chapter 10 applications of digital signal processing wang weilian [email protected] school of...

CHAPTER 10 Applications of Digital Signal

Processing

CHAPTER 10 Applications of Digital Signal

Processing

Wang Weilian

[email protected]

School of Information Science and Technology

Yunnan University

云南大学滇池学院课程：数字信号处理 Applications of Digital Signal Processing 2

OutlineOutline

• Speech Signals Processing

• Dual-Tone Multifrequency Signal Detection


Speech Signals ProcessingSpeech Signals Processing

• Speech Analysis

– parameterize the speech signal

• To reduce the bandwidth

• To characterize the speech signal with only a few features

– Speech Signal Processing is one of the kernel technologies in those fields as follows: Information Superhighway, Multimedia, OAS (office automation system), Modern Communications System, Intelligent System and so on.



• Speech Analysis — time-domain

– data windowing: ( ) ( ) ( )wm

s n s m w n m

Windowing Calculation




– Energy:

• Selecting 10ms ~ 30ms as the length of the window in general

12 2[ ( ) ( )] [ ( ) ( )]

n N

nm m n

E x m w n m x m w n m

2 2 2 2[ ( ) ( )] [ ( ) ( )] ( ) ( )nm m

E x m w n m x m h n m x n h n

2( ) ( )h n w n




– The zero crossing rate ( ZCR ):

• where

| sgn[ ( )] sgn[ ( 1)] | ( )

|sgn[ ( )] sgn[ ( 1)]| ( )

nm

Z x n x n w n m

x n x n w n

1 ( 0)sgn[ ]

0 ( 0)

1/ 2 (0 1)( )

0

xx

x

N n Nw n

other




– Energy and ZCR:

Energy and ZCR




– The Autocorrelation function:

• If , then:

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

R k x m w n m x m k w n m k

( ) ( ) [ ( ) ( ) ( ) ( ( ))]n nm

R k R k x m w n m x m k w n m k

( ) ( ) ( )kh n w n w n k

( ) [ ( ) ( )] ( ) [ ( ) ( )] ( )n k km

R k x m x m k h n m x n x n k h n




– The Autocorrelation function:

The block diagram of the autocorrelation function



• Speech Analysis — frequency-domain

– Fourier Transform and Spectrogram

( ) ( ) ( )j j mn

m

X e x m w n m e

The filter-explanation of the FT




– The spectrogram:

The spectrogram




– Spectra analysis

• The power spectra ( energy density function ):

• Complex Ceptrum:

• Quefrency:

2( ) ( ) ( ) | ( ) |j j j jn n n nS e X e X e X e

1( ) [ln[ [ ( )]]]x n Z Z x n

[ ( ) ( )]( )

2

x n x nc n




– Linear Predictive:

• The model of signal generation

Autoregressive Moving Average Model




– Linear Predictive:

• G is the gain factor.

• U(z) / S(z) is the Z-Transform of input / output sequence

1

1

1( )

( )( )

1

qj

jjp

ii

i

b zS z

H z GU z

a z

01 0

( ) ( ) ( ) ( 1)p q

i ji j

s n a s n i G b u n j b

，




Linear Predictive ( autocorrelation method )



• Speech Synthesis

– Formant Synthesis: the transfer function of formants can be simulated by using a 2th-order digital filter generally.

( ) ( ) ( 1) ( 2)y n Ax n By n Cy n

exp( 2 ),

2exp( )cos(2 ),

1

w

w

C B T

B B T FT

A B C

, and .wF is the formant frequency B is the bandwidth



• Speech Recognition

Communication via Spoken Language


Dual-Tone Multifrequency Signal DetectionDual-Tone Multifrequency Signal Detection

• A DTMF signal consist of a sum of two tones with frequencies taken from two mutually exclusive groups of preassigned frequencies.

• Each pair of such tones represents a unique number or a symbol.


Dual-Tone Multifrequency Signal DetectionDual-Tone Multifrequency Signal Detection

• Decoding of a DTMF signal thus involves identifying the two tones in that signal and determining their corresponding number or symbol.

• The DTMF decoder computes the DFT samples closest in frequency to the eight DTMF fundamental tones and their respective second harmonics.

• The DFT length N determines the frequency spacing between the locations of the DFT samples and the time it takes to compute the DFT sample. The frequency corresponding to the DFT index ( bin number ) k is:

, 0,1, , 1k Tf kF N k N

chapter 10 applications of digital signal processing wang weilian [email protected] school of...

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