mbe - sharif

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MBE Vocoder

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Outline

Introduction to vocoders

MBE vocoder

– MBE Parameters

– Parameter estimation

– Analysis and synthesis algorithm

AMBE

IMBE

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Vocoders - analyzer

1. Speech analyzed first by segmenting speech using a

window (e.g. Hamming window)

2. Excitation and system parameters are calculated for

each segment

1. Excitation parameters : voiced/unvoiced, pitch period

2. System parameters: spectral envelope / system impulse

response

3. Sending this parameters

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Vocoders - Synthesizer

System

parameters

Excitation Signal

White noise/ unvoiced

Pulse train/voiced

Synthesized

voice

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Vocoders

But usually vocoders have poor quality– Fundamental limitation in speech models

– Inaccurate parameter estimation

– Incapability of pulse train/ white noise to produce all voice

• speech synthesized entirely with a periodic source exhibits a “buzzy” quality, and speech synthesized entirely with a noise source exhibits a “hoarse” quality

Potential solution to buzziness of vocoders is to use of mixed excitation models

In these vocoders periodic and noise like excitations are mixed with a calculated ratio and this ration will be sent along the parameters

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Multi Band Excitation Speech

Model

Due to stationary nature of a speech signal, a window w(n) is usually applied to signal

The Fourier transform of a windowed segment can be modeled as the product

of a spectral envelope and an excitation spectrum

In most models is a smoothed version of the original speech spectrum

)(ws

)(wH

)()()( nsnwnsw

|)(| wE

|)(|)()(ˆ www EHs

)(wH )(ws

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MBE model (Cont’d)

the spectral envelope must be represented accurately enough

to prevent degradations in the spectral envelope from

dominating.

– quality improvements achieved by the addition of a frequency

dependent voiced/unvoiced mixture function.

In previous simple models, the excitation spectrum is totally

specified by the fundamental frequency w0 and a

voiced/unvoiced decision for the entire spectrum.

In MBE model, the excitation spectrum is specified by the

fundamental frequency w0 and a frequency dependent

voiced/unvoiced mixture function.

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Multi Banding

In general, a continuously varying frequency dependent voiced/unvoiced mixture function would require a large number of parameters to represent it accurately. The addition of a large number of parameters would severely decrease the utility of this model in such applications as bit-rate reduction.

To further reduce the number of these binary parameters, the spectrum is divided into multiple frequency bands and a binary voiced/unvoiced parameter is allocated to each band.

MBE model differs from previous models in that the spectrum is divided into a large number of frequency bands (typically 20 or more), whereas previous models used three frequency bands at most .

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Multi Banding

Original

spectrum

Spectral

envelope

Periodic

spectrum

V/UV

information

Noise

spectrum

Excitation

spectrum

Synthetic

spectrum

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MBE Parameters

The parameters used in MBE model are:

1. spectral envelope

2. the fundamental frequency

3. the V/UV information for each harmonic

4. and the phase of each harmonic declared voiced. The phases of harmonics in frequency bands declared unvoiced are not included since they are not required by the synthesis algorithm

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Parameter Estimation

In many approaches (LPC based algorithms) the algorithms for

estimation of excitation parameters and estimation of spectral envelope

parameters operate independently.

These parameters are usually estimated based on heuristic criterion

without explicit consideration of how close the synthesized speech will

be to the original speech.

– This can result in a synthetic spectrum quite different from the

original spectrum.

In MBE the excitation and spectral envelope parameters are estimated

simultaneously so that the synthesized spectrum is closest in the least

squares sense to the spectrum of the original speech “analysis by

synthesis”

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Parameter Estimation (Cont’d)

the estimation process has been divided into two

major steps.

1. In the first step, the pitch period and spectral

envelope parameters are estimated to minimize the

error between the original spectrum and the synthetic

spectrum.

2. Then, the V/UV decisions are made based on the

closeness of fit between the original and the synthetic

spectrum at each harmonic of the estimated

fundamental.

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Parameter Estimation (cont’d)

The parameters estimated by

minimizing the following error

criterion:

– Where

The error in an interval

is minimized at:

dss ww

2

)(ˆ)(2

1

|)(|)()(ˆ www EHs

dEAsm

m

b

a

Wmwm

2

)()(2

1

dEw

dEwSw

Am

m

m

m

b

a

b

a

m 2

)(

)()(

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Pitch Estimation and Spectral

Envelope

An efficient method for obtaining a good approximation for the periodic transform P ( w ) in this interval is to precompute samples of the Fourier transform of the window w (n) and center it around the harmonic frequency associated with this interval.

For unvoiced frequency intervals, the envelope parameters are estimated by substituting idealized white noise (unity across the band) for |E (a)| in previous formulas which reduces to averaging the original spectrum in each frequency interval.

For unvoiced regions, only the magnitude of A, is estimated since the phase of A, is not required for speech synthesis.

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More about pitch estimation

Experimentally, the error E tends to vary slowly

with the pitch period P

the initial estimate is obtained by evaluating the

error for integer pitch periods

Since integer multiples of the correct pitch period

have spectra with harmonics at the correct

frequencies, the error E will be comparable for the

correct pitch period and its integer multiples.

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More about pitch estimation

(Cont’d)

Speech

segment

Original

spectrum

Error/Pitch

Original and

Synthetic

P=42.48

Original and

Synthetic

P=42

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V/UV Decision

The voiced/unvoiced decision for each harmonic is made by comparing the normalized error over each harmonic of the estimated fundamental to a threshold

When the normalized error over mth harmonic is below the threshold, this frame will be marked as voiced else unvoiced

dSwm

m

b

a

m

m 2

)(2

1

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Analysis Algorithm Flowchart

Window

Speech

segment

start

Compute error vs. pitch period

Autocorrelation approach

Select initial pitch period

(Dynamic programming

Pitch tracker)

Refine initial pitch period

(frequency domain approach)

Make V/UV decision for each

Frequency band

Select V/UV spectral

Envelope parameters

For each freq. band

Stop

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Speech Synthesis

The voiced signal can be synthesized as the sum of

sinusoidal oscillators with frequencies at the harmonics of

the fundamental and amplitudes set by the spectral

envelope parameters (The time domain method).

