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Analyze This

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

 

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FIG. 1: This BIAS Reveal display shows peakand RMS power histories for each channel.Notice the song fade-out at the right of thedisplay and the roughly 12 dB crest factor.

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We rightly rely on our ears as thefinal arbiters of recording andmixing issues. Sometimes,however, identifying the cause of an audible production problem byear can be difficult. Metering andanalysis tools providequantitative information thatallows us to observe an audiosignal from various perspectives,which can be the key to avoidingproblems and identifying peskyartifacts.

Today, analysis tools haveemerged from researchlaboratories to becomeaffordable and r eadily availablein plug-in suites such as BIASReveal (part of the company's Master Perfection Suite) and Roger Nichols Digital Inspector XL.The tools are embedded in editors and processing software like Steinberg WaveLab, iZotopeOzone, and Sony Creative Software Sound For ge. You also can find them in standalone form in

the godfather of modern desktop audio analysis, Metric Halo SpectraFoo, as well as in newer entries such as Audiofile Engineering Spectre and SuperMegaUltraGroovy FuzzMeasure Pro.You can even find shareware and freeware analysis tools, including a scaled-down Inspector from RND.

 Although the tools are available, how many people really know how to use them? Most usersunderstand level meters and know the difference between peak and RMS (averaged) levels, butfar fewer can read a spectrogram or a correlation meter, or even know what these tools are.

To help you understand computer-based metering and analysis tools, I'll discuss what they are,when you would use them, and how to make sense of what they say. I can only scratch thesurface here, but hopefully that will be enough to embolden you to try out and learn more aboutsome analysis tools. I'll look primarily at affordable software tools for signal analysis, rather thanat tools for system or component measurement, although the line between the two types can befuzzy. Hardware analyzers and high-end, specialized software such as acoustical-analysisprograms have been excluded.

A Few DistinctionsMathematically speaking, an audio phenomenoncan be seen as a function of time or of 

frequency, and there are ways to convertbetween these two domains. Most audio-analysistools exploit this concept to let you view thesame data in multiple ways, each of which yieldsits own insights. For our purposes, we can breakthe available audio-analysis tools into thefollowing categories: level, spectral analysis,phase- and stereo- image meters, transfer functions, and code tools and statistics.

 Analyzers are often distinguished by the ways inwhich they handle time. Analysis tools fall intoone of two presentation approaches; I will refer to the first as “now,” and the second as “then.”“Now” tools are real-time, and they display ananalysis of a signal as it happens. “Then” toolsshow analysis over time, which provides ahistorical context for spotting trends or episodesin the audio (see Fig. 1).

Since histories and averaged values represent analysis over time, some time must elapse beforea history or an average can be created. In real-time applications, such as live sound, the latency

incurred by these processes is annoying at best, and unacceptable at worst. However, in manycases, such as RMS measurement, the time over which the signal is averaged can be shortenough to be almost realtime.

 Another distinction occurs between those analysis tools that are available as plug-ins and those

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that run standalone. Plug-ins are usable within DAW and audio-editor hosts, which makes themwell suited for production tasks such as monitoring signals during recording, overdubbing, or mixing. Standalone programs can be used to observe a live signal — sometimes even multiplesignals. But here the basic paradigm is different: only a single instance of an analyzer program isexpected to be running, and you generally use the program as a bench-test instrument rather than as a production-monitoring tool.

 Analysis tools provide broad capabilities, but people's needs for them are often specific. Toaccommodate such needs, most analysis tools are highly configurable, offering graphicpreferences, variable parameters for processes such as Fast Fourier Transforms (FFTs), andparameters for defining quantities (such as the number of clipped samples that constitute a “clip”condition). The more comfortable you become with these tools, the more you will probablycustomize.

PFFT! A good place to start working with analysis tools is by looking at the most important analysistechnique for audio, which underlies a number of tools: the FFT. FFTs convert data from the timedomain into the frequency domain. (An inverse FFT converts in the other direction.) FFTs allowyou to look at the spectrum of a signal.

The SpectraFoo manual says, “The FFT algorithm is an efficient means of computing a Fourier transform on a computer. The Fourier transform, developed between 1804 and 1807 by themathematician Joseph Fourier as part of a study of heat transfer, converts a continuous recordof amplitude versus time into a record of amplitude versus frequency. A modification of theFourier Transform called the Discrete Fourier Transform (DFT) was developed to deal withsampled, rather than continuous, waveforms. The FFT algorithm was developed as an efficientway of computing the DFT on digital computers.”

The FFT is the basis for everything from spectrum analyzers to transfer functions. The two keyparameters that determine FFT performance are block size (the number of samples on which theFFT is performed) and window type (a preconditioning function applied to reduce error in theFFT). For more on FFTs and the impact of these parameters, see the online bonus material atwww.emusician.com.

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