5/17/2012 1 Digital Signal Processing: Digital Signal Processing: An Introduction and Some Examples of its Everyday Use Dr D. H. Crawford EPSON Scotland Design Centre Contents • What is DSP? • What is DSP used for? What is DSP used for? – Speech & Audio processing – Image & Video processing – Adaptive filtering • DSP Devices and Architectures Slide 2 • DSP at EPSON Scotland Design Centre • Summary & Conclusions
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5/17/2012
1
Digital Signal Processing:Digital Signal Processing:An Introduction and Some Examples of its
Everyday Use
Dr D. H. Crawford
EPSON Scotland Design Centre
Contents
• What is DSP?
• What is DSP used for?What is DSP used for?– Speech & Audio processing
– Image & Video processing
– Adaptive filtering
• DSP Devices and Architectures
Slide 2
• DSP at EPSON Scotland Design Centre
• Summary & Conclusions
5/17/2012
2
What is DSP?
• Digital Signal Processing – the processing or manipulation of signals using digital p g g gtechniques
ADC DACDigital Signal
ProcessorA l Di it l t
Input Signal
Output Signal
Slide 3
ProcessorAnalogue to Digital Converter
Digital to Analogue Converter
What is DSP Used For?
Slide 4
…And much more!…And much more!
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Speech Processing
• Speech coding/compression
Slide 5
• Speech synthesis
• Speech recognition
Some Properties of Speech
The blue--- s---p--o---------t i-s--on--the-- k--ey a---g--ai----n------
Slide 6
“oo” in “blue”“o” in “spot”“ee” in “key”“e” in “again”“s” in “spot”“k” in “key”
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Some Properties of Speech
Vowels
“ee” in “key”“o” in “spot”“oo” in “blue” “e” in “again”
Consonants
•Quasi-periodic
•Relatively high signal power
Slide 7
“s” in “spot” “k” in “key”
•Non-periodic (random)
•Relatively low signal power
Speech Coding
TRAU
BSC
MSC64 kbits/s
13 kbi /
22.8 kbits/s
Slide 8
BTS
13 kbits/s
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Speech Coding – Linear Prediction
• Try to predict the current sample value;
• Transmit the prediction error.
A(z)
s(n)+
– d(n)
se(n)… d(n)
A(z)
++
se(n)
sr(n)
Transmit the prediction error.
Slide 9
e( )
Speech Coding – Vocoder
Original Speech
Analysis:
Encoder
Pulse Train V/U
Decoder
Analysis:• Voiced/Unvoiced decision• Pitch Period (voiced only)• Signal power (Gain)
Signal PowerPitch
Period
Slide 10
G
Pulse Train
Random Noise
Vocal TractModel
V/U
Synthesized SpeechLPC-10:
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To be ornot to bethat is thequestion
Tu bee awrnawt tu beedhat iz dhekwestchun
phonetic formInputtext
Text-to-Speech Synthesis
Textnormalization
expandsabbreviationsdates, times,money..etc
Parsing Pronunciation
kwestchun
semantic &syntactic ‘partsof speech’ analysis of text
phonetic descriptionof each word, dictionarywith letter-to-sound rules as a back up
Slide 11
Prosodyrules
Waveformgeneration
Synthesized speech
Apply wordstress, durationand pitch
Phonetic-to-acoustictransformation
Text-to-speech synthesis sounds very natural these days.
Speech Synthesis Applications
• Speaking clocks
S k ( i bl ) t• Spoken (variable) announcements
• Talking emails + talking heads for mobile
• Synthesis of location-based information (e.g. traffic information)
• Interactive systems (e g catalogue ordering
Slide 12
• Interactive systems (e.g. catalogue ordering, Yellow Pages, ...)
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Speech/Speaker Recognition• Speech Recognition – What has been spoken?
– Speaker dependent – Recognition system trained f ti l ’ ifor a particular person’s voice.
– Speaker independent – Recognition system expected to deal with a wide variety of speakers.