented and
analyzed with the other languange programs, such as Java, C, etc 2.
System might be impelemented with using another method for
estimating missing high frequency such as statistical method GMM,
HMM, etc 3. System might be added by using with estimating wideband
energy in order to have a higher quality. 4. System can be applied
with dialect region 5. System for reconstruction wideband speech
signal can be implemented as a real time process19 20. Thank you
DankeBedankt Merci Maturnuwun 20
