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 Speech Compression - a novel method
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This 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.

Hence 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.

Thus 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.

Since 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 and phase).

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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