MELP Vocoder Page 0 of 23 Outline Introduction MELP Vocoder - - PowerPoint PPT Presentation

MELP Vocoder

Page 0 of 23

Outline



Introduction



MELP Vocoder Features



Algorithm Description



Parameters & Comparison

Page 1 of 23

Introduction



Traditional pitched-excited LPC vocoders use either a periodic train or white noise for synthesis filter  intelligible speech at very low bit rates



But sometimes results in mechanical or buzzy sound and are prone to tonal noise

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Introduction



These problems arise from:



Inability of a simple pulse train to reproduce all kind of voiced speech



MELP vocoder uses a mixed-excitation model and it represents a richer ensemble of speech characteristic

 Produce more natural sounding speech

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MELP vocoder



Robust in background noise environments



Based on traditional LPC model, also includes additional features

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Aperiodic pulses Adaptive spectral enhancement Mixed excitation Pulse dispersion

هدننك دك

هحفص5 زا54

LPC LPC LPC LSF LSF LSF MSVQ

ردكو MELP

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ردكو MELP

هيروف ليدبت ياه هنماد هبساحم

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• FFT

ردكو MELP

يدويرپ ريغ مچرپ نييعت و ييادص ياه تدش هبساحمك

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L=40,41,…,160

ردكو MELP

جوا طاقن يگدنكارپ نازيم

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ردكو MELP

 

   



79 80 79 80 2

] [ 160 1 ] [ 160 1

n n

n e n e p P=12.64 P=6.77 P=1.1 P=1.16

جوا طاقن يگدنكارپ نازيم

هحفص10 زا54

 

   



79 80 79 80 2

] [ 160 1 ] [ 160 1

n n

n e n e p

ردكو MELP

تيب صاصتخا لودج

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LSF

25 25 8

• 8

8

VS1

7 7 4

• 1
• 13

1 1

54 54

ردكو MELP

Mixed Excitation



Mixed-excitation is implemented using a multi-band mixing model



This model can simulate frequency dependent voicing strength



Using a mixture of Aperiodic/periodic and white noise as excitation



Primary effect of this unit is to reduce the buzz in broadband acoustic noise

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Aperiodic pulses



When input signal is voiced, MELP vocoder can synthesize speech using either aperiodic or periodic pulses.



Aperiodic pulses used during transition regions between voiced and unvoiced segments of speech signal

 Producing erratic glottal pulses without tonal noise

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Pulse Dispersion



Pulse dispersion is implemented using fixed pulse dispersion filter based on a flattened triangle pulse



The pulse dispersion filter improves the match of bandpass filtered synthetic and natural speech waveforms in frequency bands which do not contain a formant resonance.  Spreading the excitation energy with a pitch period Reduce harsh quality of the synthetic speech

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Adaptive spectral enhancement filter



Based on the poles of the vocal tract filter



Is used to enhance the formant structure in the synthetic speech



This filter improves the match between synthetic and natural bandpass waveforms  more natural speech

• utput

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MELP Algorithm Description (Encoder)

.1

filter out any low frequency noise

.2

This filtered speech is again filtered in

• rder to perform the initial pitch search

for the pitch estimation

.3

The next step is to perform the Bandpass voicing analysis

• In this step we decide to use periodic/Aperiodic train or

white noise model

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MELP Algorithm Description (Encoder) cont’d



In this stage A voice degree parameter is estimated in each band, based on the normalized correlation function of the speech signal and the smoothed rectified signal in the non-DC band



Let sk(n) denote the speech signal in band k, uk(n) denote the DC-removed smoothed rectified signal of sk(n). The correlation function:

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2 / 1 1 2 1 2 1

] ) ( ) ( [ ) ( ) ( ) (

  

     

  

N n N n N n x

p n x n x p n x n x p R

P – the pitch of current frame N – the frame length k – the voicing strength for band (defined as max(Rsk(P),Ruk(P)))

MELP Algorithm Description (Encoder ) cont’d



The jittery state is determined by the peakiness of the fullwave rectified LP residue e(n):

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 

   



1 1 2 / 1 2

) ( 1 ] ) ( 1 [

N n N n

n e N n e N Peakiness

 If peakiness is greater than some threshold, the speech

frame is then flagged as jittered (Aperiodic flag will be set)

MELP Algorithm Description (Encoder) cont’d

4.

Applying a LPC analysis 5. Calculating final pitch estimate 6. Calculating Gain estimate 7. quantize the LPC coefficients, pitch, gain and bandpass voicing .8 Fourier magnitudes are determined and quantized



The information in these coefficients improves the accuracy of the speech production model at the perceptually-important lower frequencies

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MELP Encoder

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Pre filter Pitch Search

Bandpass Voicing Decision

Gain Calculator LPC Analysis Filter Final Pitch And voicing Decision LSF

quantization

Quantize Gain, pitch, Voicing, jitter

Fourier Magnitude calculation Apply Forward Error Correction

Input signal Transmitted Bitstream

MELP Algorithm (Decoder)

.1

Decoding the pitch

.2

Applying gain attenuation

.3

Interpolating linearly all of the synthesis parameters pitch-synchronously

.4

Generating mixed-excitation

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MELP Algorithm (Decoder) cont’d

.5

Applying an adaptive spectral enhancement filter

.6

LPC synthesis and applying gain factor

.7

Dispersion filtering

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MELP Decoder

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Decode parameters Noise Generator Noise Shaping Filter Pulse Generator Pulse Position Jitter Pulse Shaping Filter Adaptive Spectral Enhancement + LPC Synthesis Filter Pulse Dispersion Filter gain Received Bitstream Synthesized Speech

Parameter Quantization

Parameters Voiced Unvoiced LSF parameters 25 25 Fourier magnitudes 8

• Gain (2 per frames)

8 8

• Pitch. overall voicing

7 7 Bandpass voicing 4

• Aperiodic flag

1

• Error protection
• 13

Sync bit 1 1

Total bits / 22.5 ms frame 54 54

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Bit transmission order

Page 25 of 23

Comparison of the 2400 BPS MELP with

• ther Standard Coders



Diagnostic Acceptability Measure



Two Conditions



Quiet



Office



Continuously Variable Slope Delta Modulation (CVSD)

○

16,000 bps



Code Excited Linear Prediction (CELP)

○

4800 bps

○

FS1016



Mixed Excitation Linear Prediction (MELP)

○

2400 bps

○

FIPS Publication 137



Linear Predictive Coding (LPC)

○

2400 bps Page 26 of 23

Comparison of the 2400 BPS MELP with other Standard Coders (cont’d)



Mean Opinion Score in Six Conditions Quiet



Anechoic Sound Chamber



Dynamic Microphone

Quiet - H250



Anechoic Sound Chamber



H250 Microphone

1% Random Bit Errors



Anechoic Sound Chamber



Dynamic Microphone

0.5% Random Block Errors



Anechoic Sound Chamber



Dynamic Microphone



50% Errors within a 35ms block

Office



Modern Office Environment



Dynamic Microphone

Mobile Command Environment



Field Shelter



EV M87 Microphone

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Comparison of the 2400 BPS MELP with other Standard Coders (cont’d)



Complexity with three Measurements



RAM



ROM



MIPS

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Page 29 of 23

LPC 10

Voice samples

Voice samples

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Original Sound MELP 1800 MELP 2000 MELP 2200