Compression - a novel method
paper illustrates a novel method of speech compression and transmission. This
method saves the transmission bandwidth required for the speech signal by a considerable
amount. This scheme exploits the property of low pass nature of the speech signal.
Also this method applies equally well for any signal, which is low pass in nature,
speech being the more widely used in Real Time Communication, is highlighted here.
As per this method, the low pass signal (speech)
at the transmitter is divided into set of packets, each containing, say N number
of samples. Of the N samples per packet, only certain lesser number of samples,
say N alone are transmitted. Here is less than unity, so compression is achieved.
The N samples per packet are subjected to a N-Point DFT. Since low pass signals
alone are considered here, the number of significant values in the set of DFT
samples is very limited. Transmitting these significant samples alone would suffice
for reliable transmission. The number of samples, which are transmitted, is determined
by the parameter .
The parameter is almost
independent of the source of the speech signal. In other methods of speech compression,
the specific characteristics of the source such as pitch are important for the
algorithm to work. An exact reverse process at the
receiver reconstructs the samples. At the receiver, the N-point IDFT of the received
signal is performed after necessary zero padding. Zero padding is necessary because
at the transmitter of the N samples only N samples are transmitted, but at the
receiver N samples are again needed to honestly reconstruct the signal.
this method is efficient as only a portion of the total number of samples is transmitted
thereby saving the bandwidth. Since the frequency samples are transmitted the
phase information has also to be transmitted. Here again by exploiting the property
of signals and their spectra that the PHASE INFORMATION CAN BE EMBEDDED WITHIN
THE MAGNITUDE SPECTRUM by using simple mathematics without any heavy computations
or by increasing the bandwidth.
Also the simulation
result of this method shows that smaller the size of the packet the more faithful
is the reproduction of received signal that is again an advantage as the computation
time is reduced. The reduction in the computation time is due to the fact that
the transmitter has to wait until N samples are obtained before starting the transmission.
If N is small, the transmitter has to wait for a less duration of time and a smaller
value of N achieves a better reconstruction at the receiver.
this scheme provides a more efficient method of speech compression and this scheme
is also very easy to implement with the help of available high-speed processors.Transmitting
the spectrum of the signal instead of transmitting the original signal is far
more efficient. This is because the energy of the speech signal above 4 kHz is
negligible; we can very well compute the spectrum of the signal and transmit only
the samples that correspond to 4 KHz of the spectrum irrespective of the sampling
frequency. By this type of transmission we can save the bandwidth required for
transmission considerably. Also it is not necessary that we have to transmit all
the samples corresponding to the 4 kHz frequency as it is sufficient to transmit
a fraction of the samples without any degradation in the quality.
the spectrum is considered in the above method both the magnitude and phase information
must be transmitted to reproduce the signal without any error. But this requires
twice the actual bandwidth. Exploiting the property of real and even signals can
solve this problem. The spectrum of the samples is real and evenliness is artificially
introduced such that their spectra are also real and even. Thus by simple mathematics
the complete phase information is embedded within the magnitude spectrum and it
is needed only to send 'aN' samples instead of '2N'samples of the spectra (Magnitude
Adopting all these procedures
and embedding the phase information in the magnitude spectrum have performed a
MATLAB simulation performed to determine the optimum value of 'a' and 'N'. The
result of the simulation is also provided.
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