Data Acquisition

Figure 5.19Aliasing due to undersamplingIn the example of machine vibration analysis, the frequency components were clearlyvisible and constant. However, in the case of speech digitization or speech analysis, thedesired signal consists of many frequency components that vary quickly and unpredictably.An application may require spoken messages to be digitized and stored for later playback.As most speech is composed of frequency components below 5 kHz, digitizing the incomingsignal at 10 kHz appears to be adequate and places only low demand on memoryusage. Unfortunately, an attempt to digitize a message signal from a microphone in this wayresulted in the message so buried in extraneous hums, pops, and whines that it could hardlybe used. The frequency spectrum of the sampled signal is shown in Figure 5.20(a).In the assumption that high frequencies present on the input were aliasing down, a 5 kHz,antialiasing filter was put in place, leading to the spectrum in Figure 5.20(b). The spectrumshows little difference from the unfiltered signal’s spectrum. Increasing the sample rate (to100 kHz, Figure 5.20(c)) shows why: although attenuated, components above the filter’scutoff point are still present and do alias down. The filter had a roll-off of 24 dB/octave andthe real-world properties of the filter allowed the attenuated high-frequency components tofold down into the band of interest. Practically, solutions are using filters with greater roll-off(reducing the magnitude of high-frequency components that might alias) or sampling at ahigher rate (frequencies in the new sampling band do not fold down).