The unvoiced signal can be synthesized as the sum of

bandpass filtered white noise

The frequency domain method was selected for

synthesizing the unvoiced portion of the synthetic speech.

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Synthesis algorithm block diagram

Separate

Voiced/Unvoiced

Envelope samples

Bank of

Harmonic

oscillators

STFT Replace

envelope

Weighted

Overlap-add

Linear

interpolation

V/UV

Decision

Envelope

samples

Voiced envelope

samples

Unvoiced envelope

samples

Voiced envelope

samples

Unvoiced envelope

samples

Voiced

speech

Unvoiced envelope

samples

White noise

sequence

Unvoiced

speech

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MBE Synthesis algorithm

First, the spectral envelope samples are separated into voiced or unvoiced spectral envelope samples depending on whether they are in frequency bands declared voiced or unvoiced

Voiced envelope samples include both magnitude and phase, whereas unvoiced envelope samples include only the magnitude.

Voiced speech is synthesized from the voiced envelope samples by summing the outputs of a band of sinusoidal oscillators running at the harmonics of the fundamental frequency

m

mmv ttAts ))(cos()()(ˆ

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MBE Synthesis algorithm (Voiced)

The phase function is determined by an initial

phase and a frequency track as follows:

The frequency track is linearly interpolated

between the mth harmonic of the current frame and

that of the next frame by:

m

0 )(tm

0

0

)()( t

mm dt

)(tm

mmS

tSm

S

tSmt

)(

)()0()( 00

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MBE Synthesis algorithm

(Unvoiced)

Unvoiced speech is synthesized from the unvoiced envelope samples by first synthesizing a white noise sequence.

For each frame, the white noise sequence is windowed and an FFT is applied to produce samples of the Fourier transform

In each unvoiced frequency band, the noise transform samples are normalized to have unity magnitude. The unvoiced spectral envelope is constructed by linearly interpolating between the envelope samples |Am(t)|.

The normalized noise transform is multiplied by the spectral envelope to produce the synthetic transform. The synthetic transforms are then used to synthesize unvoiced speech using the weighted overlap-add method.

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MBE Synthesis (Cont’d)

The final synthesized speech is generated by summing the

voiced and unvoiced synthesized speech signals

+Synthesized

speech

Voiced

speech

Unvoiced

speech

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Bit Allocation

Parameter Bits

Fundamental

Frequency

9

Harmonic

Magnitude

139-94

Harmonic

Phase

0-45

Voiced/Unvoiced

Bits

12

Total 160

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Advanced MBE (AMBE)

MBE coding rate at 2400 bps

AMBE coding rate at 1200/2400 bps

Four new features

1. Enhanced V/UV decision

2. Initial pitch detection

3. Refined pitch determination

4. Dual rate coding

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Enhanced V/UV decision

divide the whole speech frequency band

into 4 subbands and 2 subbands for 2.4 kbps

and 1.2 kbps respectively.

That is to say only 4 bits and 2 bits are used

to encode U/V decisions for 2.4 kb/s and

1.2 kb/s vocoder respectively.

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Initial pitch detection

MBE takes 2 steps to detect the refined initial pitch period

– Spectrum matching technique to find the initial pitch period

– Using DTW-based (Discrete Time Wrapping) technique to

smooth the estimation

Computational complexity is very high

In MBE, a modified three-level center clipped auto-

correlation method is used to detect the initial pitch period,

and also use a simple smoothing method to correct the

pitch errors.

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Redefined pitch determination

To find the best pitch the basic method is to compute the error between the original speech spectrum and the shaped voiced speech spectrum by first supposing a pitch period

The supposed pitch of which the spectrum error is minimum is chosen as the last pitch

To reduce the computational complexity, AMBE uses a 256- point FFT to get the speech spectrum, and 5-point window spectrum is used to form the voiced harmonic spectrum.

To get the refined pitch, AMBE perform seven times of spectrum matching process. In every time. AMBE first set a supposed pitch, then shape a harmonic spectrum over the overall frequency band according to the supposed pitch and window spectrum, and an error can be calculated by subtracting the shaped spectrum from speech spectrum. After the seven times of matching process, the refined pitch can easily be determined

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Dual rate coding

Parameter 2400 bps 1200 bps

Pitch

quantization

8 6

V/UV decision 4 2

Amplitude

quantization

41 19

total 53 27

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Improved MBE (IMBE)

A 2400 bps coder based on MBE

Substantially better than U.S government

standard LPC-10e

The parameters of the MBE speech model :

– the fundamental frequency

– voiced/unvoiced information

– the spectral envelope.

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IMBE algorithm

estimate the excitation and system parameters

which minimize the distance between the original

and synthetic speech spectra (analysis by

synthesis)

Once these parameters are estimated,

voiced/unvoiced decisions are made by

comparing the spectral error over a series of

harmonics to a prescribed threshold

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IMBE block diagram

IMBE algorithm block diagram

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IMBE Coding

IMBE offered in 2.4, 4.8 and 8.0 kbps

Analysis and synthesis routines are the same except the bit allocation

The fundamental frequency needs accuracy of about l Hz. and requires about 9 bits per frame.

The V/UV decisions are encoded with one bit per decision.

The remaining bits are allocated to error control and the spectral envelope information.

mbe - sharif

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