""Can you maybe give some hint?""
The most commonly used model for HRTF implementation is the one refered to
as "minimum phase filter and pure delay".It is composed of:
-a minimum-phase filter, which accounts for the magnitude spectrum of HRTF
-and a pure delay , which represents the temporal information contained in
the HRTF
If H(f) is the HRTF to be implemented , the corresponding Hminphase is given
by:
2010/5/27 Friedrich Romstedt <friedrichromst...@gmail.com>
> 2010/5/27 arthur de conihout <arthurdeconih...@gmail.com>:
> > I try to make me clearer on my project :
> > [...]
>
> I think I understood now. Thank you for explanation.
>
> > original = [s / 2.0**15 for s in original]
> >
> > nframes=filtre.getnframes()
> > nchannels=filtre.getnchannels()
> > filtre = struct.unpack_from("%dh" % nframes*nchannels,
> > filtre.readframes(nframes*nchannels))
> > filtre = [s / 2.0**15 for s in filtre]
> >
> > result = numpy.convolve(original, filtre)
>
> Additionally to what David pointed out, it should also suffice to
> normalise only the impulse response channel.
>
> Furthermore, I would spend some thought on what "normalised" actually
> means. And I think it can only be understood in Fourier domain. When
> taking the conservation of energy into account, i.e. the conservation
> of the L2 norm of the impulse response function under Fourier
> transformation, it can be normalised in time domain also by setting
> the time-domain L2 norm to unity. (This is already much different
> from the maximum normalisation.) Then the energy content of the
> signal before and after the processing is identical. I.e., it
> emphasises some frequencies in favour of others which are diminished
> in volume.
>
> A better approach might be in my opinion to use the (already directly)
> determined transfer function. This can be normalised by the power of
> the input signal, which can e.g. be determined by a reference
> measurement without any obstruction in defined distance, I would say.
>
> > result = [ sample * 2.0**15 for sample in result ]
> > filtered.writeframes(struct.pack('%dh' % len(result), *result))
>
> It's to me too a mystery why you observe this octave jump you reported
> on. I guess it's an octave, because you can compensate by doubling
> the sampling rate. Can you check whether your playback program loads
> the output file as single or double channel? Also I guess some
> relation between your observation of "incomplete convolution" and this
> pitch change.
>
> And now, I'm very irritated by the way you handle multiple channels in
> the input file. Actually you load maybe a two-channel input file,
> extract *all* the data, i.e. data from ch1, ch2, ch1, ch2, ..., and
> then convolve this? For single-channel input, this is correct, but
> when your input data is two-channel, it explains on the one hand why
> your program maybe doesn't work properly and on the other why you have
> pitch-halfening (you double the length of each frame). It is I think
> possible that you didn't notice more strange phenomenon, since the
> impulse response function is applied to each datum individually.
>
> > i had a look to what you sent me i m on my way understanding maybe your
> > initialisation tests will allow me to make difference between every wav
> > formats?
>
> Exactly! It should be able to decode at least most wavs, with
> different samp widhts and different channel number. But I think I
> will myself in future hold to David's advice ...
>
> > i want to be able to encode every formats (16bit unsigned, 32bits)what
> > precautions do i have to respect in the filtering?do filter and original
> > must be the same or?
> > Thank you
>
> All problems would go away when you use the transfer function
> directly. It should be present as some function, which can easily be
> interpolated to the frequency points your FFT of the input signal
> yields. Interpolating the time-domain impulse response is not a good
> idea, since it assumes already frequency-boundedness, which is not
> necessarily fulfilled, I guess. Also it's much more complicated.
>
> When you measure transfer function, how can you reconstruct a unique
> impulse response? Do you measure also phases? When you shine in with
> white noise, do autocorrelation of the result and Fourier transform,
> the phases of the transfer function are lost, you only obtain
> amplitudes (squared). Of course it is the same to multiply in Fourier
> space with the transfer function as to convolve with its Fourier-back
> transform, but I'm not yet convinced that this is a good idea to do it
> in time domain tough ... Can you maybe give some hint?
>
> This means you essentially convolve with the autocorrelation function
> itself - but this one is symmetric and therefore not causal. I think
> it's strange when sound starts before it starts in the input ... the
> impulse response must be causal. Anyway, this problem remains also
> when you multiply with the plain real numbers in Fourier domain which
> result from the Fourier transform of the autocorrelation function -
> it's still acausal. The only way to circumvent this I'm seeing
> currently is to measure the phases of the transfer function too - i.e.
> to do a frequency sweep, white noise autocorrelation is then as far as
> I think not sufficient.
>
> Sorry, I feel it didn't came out very clear ...
>
> Friedrich
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
