Human hearing beats FFT Options

[ncdrawl]

http://phys.org/news/2013-02-human-fourier...-principle.html

[2Bdecided]

Never mind the title, I still don't find a satisfactory answer in this thread.

I understand that the human ear uses a wobbling membrane as something like a filter bank, with a number of non-linear processes, and an amazing analysis of the signals coming from it, to deliver the hearing capacities that we can probe in listening tests and experience every day. I understand that this is nothing like an FFT. I understand that the frequency resolution of masked noise is not that critical, so we use FFTs in codecs in a place where their frequency resolution is far over-specified, rather than being an issue.

However, we often describe other things in audio and hearing with an FFT-like model. It crops up in sampling theory. We push all the audio through a comparable filterbank in most lossy codecs. It is true that these transforms are mathematically lossless/reversible - but if we're messing with things in the other domain, this is little comfort.

So, simply, what is the reason that this is OK?

Cheers,
David.

[knutinh]

Never mind the title, I still don't find a satisfactory answer in this thread.

I understand that the human ear uses a wobbling membrane as something like a filter bank, with a number of non-linear processes, and an amazing analysis of the signals coming from it, to deliver the hearing capacities that we can probe in listening tests and experience every day. I understand that this is nothing like an FFT. I understand that the frequency resolution of masked noise is not that critical, so we use FFTs in codecs in a place where their frequency resolution is far over-specified, rather than being an issue.

However, we often describe other things in audio and hearing with an FFT-like model. It crops up in sampling theory. We push all the audio through a comparable filterbank in most lossy codecs. It is true that these transforms are mathematically lossless/reversible - but if we're messing with things in the other domain, this is little comfort.

So, simply, what is the reason that this is OK?

Cheers,
David.

I guess the switching between two different time/frequency resolution transforms in many lossy codecs is a sort of "ad hoc" fix for not doing a proper modelling of our hearing aparatus?

Not all audio processing/transmission may need to include an accurate model of our hearing. Perhaps a crude STFT is simply sufficient for some applications.

So what if we deviced an insanely complex, irregular, nonlinear filterbank (Volterra filterbank?). What could it be used for? Better lossy coding? (I think that there are other tradeoffs in lossy coding as well, such as signal compaction). Could we make better "frequency analyzers"? (what engineers would be able to interpret the plots from such a device?).

-k