The EM Poll

Analyze This

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

           

BONUS MATERIAL
Learn more about FFTs (Fast Fourier Transforms) and their impact on data parameters

We rightly rely on our ears as the final arbiters of recording and mixing issues. Sometimes, however, identifying the cause of an audible production problem by ear can be difficult. Metering and analysis tools provide quantitative information that allows us to observe an audio signal from various perspectives, which can be the key to avoiding problems and identifying pesky artifacts.

Today, analysis tools have emerged from research laboratories to become affordable and readily available in plug-in suites such as BIAS Reveal (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, iZotope Ozone, and Sony Creative Software Sound Forge. 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 users understand level meters and know the difference between peak and RMS (averaged) levels, but far 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 the surface here, but hopefully that will be enough to embolden you to try out and learn more about some analysis tools. I'll look primarily at affordable software tools for signal analysis, rather than at tools for system or component measurement, although the line between the two types can be fuzzy. Hardware analyzers and high-end, specialized software such as acoustical-analysis programs have been excluded.

A Few Distinctions

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

Mathematically speaking, an audio phenomenon can be seen as a function of time or of frequency, and there are ways to convert between these two domains. Most audio-analysis tools exploit this concept to let you view the same data in multiple ways, each of which yields its own insights. For our purposes, we can break the available audio-analysis tools into the following categories: level, spectral analysis, phase- and stereo-image meters, transfer functions, and code tools and statistics.

Analyzers are often distinguished by the ways in which they handle time. Analysis tools fall into one 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 an analysis of a signal as it happens. “Then” tools show analysis over time, which provides a historical context for spotting trends or episodes in the audio (see Fig. 1).

Since histories and averaged values represent analysis over time, some time must elapse before a 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 many cases, such as RMS measurement, the time over which the signal is averaged can be short enough to be almost realtime.

Another distinction occurs between those analysis tools that are available as plug-ins and those that run standalone. Plug-ins are usable within DAW and audio-editor hosts, which makes them well 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 multiple signals. But here the basic paradigm is different: only a single instance of an analyzer program is expected 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. To accommodate such needs, most analysis tools are highly configurable, offering graphic preferences, variable parameters for processes such as Fast Fourier Transforms (FFTs), and parameters 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 probably customize.

PFFT!

A good place to start working with analysis tools is by looking at the most important analysis technique for audio, which underlies a number of tools: the FFT. FFTs convert data from the time domain into the frequency domain. (An inverse FFT converts in the other direction.) FFTs allow you 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 the mathematician Joseph Fourier as part of a study of heat transfer, converts a continuous record of amplitude versus time into a record of amplitude versus frequency. A modification of the Fourier Transform called the Discrete Fourier Transform (DFT) was developed to deal with sampled, rather than continuous, waveforms. The FFT algorithm was developed as an efficient way of computing the DFT on digital computers.”

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