Comparing two Bose headphones, both of which use active technology.

In this posting I’m going to do something I rarely do, which is, something like a product review. I have purchased the following two headphones within the past few months:

  1. Bose QuietComfort 25 Noise Cancelling
  2. Bose AE2 SoundLink

The first set of headphones has an analog 3.5mm stereo input cable, which has a dual-purpose Mike / Headphone Jack, and comes either compatible with Samsung, or with Apple phones, while the second uses Bluetooth to connect to either brand of phone. I should add that the phone I use with either set of headphones is a Samsung Galaxy S9, which supports Bluetooth 5.

The first set of headphones requires a single, AAA alkaline battery to work properly. And this not only fuels its active noise cancelling, but also an equalizer chip that has become standard with many similar middle-price-range headphones. The second has a built-in rechargeable Lithium-Ion Battery, which is rumoured to be good for 10-15 hours of play-time, which I have not yet tested. Like the first, the second has an equalizer chip, but no active noise cancellation.

I think that right off the bat I should point out, that I don’t approve of this use of an equalizer chip, effectively, to compensate for the sound oddities of the internal voice-coils. I think that more properly, the voice-coils should be designed to deliver the best frequency response possible, by themselves. But the reality in the year 2019 is, that many headphones come with an internal equalizer chip instead.

What I’ve found is that the first set of headphones, while having excellent noise cancellation, has two main drawbacks:

  • The jack into which the analog cable fits, is poorly designed, and can cause bad connections,
  • The single, AAA battery can only deliver a voltage of 1.5V, and if the actual voltage is any lower, either because a Ni-MH battery was used in place of an alkaline cell, or, because the battery is just plain low, the low-voltage equalizer chip will no longer work fully, resulting in sound that reveals the deficiencies in the voice-coil.

The second set of headphones overcomes both these limitations, and I fully expect that its equalizer chips will have uniform behaviour, that my ears will be able to adjust to in the long term, even when I use them for hours or days. Also, I’d tend to say that the way the equalizer arrangement worked in the first set of headphones, was not complete in fulfilling its job, even when the battery was fully charged. Therefore, If I only had the money to buy one of the headphones, I’d choose the second set, which I just received today.

But, having said that, I should also add that I have two 12,000BTU air conditioners running in the Summer months, which really require the noise-cancellation of the first set of headphones, that the second set does not provide.

Also, I have an observation of why the EQ chip in the second set of headphones may work better than the similarly purposed chip in the first set…

(Updated 9/28/2019, 19h05 … )

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The approximate Difference between a DFT and an FFT

Both the Discreet Fourier Transform and the Fast Fourier Transform produce complex-numbered coefficients, the non-zero amplitudes of which will represent frequency components in the signal. They both produce a more accurate measure of this property of the signal, than the Discreet Cosine Transforms do.

Without getting into rigorous Math,

If we have a 1024-sample interval in the time-domain, then the DFT of that simply computes the coefficients from 0 through to 1023, half-cycles. A frequency component present at one coefficient, let us say an even-numbered coefficient, will also have a non-zero effect on the adjacent, odd-numbered coefficients, which can therefore not be separated fully, by a Fourier Transform that defines both sets. A DFT will generally compute them all.

An FFT has as a premise, a specific number of coefficients per octave. That number could be (1), but seldom actually is. In general, an FFT will at first compute (2 * n) coefficients over the full sampling interval, will then fold the interval, and will then compute another (n) coefficients, and will fold the interval again, until the highest-frequency coefficient approaches 1/2 the number of time-domain samples in the last computed interval.

This will cause the higher-octave coefficients to be more spread out and less numerous, but because they are also being computed for successively shorter sampling intervals, they also become less selective, so that all the signal energy is eventually accounted for.

Also, with an FFT, it is usually the coefficients which correspond to the even-numbered ones in the DFT which are computed, again because one frequency component from the signal does not need to be accounted for twice. Thus, whole-numbers of cycles per sampling interval are usually computed.

For example, if we start with a 1024-sample interval in the time-domain, we may decide that we want to achieve (n = 4) coefficients per octave. We therefore compute 8 over the full interval, including (F = 0) but excluding (F = 8). Then we fold the interval down to 512 samples, and compute the coefficients from (F = 4) through (F = 7).

A frequency component that completes the 1024-sample interval 8 times, will complete the 512-sample interval 4 times, so that the second set of coefficients continues where the first left off. And then again, for a twice-folded interval of 256 samples, we compute from (F = 4) through (F = 7)…


 

After we have folded our original sampling interval 6 times, we are left with a 16-sample interval, which forms the end of our series, because (F = 8) would fit in exactly, into 16 samples. And, true to form, we omit the last coefficient, as we did with the DFT.

210  =  1024

10 – 6 = 4

24  =  16


So we would end up with

(1 * 8) + (6 * 4) =  32  Coefficients .

And this type of symmetry seemed relevant in this earlier posting.

Dirk

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