Li Tan and Jean Jiang DSP Fundamentals and Applications 2 nd Edition 2013

Li tan and jean jiang dsp fundamentals and

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Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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The Digital Signal Process (2) 12 There are many real-world DSP applications that do not require DAC. Data acquisition and digital information display, speech recognition, data encoding, and so on. Similarly, DSP applications that need no ADC include CD players, text-to-speech synthesis, and digital tone generators, among others. Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Basic DSP Examples In Block 13 Diagrams
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Digital Filtering 14 Let us consider the situation shown in Figure 3. Figure 3: The simple digital filtering block. A digitized noisy signal obtained from digitizing analog voltages (sensor output) containing a useful low-frequency signal and noise that occupies all of the frequency range. After ADC, the digitized noisy signal x(n) where n is the sample number, can be enhanced using digital filtering. DSP block operates as a simple digital lowpass filter. Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Digital Filtering (2) 15 After processing the digitized noisy signal x(n) the digital lowpass filter produces a clean digital signal y(n) . The cleaned signal y(n) can be applied to another DSP algorithm for a different application or convert it to the analog signal via DAC and the reconstruction filter. Typical applications of noise filtering include acquisition of clean digital audio and biomedical signals and enhancement of speech recording, among others. The digitized noisy signal and clean digital signal, respectively, are plotted in Figure 4. Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Digital Filtering (3) 16 Figure 4: (Top) Digitized noisy signal. (Bottom) Clean digital signal using the digital lowpass filter. Clean signal Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Signal Frequency (Spectrum) Analysis 17 As shown in Figure 5, certain DSP applications often require that time domain information and the frequency content of the signal be analyzed. Figure 5: Signal spectral analysis. Figure 6 (next slide) shows a digitized audio signal and its calculated signal spectrum (frequency content), that is, the signal amplitude versus its corresponding frequency, obtained from a DSP algorithm, called the fast Fourier transform (FFT). Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Signal Frequency (Spectrum) Analysis (2) 18 Figure 6: Audio signals and their spectrums. Li Tan and Jean Jiang, “DSP Fundamentals and Applications”, 2 nd Edition, 2013, Elsevier.
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Signal Frequency (Spectrum) Analysis (3) 19 The plot in Figure 6(a) is a time domain display of the recorded audio signal with a frequency of 1,000 Hz sampled at 16,000 samples per second, while the frequency content display of plot (b) displays the calculated signal spectrum versus frequency, in which the peak amplitude is clearly located at 1,000 Hz.
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