RPE-LTP Regular Pulse Excited Long Term Prediction

RPE-LTP, or Regular Pulse Excited Long Term Prediction, is a coding technique used in speech compression algorithms. It is a key component of the CELP (Code-Excited Linear Prediction) coding method, which is widely used in telecommunications and speech coding applications.

The goal of speech compression is to reduce the amount of data required to represent speech signals without significant loss in quality. RPE-LTP is specifically designed to efficiently represent the periodic and quasi-periodic components of speech signals, which are essential for maintaining speech intelligibility.

The RPE-LTP algorithm works by exploiting the predictable nature of voiced speech sounds. Voiced sounds are produced when the vocal cords vibrate periodically, creating a harmonic structure. These periodic components can be modeled and predicted, allowing for efficient coding.

The RPE-LTP algorithm operates in the time domain and consists of two main stages: long-term prediction and short-term prediction.

Long-Term Prediction: In this stage, the algorithm predicts the long-term periodicity of the speech signal. It estimates the pitch period, which is the fundamental frequency of the voiced sounds. The pitch period represents the interval between consecutive glottal pulses produced by the vocal cords.

To estimate the pitch period, the RPE-LTP algorithm typically uses an autocorrelation analysis. The autocorrelation function is calculated by correlating the speech signal with itself at different time lags. Peaks in the autocorrelation function indicate the periodicity of the signal, and the highest peak corresponds to the pitch period.

Once the pitch period is estimated, the RPE-LTP algorithm constructs a long-term prediction signal by replicating a portion of the speech signal with the estimated pitch period. This replicated segment is referred to as the excitation signal, and it represents the periodicity of the speech.

Short-Term Prediction: The short-term prediction stage models the residual component of the speech signal that remains after subtracting the long-term prediction. The residual component contains the non-periodic and high-frequency components of the speech.

To model the short-term residual, the RPE-LTP algorithm typically employs a linear prediction model, such as the LPC (Linear Predictive Coding) model. The LPC model estimates the spectral envelope of the speech signal by fitting a linear filter to the speech samples.

Once the spectral envelope is estimated, the RPE-LTP algorithm uses it to predict the short-term residual. The predicted residual is then subtracted from the original speech signal to obtain the quantization error or the difference signal.

The excitation signal (long-term prediction) and the quantization error (short-term prediction) are encoded and transmitted or stored using a suitable bit allocation scheme, which varies depending on the desired compression rate.

During decoding or playback, the inverse process is applied to reconstruct the speech signal. The long-term prediction signal is combined with the short-term residual, and additional post-processing techniques may be employed to enhance the speech quality.

Overall, RPE-LTP is an efficient coding technique that exploits the periodic nature of voiced speech sounds to reduce the data required for representing speech signals. It is widely used in speech coding applications, such as voice communication systems, speech recognition, and multimedia compression algorithms.