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Author Topic: Microphone Myth Buster #1: Frequency Response Graphs Tell You Something  (Read 17753 times)

klaus

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Thanks for your thoughtful reply, Les.

What still remains a mystery to me is what would be desirable to see in an ideal frequency graph.

Let's assume for a moment that slanted, self-aggrandizing distortions in the measuring process are eliminated, and the mic is measured by an independent party which pays close attention to any of the many treacherous variables that can falsify the outcome of the process. Let's also assume minimum smoothing of the curves is done as the signal (sine wave, most likely) is swept across the spectrum.

What shape of the curve would best represent what we like to hear in a good mic? A ruler flat response? Boosts or cuts at certain sections of the frequency band? A prominent peak or trough somewhere? And, if we want to include a polar pattern of the mic, do we really know that off-axis uniformity is a desirable feature of a good mic- not only visually ideal but acoustical as well? Has "uniform off-axis response"  been correlated and shown to be indicative of other desirable measurement features in a mic?

The mind would lead us to conclude that "flat" and "uniform off-axis response" is best. But that assumption may not take into account  that a microphone does not behave like ears and brain. (You showed with your pressure/pressure gradient experiment, that this may not be the case. I could cite numerous other examples of how the ear processes sound unlike a microphone, and how a good microphone's processing is at best a euphemistic approximation of our hearing.)

What I keep coming back to is this:
So far, visual representation in form of plots or specifications has failed to be a useful tool in predicting a microphone's sound and quality.

If a recording mic were to be only used as a faithful reproducer of sine waves fed into a loudspeaker, we would have less of a problem with the truthful visual representation of that event. But our goal is much more ambitious: we would like to visually validate what we hear out of the actual mic, so that that visual representation of sound can be a useful tool for the design, selection and marketing of microphones, which it still massively fails to do, all utterances of pocket protector types to the contrary.

I have my doubts that single event (sine wave) plotting and tracing will ever give us enough information about a microphone's ability to process complex waveforms in the time domain. Our ears are still best suited to predict a mic's usefulness as a tool to capture music.

The low priority for listening education in our business, and the resulting fear of many engineers and musicians to trust their ears to make esthetic decisions continues to fascinate me- topic for another subject.)



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Klaus Heyne
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klaus

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The impotence of comparing frequency plots of mics to shape sounds becomes apparent, latest when you considering pair matching.

I just finished modifying/upgrading a pair of new U87Ai mics for a prominent classical pianist. The mics sounded a bit different in timbre from one another- not much, but enough so, that I was not able to eliminate the deviation and optimise the sound by tweaking low or high end response in the amps. It was clearly the capsule character imprint that made the difference: one was a bit forward in the mids, but slightly attenuated in the highs. The other was silky throughout, with a little bit of a 'blah' factor in the intelligibility range. Not really missing anything there, but just not lively enough for my taste. 

Now, explain anyone to me, how what was desirable and what was not in these two mics could be detected by looking at a frequency chart, and what one could do about it to correct the shortcomings of both mics/capsules with the help of the chart? While the phenomena were clearly audible, a frequency chart would not be able to correlate this to what I heard, and offer a path to a solution. 

I ended up splitting the better sounding of the two capsules, so that I would have two front sides with similar timbres, then fine tuned capsules and mic amps a bit, to the point that both mics now sound indistinguishable from each other to my ears.

Facit: The outcome, rather than the starting point, would probably be the more telling and interesting exercise to plot a frequency graph of: how close would these plots now be? Or would they still tell different stories, while our ears judge the mics identical in sound?
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Klaus Heyne
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halocline

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The low priority for listening education in our business, and the resulting fear of many engineers and musicians to trust their ears to make esthetic decisions continues to fascinate me- topic for another subject.)

Ear training, what you call listening education, is typically the most painstaking and difficult skill in music education in general, not just in audio engineering. Developing the skill to distinguish between very fine gradations of sound is not something that can be simply learned through reading or conceptual understanding, it's based on (usually) years of effort, drive to learn, and opportunity to have continual access to source audio material in order to develop the increased familiarity necessary to make increasingly better decisions based on actual sonic perception.

There might be a useful analogy between written language and frequency plots; the information is there, but the expressiveness is typically found in elements not captured by written or visual representation. Maybe it's a dumb analogy...I don't know that much about frequency plots to really judge.
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Kai

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...what ...could be detected by looking at a frequency chart... a frequency chart would not be able to correlate this to what I heard,...
What's your measurement setup and method?
Maybe it's too different to your listening tests, which you once described as:
you do use your own voice as source and a Sennheiser HD 414 without foam as monitor.

When I hear sound differences between same model mic's I usually can track them down to the frequency response taken from 2 different distances with directional mics.

Regards
Kai
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