Wideband speech signals of 2 males and 2 females were coded using an improved version of Linear Predictive Coding (LPC). The sampling frequency was at 16 kHz and the bit rate was at 15450 bits per second, where the original bit rate was at 128000 bits per second. The tradeoffs between the bit rate, end-to-end delay, speech quality and complexity were analyzed. Simulations as well as Segmental SNR evaluations were done to analyze the performance of the implemented algorithm.
Speech coding has been and still is a major issue in the area of digital speech processing. Speech coding is the act of transforming the speech signal at hand, to a more compact form, which can then be transmitted with a considerably smaller memory. The motivation behind this is the fact that access to unlimited amount of bandwidth is not possible. Therefore, there is a need to code and compress speech signals. Speech compression is required in long-distance communication, high-quality speech storage, and message encryption.
For example, in digital cellular technology many users need to share the same frequency bandwidth. Utilizing speech compression makes it possible for more users to share the available system. Another example where speech compression is needed is in digital voice storage. For a fixed amount of available memory, compression makes it possible to store longer messages.
Speech coding is a lossy type of coding, which means that the output signal does not exactly sound like the input. The input and the output signal could be distinguished to be different. Coding of audio however, is a different kind of problem than speech coding. Audio coding tries to code the audio in a perceptually lossless way. This means that even though the input and output signals are not mathematically equivalent, the sound at the output is the same as the input. This type of coding is used in applications for audio storage, broadcasting, and Internet streaming .
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