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SIGNAL PROCESSING: SOME APPLICATIONS IN SPEECH, MUSIC, and IMAGE PROCESSING

SIGNAL PROCESSING: SOME APPLICATIONS IN SPEECH, MUSIC, and IMAGE PROCESSING. Richard M. Stern 18-396 demo January 12, 2009 Department of Electrical and Computer Engineering and School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 15213. What is signal processing?.

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SIGNAL PROCESSING: SOME APPLICATIONS IN SPEECH, MUSIC, and IMAGE PROCESSING

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  1. SIGNAL PROCESSING: SOME APPLICATIONS IN SPEECH, MUSIC, and IMAGE PROCESSING Richard M. Stern 18-396 demo January 12, 2009 Department of Electrical and Computer Engineering and School of Computer Science Carnegie Mellon University Pittsburgh, Pennsylvania 15213

  2. What is signal processing? • Oppenheim and Schafer’s definition (1999): • [The discipline that is concerned with] the representation, transformation, and manipulation of signals and the information they contain

  3. Why perform signal processing? • To understand the content of signals • To represent signals in a form that is more insightful to us • To transform signals into a form that is more useful to us

  4. Representation of speech in time domain

  5. Representation of speech in frequency domain

  6. Signal representation: turning sine waves into square waves

  7. Pitch Pulse train source Vocal tract model Noise source Signal processing in human speech production: the source-filter model of speech A useful model for representing the generation of speech sounds: Amplitude p[n]

  8. Speech coding: separating the vocal tract excitation and and filter Original speech: Speech with 75-Hz excitation: Speech with 150 Hz excitation: Speech with noise excitation:

  9. Representation and filtering of speech sounds

  10. Linear filtering the waveform y[n] x[n] Filter 1: y[n] = 3.6y[n–1]+5.0y[n–2]–3.2y[n–3]+.82y[n–4] +.013x[n]–.032x[n–1]+.044x[n–2]–.033x[n–3]+.013x[n–4] Filter 2: y[n] = 2.7y[n–1]–3.3y[n–2]+2.0y[n–3–.57y[n–4] +.35x[n]–1.3x[n–1]+2.0x[n–2]–1.3x[n–3]+.35x[n–4]

  11. Filter 1 in the time domain

  12. Output of Filter 1 in the frequency domain Original: Lowpass:

  13. Filter 2 in the time domain

  14. Output of Filter 2 in the frequency domain Original: Highpass:

  15. What happens when we filter images?

  16. Lowpass filtering with a Gaussian kernel ….

  17. Not enough blur??

  18. We can also highpass filter ….

  19. … and threshold to detect the image edges

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