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Pattern Recognition
Gerhard Schmidt
Christian-Albrechts-Universität zu KielFaculty of Engineering Institute of Electrical and Information EngineeringDigital Signal Processing and System Theory
Part 1: Introduction and Motivation
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 2
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Introduction and Motivation
Contents of the Lecture „Pattern Recognition“
❑ Speech and audio signal paths in a car
❑ Contents of the lecture
❑ Boundary conditions of the lecture (exercises, exam, etc.)
❑ Notation used in the lecture
❑ Literature
❑ Example of medical, speech, and audio signal processing
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 3
•
Introduction and Motivation
Speech and Audio Signal Paths in a Car – Part 1
Into the car
Out of the car
Within the car
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 4
•
Introduction and Motivation
Speech and Audio Signal Paths in a Car – Part 2
Signal processing inthe „receiving path“
Signal processing forenhancing the
communication quality and the sound impression
Signal processing inthe „sending path“
Speech dialogsystem and
phone
Music and audio
sources
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 5
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Introduction and Motivation
Contents of the Lecture (Entire Term)
❑ Preprocessing for improving the „noise robustness“
❑ Single-channel noise suppression
❑ Beamforming
❑ Pattern recognition (using speech and speaker recognition as an example)
❑ Basics of speech production
❑ Feature extraction
❑ Codebook generation
❑ Generation of Gaussian mixture models (GMMs)
❑ Hidden Markov models (HMMs)
❑ Enhancing the playback of audio signals
❑ Extending the bandwidth of speech signals (as application of codebooks)
❑ Loudspeaker equalization
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 6
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Introduction and Motivation
Boundary Conditions of the Lecture
❑ ECTS points
❑ 4 credit points
❑ Oral examination
❑ about 20 minutes per student
❑ After the term
❑ Talks (part of the exercise)
❑ About 10 minutes talk plus 5 minutes discussion
❑ Topics are available from now on
❑ Lecture slides
❑ Printed at the beginning of each lecture
❑ In the internet via dss.tf.uni-kiel.de
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 7
•
Introduction and Motivation
Notation – Part 1
Scalars:
❑ Signals:
❑ Impulse responses (time-variant):
❑ Example for a (real) convolution:
Vectors:
❑ Signal vectors:
❑ Impulse response vectors (time-variant) :
❑ Example for a real convolution:
Matrices:
Coefficient index
Boldface and uppercase
Boldface and lowercase
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 8
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Introduction and Motivation
Notation – Part 2
Random variables and processes:
❑ Notation:
❑ Probability density function:
❑ Stationary random processes:
❑ Expected values of stationary random processes:
No differences between deterministic signals and randomprocesses – different writing styles:
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 9
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Introduction and Motivation
Notation – Part 3
Auto and cross correlation for real, stationary random processes:
❑ Auto-correlation function:
❑ Cross-correlation function:
❑ (Auto) power spectral density:
❑ (Cross) power spectral density:
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 10
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Introduction and Motivation
Notation – Part 4
Stationary white noise:
❑ Auto-correlation function:
❑ Auto power spectral density:
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 11
•
Introduction and Motivation
Literature – Part 1
❑ E. Hänsler: Statistische Signale: Grundlagen und Anwendungen, Springer, 2001 (in German)
❑ A. Papoulis: Probability, Random Variables, and Stochastic Processes, McGraw-Hill, 1965
Statistical signal theory:
❑ E. Hänsler, G. Schmidt: Acoustic Echo and Noise Control, Wiley, 2004
❑ S. Haykin: Adaptive Filter Theory, Prentice Hall, 2002
❑ A. Sayed: Fundamentals of Adaptive Filtering, Wiley, 2004
Noise suppression, beamforming, adaptive filters:
❑ E. Hänsler, G. Schmidt: Topics in Acoustic Echo and Noise Control, Springer, 2006
❑ B. Iser, et al.: Bandwidth Extension of Speech Signals, Springer, 2008
❑ E. Hänsler, G. Schmidt: Speech and Audio Processing in Adverse Environments, Springer, 2008
❑ J. Benesty, et al.: Speech Enhancement, Springer, 2005
Application examples for speech processing:
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 12
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Introduction and Motivation
Literature – Part 2
Speech processing:
❑ L. R. Rabiner, R. W. Schafer: Digital Processing of Speech Signals, Prentice Hall, 1978
❑ P. Vary, U. Heute, W. Hess: Digitale Sprachsignalverarbeitung, Teubner, 1998 (in German)
❑ P. Vary, R. Martin: Digital Speech Transmission, Wiley, 2006
❑ L. R. Rabiner, R. W. Schafer: Introduction to Digital Speech Processing, Now, 2008
❑ B. Pfister, T. Kaufman: Sprachverarbeitung, Springer, 2008 (in German)
Audio processing:
❑ U. Zölzer: DAFX – Digital Audio Effects, Wiley, 2002
❑ E. Larsen, R. M. Aarts: Audio Bandwidth Extension, Wiley, 2004
❑ M. Talbot-Shmith: Audio Engineer‘s Reference Book, Focal Press, 1998
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 13
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 1
Hands-free telephony:
❑ Echo cancellation as well as noise and residual echo suppression
❑ Double talk and barge-in (interrupting a speech dialog system)
Medical signal processing:
❑ Brain computer interfaces
Speech recognition:
❑ Applications for a mobile phone
Audio signal processing:
❑ Loudspeaker equalization
❑ Demo of KiRAT (Kiel Real-time Audio Toolkit)
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 14
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 2
Example 1
Hands-Free Telephony
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 15
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 3
( )y n
+
Noise and residual echo suppression
Echocancellation
Hands-free telephony – a basic system:
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 16
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 4
Transmission to thecommunication partner
(channel delay: about 180 ms)
Remote communication
partner
Received signal(„Hearing channel“ of the remote communication partner)
Initial filter convergence:
Adaptation at thebeginning of the call
Without Wiener filter
With Wiener filter
Enclosure dislocations:
Stereo signals (16 kHz):
Left:
Receivedsignal ...
Right:
Sentsignal ...
... of the remote communication partner
Double talk:
Both partners speak simultaneously
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 17
•
Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 7
Example 2
Pattern Recognition forMedical Applications
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 18
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 8
Electro-encephalography(EEG)
Magneto-encephalography(MEG)
Electro-cardiography
(ECG)
Magneto-
cardiography
(MCG)
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 19
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 9a
❑ Helping medical doctors to distinguish better between deseases
❑ „Conventional“ measures
❑ Establishment of so-called early biomarkers
❑ To localize areas of interest in the heart or in the brain
❑ Networks that cause epilepctic seizures, etc.
❑ Unwanted „exciation channels“in the heart
❑ Brain-computer interfaces
❑ Control of electronic devices for handicapped people
What are these measures good for?
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 20
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 9b
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 21
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 10
Example 3
Speech Recognition
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 22
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 11
Video from/with:
❑ Raymond Brückner (SVOX)❑ Andreas Löw (SVOX)❑ Patrick Langer (SVOX)
Link to video
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 23
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 12
Example 4
Audio Signal Processing
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 24
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Introduction and Motivation
Application Examples from Medical, Speech, and Audio Processing – Part 13
Digital Signal Processing and System Theory | Pattern Recognition | Introduction Slide 25
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Introduction and Motivation
Summary and Outlook
Summary:
❑ Speech and audio signal paths in a car
❑ Contents of the lecture
❑ Boundary conditions of the lecture (exercises, exam, etc.)
❑ Notation used in the lecture
❑ Literature
❑ Example of medical, speech, and audio signal processing
Next week:
❑ Noise suppression