Analyze This

Dec 1, 2007 12:00 PM, By Larry the O

Spectral Analysis

After level, the most common analysis task is spectral analysis. Spectral-analysis tools show the amplitude of each frequency component of a signal. The analysis parameters are key to determining frequency resolution and accuracy across the spectrum. FFT spectrum analyzers, the most common type, are x-y displays that show frequency on the x-axis and amplitude on the yaxis (see Fig. 4). FFTs can be read intuitively: if you have a lot of energy at or around one frequency, that fact is self-evident from the higher readout shown at that frequency.

That is not to say, however, that spectrum analyzers are only narrowly useful. You can look at both RMS and peak spectral analyses with them and use hold functions in the way that they are used with level metering. Using hold functions can be very helpful when you are trying to pinpoint problem frequencies that aren't easily identified by ear. Since spectrum analyzers usually give a readout of the frequency and level corresponding to the current position of the cursor, you can easily home in on where a problem lies by looking at the hold display and moving the cursor where there are anomalies.

Weighty Matters

Many spectrum analysis tools offer A-, B-, and C-weighting curves, which make the analyzer read more in the way that sound is perceived. Human hearing response is not linear across frequency, so when a spectrum analyzer shows equal levels of high and low frequencies in a signal, it is likely to sound as though there is more treble than bass. Worse, the frequency response of this filtering effect in human perception changes with level.

Weighting curves apply filters to the signal that is routed to the analyzer in order to bring the readings more in line with how sound is perceived. A-weighting initially approximated the 40 dB Fletcher-Munson equal-loudness curve; B-weighting (rarely used), the 70 dB curve; and C-weighting, the 100 dB curve. In that method, you used the appropriate curve for the source level (see Fig. 5). That idea mutated, though, and there was a movement to standardize level measurement around Aweighting only. Today, weighting curves are often chosen for their appropriateness to the application rather than to the listening level. For instance, Aweighting has long been used in outdoor measurements of ambient sound, in which people easily tune out continuous, low-frequency background sounds.

Third-Octave Analysis

Third-octave analyzers show the spectrum broken down into ISO third-octave bands, a display familiar to those who have used hardware third-octave RTAs (real-time analyzers). Third-octave analyzers are useful for getting a feel for the overall shape of the spectrum. Third-octave bands, however, are not fine enough to pinpoint many problems, and their center frequencies do not closely relate to the harmonic relationships that dominate musical and acoustical signals.

There is an important distinction between FFT spectrum analyzers and third-octave equalizers. FFT analysis produces a linearly spaced response; that is, it breaks the spectrum into bands with spacing that has a fixed number of hertz. White noise, which has equal energy per frequency, shows a flat response when viewed with an FFT analyzer.

In contrast, third-octave analysis produces a logarithmically spaced representation, based on a division of an octave. Pink noise, which has equal energy per octave, shows a flat response when viewed with a third-octave analyzer. White noise shows much more energy in the higher octaves than the lower ones, looking like a low-frequency rolloff.

The Colorful Spectrogram

A spectrogram (sometimes called “sonogram”) shows a spectral history, a continuous 3-axis record of FFTs performed on an incoming signal. The spectrogram shows time along one axis and frequency along the second axis and uses color (the third axis) to show level.

Spectrograms are used heavily in speech research but are useful also for studio work, and they're easy to read once you are accustomed to them. For example, it's not hard to see where the beat is in a typical pop song: sharp, regularly spaced lines along the spectrum indicate transients that are probably the snare, and cymbal crashes can be seen by the smear in the high frequencies that follow some snare hits. Other transient events, such as a cough or a door-close during a live recording, can also be spotted (see Fig. 6). Spectrograms are helpful in comparing the spectra of different songs. Note that the larger the FFT size, the more history is shown in a spectrogram.

Phase and Stereo-Image Meters

Phase and stereo-image meters illustrate the relative time relationships between the left and right channels of the stereo signal. The simplest of these tools is the stereo-balance meter, a horizontal strip showing power distribution between the two channels. The stereo-balance meter can be useful for balancing stereo tracks of sections, such as background vocals and strings, and for checking stereo-miking techniques.

Phase monitoring comes in several forms, the most familiar being the Lissajous display — a simple x-y display in which each axis shows the instantaneous level of one channel. If the display shows a line (or, more often, in practice, a narrow oval) pointing from lower left to upper right, the material in both channels is very much in phase, meaning that the signal should be highly mono compatible. If the display shows a straight line pointing from upper left to lower right, then that means there are identical signals in each channel, but with opposite polarities, which usually is not good. A somewhat fatter oval is the most common, and a circle would indicate that you have as much out-of-phase material as you would want in a mix.

A vector scope works similarly to a Lissajous, except that the display has been rotated 45 degrees counterclockwise, so that in-phase behavior is shown by a line going straight up and down, and out-of-phase material is shown by a horizontal line.