Hello Matei,
Look at codec2-dec/src/codec2.c
c2sim can dump the parameters to text files
- David
On 08/02/18 04:29, Matei Alexandru Coltoiu wrote:
So, I would like to know how are the data vectors that come out of the
encoder composed, by that I mean, how are the 4 parameters, pitch,
energy, voicing and spectrum organised in the bitstream. I would like to
extract the parameters in order to work on them.
On Fri, Nov 3, 2017 at 12:22 PM, Matei Alexandru Coltoiu
<coltoiu.ma...@gmail.com <mailto:coltoiu.ma...@gmail.com>> wrote:
David, the current block scheme of soft-decision demodulators (as
far as I konow) includes a demodulator that still outputs integer
values, but it outputs current quantisation tresholds and accepts a
feedback for the decision making process. My theoretical demodulator
would be a non-decision demodulator which outputs raw data. It is
true that you would have to have much more memory and processing
power available in order to process such large data quantities but
today we have such posibilities.
It is quite intersting that already in 1993 an article was
published, Dynamic Adaptation of Quantisation Thresholds for
Soft-Decision Viterbi Decoding with a Reinforcement Learning Neural
Network. It uses a soft demodulator wich uses feedback from a neural
network. That's a concept 25 Years old, when neural networks were
more like a concept with little practical implementations. Today we
already have such networks available.
So there are 3 steps we could take: Develop a non decision making
demodulator, feeding raw data into a neural network, feeding
processed raw data intro the modified decoder, made able to process
floating point binary data.
Tomas, the encoding side is a different story. The H 2.64 loop
filter prevents some encoding artefacts from becoming to obvious.
Now, first we would have to consider wich artefacts we would like to
smoothen, encoding artafacts or specific possible BER artefacts.
Then, we would have objective and subjective artefacts, that means
you would have artefacts affecting the objective form of the signal,
but we do not know unless we test with a group of subjects how muh
these artefacts affect the subjective perception of the speech.
The only simple approach for an objective filtering, for both
encoding and BER artefacts I see here is a filter which is modelled
by a neural network. You take the filter parameters and model them
with the help of a neural network which compares original and
encoded signal.
However, I would leave this for another topic and I would
concentrate on the receiver part for now.
--
Colțoiu Matei Alexandru, telefon: 0373741128 <tel:0373%20741%20128>
whatsapp 0748021325 <tel:0748%20021%20325>/0770663003
<tel:0770%20663%20003>. Skype: rav0rmat
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