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Clipping or Too Much Power?

This is not rocket science.

The following test is quite easy, and even without doing the test, there is a relatively simple mathematical solution.

For the test, all that is needed is an amplifier, dummy load, signal source, oscilloscope and a real time spectrum analyzer. Most of the software programs will measure distortion in real time and include a oscilloscope function that will operate concurrently with the spectrum analyzer.

What happens to the distribution of the spectral energy in a waveform (simple or complex) , clipped or otherwise is quite easy to see and measure.

How many with a strong opinion one way or the other have actually done this.

I see a lot of I think, I believe, I heard or I was told when it comes to a persons opinion about this.

Hard science has a hard time on AK.

For those of you that have actually done this and have posted an opinion, please post your test methodology and the results.

Please feel free to include the necessary math.

I am not asking because I do not know the answer, I do. I am asking to separate the wheat from the chaff, in terms of those that have posted an opinion.

sqlsavior recommended an internet search for "square wave Fourier transform".

Below are pictures from the internet base on his recommended search terms.

For those of you with an opinion, are these correct representations or not and explain why. Again feel free to include the math needed to substantiate your opinion.

As mentioned earlier in this thread, some amplifiers misbehave in a rather large manner when driven into clipping. The results from that are a separate subset of the results of amplifier clipping in this context, that is the spectral distribution of energy contained in a square wave.

For a square wave with a 50 percent duty cycle, the peak amplitude of any harmonic can be calculated as 2*E/(pi*n), is this correct or not. Please explain why.

When we talk about the power in each harmonic, does it matter that the above formula gives the peak value of the sine waves that make up the harmonic content of the square wave?

What about this, 2*E/(pi*n*√2)? Is this correct? Please explain why or why not.

Does any of this even apply to this topic? Another term is compression.

Does compression in the context of this discussion have any bearing, why or why not and please explain.

Let the harmonics fly. :D

I am really looking forward to reading the responses to my questions. :D

Ken, I see that you have started another thread on this topic.
 

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So, what the papers seem to me to be saying is that clipping does kill tweeters, but that the REASON that disproportionate energy is sent to the tweeter is more due to compression than harmonics, thus "blabbing" about Fourier is more of an obfuscation than an explanation.

Fine; I'll buy that - jluvda and I were wrong, to an extent. Learn something new every day!

Still, I think presenting a false dichotomy of "too much power" vs. "clipping" as either-or causes and absolving clipping from blame is a greater obfuscation.

Eschew obfuscation! Espouse elucidation!
 
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So, what the papers seem to me to be saying is that clipping does kill tweeters, but that the REASON that disproportionate energy is sent to the tweeter is more due to compression than harmonics, thus "blabbing" about Fourier is more of an obfuscation than an explanation.

Fine; I'll buy that - jluvda and I were wrong, to an extent. Learn something new every day!

Still, I think presenting a false dichotomy of "too much power" vs. "clipping" as either-or causes and absolving clipping from blame is a greater obfuscation.

Eschew obfuscation! Espouse elucidation!

You still don't get it.

Clipping has nothing to do with it. If you were to replace the small amp with a larger one, while leaving the volume control the same, the clipping would go away, but the tweeters would still blow.

"Compression" simply explains why it is the tweeters, not the woofers, that blow. Compression protects the woofers, but not the tweeters.

You are stubborn and sarcastic... both enviable traits. Until you are wrong. Which you are.

-k
 
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