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Analyzer Mythbusting: Clearing Up Common Points Of Confusion With Dual-Channel FFT Analyzers

Clarifying “FFT vs RTA vs transfer function” along with “frequency response vs magnitude response” and several other areas sometimes misunderstood.

Sample rate is measured in samples/second (or Hz), and the size of data chunks used for each FFT operation is measured in samples, but these are two different concepts.

As with any digital device, the highest frequency that the analyzer can resolve is dictated by the sampling rate of your audio interface. In broad terms, HF cutoff (known as the Nyquist frequency) will be about half the sampling rate.

This concept should not be confused with the concept of FFT size, measured in samples. FFT size describes how many samples of data are used when calculating each frame of the measurement. Here’s the rule: In order to accurately describe a frequency via FFT, the data window must be open long enough for that frequency to complete a full cycle.

That’s it! It’s because the transform works something like this: For a 10 millisecond (ms) time record, the transform says “Hey, input data. Who went through one cycle in this chunk of time? You! 100 Hz! What’s your magnitude? What’s your phase? Cool. OK, next. Hey, who went through two cycles in this chunk of time? You! 200 Hz! What’s your magnitude? What’s your phase?” And so on. Data points (“bins”) fall at frequencies that make a full number of cycles within the time record.

A transfer function is commonly made by subtracting an electronic reference signal from an acoustic measurement signal. The measurement compares two signals and shows the relationship between them. We use the terms Measurement signal and Reference signal, but both signals can be pretty much anything we want. By the time our signals get into the analyzer, they’ve been converted to digital values, even if they were acoustic signals (sound waves) in a former life.

If you wanted to check that your two measurement microphones had matching responses, for example, you could place them nose to nose and take a transfer function measurement between them. This provides a comparative measurement between two mic output signals. Or maybe you want to measure the response created by an equalizer or system processor by taking a transfer function measurement between the input and the output of the device.

Once you realize that the analyzer will try to compare any two signals we feed it, you can see how a transfer function measurement can be used for a lot of applications besides measuring the response of a loudspeaker in a room.

In a common configuration, we configure the analyzer’s signal generator to output signal to two physical outputs on the audio interface. One is routed through the system we wish to measure, and the other is “looped back” to an input on the interface. In this way, we eliminate from the measurement any effects of interface latency.

The remaining time offset is a result of signal propagation through the system under test, not the latency of the computer-audio interface connection. In fact, that’s one of the reasons it’s not usually recommended to directly reference the internal signal generator – it keeps driver latency and interface latency in the picture, which can give some unexpected results.

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The coherence trace (not to be confused with the concept of phase coherence) functions more or less as a data quality indicator, serving to indicate the level of correlation between the measurement and reference signals. In other words, “How confident can we be that the energy showing up at the mic was caused by our reference signal?”

In order to avoid making decisions based on low-coherence data (the 60 Hz rumble caused by the HVAC or the subway under the theater should not be treated with EQ!), Smaart’s coherence blanking threshold hides data whose coherence values fall below the threshold. This is a display-only feature and has no effect on the underlying measurement data. It only controls whether or not those data points are visible on screen.

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