Pattern Recognition Part 1: Introduction and Motivation Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical and Information Engineering Digital Signal Processing and System Theory
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 Slide 2 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Speech and Audio Signal Paths in a Car – Part 1 Into the car Within the car Out of the car Slide 3 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Speech and Audio Signal Paths in a Car – Part 2 Signal processing in the „receiving path“ Music and audio sources Signal processing for enhancing the communication quality and the sound impression Speech dialog system and phone Signal processing in the „sending path“ Slide 4 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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 Slide 5 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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 Slide 6 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Notation – Part 1 Scalars: ❑ Signals: Coefficient index ❑ Impulse responses (time-variant): ❑ Example for a (real) convolution: Vectors: Boldface and lowercase ❑ Signal vectors: ❑ Impulse response vectors (time-variant) : ❑ Example for a real convolution: Matrices: Boldface and uppercase Slide 7 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Notation – Part 2 Random variables and processes: ❑ Notation: No differences between deterministic signals and random processes – different writing styles: ❑ Probability density function: ❑ Stationary random processes: ❑ Expected values of stationary random processes: Slide 8 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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: Slide 9 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Notation – Part 4 Stationary white noise: ❑ Auto-correlation function: ❑ Auto power spectral density: Slide 10 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Literature – Part 1 Statistical signal theory: ❑ E. Hänsler: Statistische Signale: Grundlagen und Anwendungen , Springer, 2001 (in German) ❑ A. Papoulis: Probability, Random Variables, and Stochastic Processes , McGraw-Hill, 1965 Noise suppression, beamforming, adaptive filters: ❑ 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 Application examples for speech processing: ❑ 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 Slide 11 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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 Slide 12 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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) Slide 13 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 2 Example 1 Hands-Free Telephony Slide 14 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 3 Hands-free telephony – a basic system: Echo cancellation Noise and residual echo suppression ( ) y n + Slide 15 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 4 Transmission to the Stereo signals (16 kHz): communication partner (channel delay: about 180 ms) Right: Left: Received Sent signal ... signal ... ... of the remote communication partner Remote Received signal communication („Hearing channel“ of the remote communication partner) partner Initial filter convergence: Double talk: Enclosure dislocations: Adaptation at the Both partners Without Wiener filter beginning of the speak With Wiener filter call simultaneously Slide 16 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 7 Example 2 Pattern Recognition for Medical Applications Slide 17 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 8 Electro- Magneto- encephalography cardiography (EEG) (MCG) Magneto- encephalography (MEG) Electro- cardiography (ECG) Slide 18 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 9a What are these measures good for? ❑ 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 Slide 19 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 9b Slide 20 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
Introduction and Motivation • Application Examples from Medical, Speech, and Audio Processing – Part 10 Example 3 Speech Recognition Slide 21 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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 Slide 22 Digital Signal Processing and System Theory | Pattern Recognition | Introduction
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