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Signal Processing Applications in Speech, Music, and Image Analysis

Discover the fundamental principles of signal processing and its applications in speech, music, and image processing. Learn how signals are represented, transformed, and manipulated to extract valuable information. Explore topics such as speech coding, linear filtering, and image edge detection.

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Signal Processing Applications in Speech, Music, and Image Analysis

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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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