Frequency analysis
Shock metrology
Piezoelectric transducers
Signal conditioning

Frequency analysis

Signal and frequency

Four examples of the relationship between the waveform of a signal in the time domain compared to its spectrum in the frequency domain.

single frequency

sum frequency

square signal

random signal

  • In the top figure a sine wave of large amplitude and wavelength is showing up as a single frequency with a high level at a low frequency.
  • The second figure shows how a sum of two signals shows up as a sum also in the frequency domain .
  • The third figure shows a square signal (complex but periodic).
  • The last figure shows a random signal (complex and not periodical).

    Most natural sound signals are complex in shape. The primary result of a frequency analysis is to show that the signal is composed of a number of discrete frequencies at individual levels present simultaneously.

    The number of discrete frequencies displayed is a function of the accuracy of the frequency analysis which normally can be defined by the user.


    To analyze a sound signal, frequency filters or a bank of filters are used. If the bandwidths of these filters are small a highly accuracy analysis is achieved.

    The signal flow chart shown illustrates the elements in a simple sound level meter.

    On top is a microphone for signal pick up. Then a gain amplification stage followed by a single frequency filter - here shown as an ideal filter. In the following we will look at real filters. After filtering follows a rectifier with the standardised time constants Fast, Slow and Impulse and the signal level is finally converted to dB and shown on the display.

    Bandpass Filters and Bandwidth

    Ideal and real filters
    Practical and ideal filter - I: Ideal filter - P: Practical filter - R: Ripple

    Ideal filters are only a mathematical abstraction. In real life, filters do not have a flat top and and vertical sides. The departure from the idealised flat top is described as an amount of ripple. The bandwidth of the filter is described as the difference between the frequencies where the level has dropped 3 dB in level corresponding to 0.707 in absolute measures.

    It is useful to define a Noise Bandwidth for a filter. This corresponds to an ideal filter of the same level as the real filter, but with its bandwidth (Noise Bandwidth) set to leave the two filters with the same 'area'.

    Treatment and Fourier transform

    The figure below shows the influence of the sampling and its spectrum (calculated by Fourier tranform).

    Integral Fourier transform
    Integral Fourier trandform.

    Fourier series
    Fourier series.

    Sampled functions
    Sampled functions - Discrete in time and periodical in frequency

    Discrete Fourier transform
    Discrete Fourier transform - Discrete and periodical in time and frequency

    The transformation of a continuous wave in a succession of discrete points introduces a periodicity in the spectrum. If the signal is periodic in time, the spectrum is then also discrete.