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CHAPTER 10 Applications of Digital Signal Processing

Wang Weilian [email protected]

School of Information Science and Technology Yunnan University

Outline

•

Speech Signals Processing

•

Dual-Tone Multifrequency Signal Detection

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 2

Speech 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.

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 3

Speech Signals Processing

•

Speech Analysis — time-domain

–

data windowing:

s w

m

   ) 云南大学滇池学院课程:数字信号处理

Windowing Calculation Applications of Digital Signal Processing 4

Speech Signals Processing

•

Speech Analysis — time-domain

–

Energy:

E n

m

   )] 2 

n N

1 

E n

  

m

 [ ( ) 2 ( )]   

m

 [ ( ) (  ( ) )] 2 )]  ( )  •

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

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 5

Speech Signals Processing

•

Speech Analysis — time-domain

–

The zero crossing rate ( ZCR ):

Z n

 

m

     ) •

where

云南大学滇池学院课程:数字信号处理   1 ( 0 (

x x

  0) 0)  0

other N

1)

Applications of Digital Signal Processing 6

Speech Signals Processing

•

Speech Analysis — time-domain

–

Energy and ZCR:

云南大学滇池学院课程:数字信号处理

Energy and ZCR Applications of Digital Signal Processing 7

Speech Signals Processing

•

Speech Analysis — time-domain

–

The Autocorrelation function:

n

  

m

  (  ) 

n

R n

(

k m

   ( ( ) ( •  )

n

m

   ( )   (  ))]  ( )] 

k

))] 云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 8

Speech Signals Processing

•

Speech Analysis — time-domain

–

The Autocorrelation function: The block diagram of the autocorrelation function

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 9

Speech Signals Processing

•

Speech Analysis — frequency-domain

–

Fourier Transform and Spectrogram

X n

(

e j

 )   

m

  )  云南大学滇池学院课程:数字信号处理

The filter-explanation of the FT Applications of Digital Signal Processing 10

Speech Signals Processing

•

Speech Analysis — frequency-domain

–

The spectrogram:

云南大学滇池学院课程:数字信号处理

The spectrogram Applications of Digital Signal Processing 11

Speech Signals Processing

•

Speech Analysis — frequency-domain

–

Spectra analysis

•

The power spectra ( energy density function ):

n j

 ) 

n j

 )

n j

 )   |

n j

 ) | 2 •

Complex Ceptrum:

 

Z

 1 •

Quefrency:

  2 

x

(

n

)] 云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 12

Speech Signals Processing

•

Speech Analysis — frequency-domain

–

Linear Predictive:

•

The model of signal generation

云南大学滇池学院课程:数字信号处理

Autoregressive Moving Average Model Applications of Digital Signal Processing 13

Speech Signals Processing

•

Speech Analysis — frequency-domain

–

Linear Predictive:

1 

j q

  1 1 

i p

  1 

j

i

 •

G is the gain factor.

•

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

i p

  1

i G j q

  0

j

j

,

b

0  1) 云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 14

Speech Signals Processing

•

Speech Analysis — frequency-domain

云南大学滇池学院课程:数字信号处理

Linear Predictive ( autocorrelation method ) Applications of Digital Signal Processing 15

Speech Signals Processing

•

Speech Synthesis

–

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

   2)

C

 

B

 2 exp(   

A B C

), ) cos(2 

FT

), , and

w

.

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 16

Speech Signals Processing

•

Speech Recognition

云南大学滇池学院课程:数字信号处理

Communication via Spoken Language Applications of Digital Signal Processing 17

Dual-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.

云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 18

Dual-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:

f k

kF T N

,

k

 0,1, ,

N

 1 云南大学滇池学院课程:数字信号处理

Applications of Digital Signal Processing 19