Telecommunication

What is V-SELP Vector Sum Excited Linear Prediction

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V-SELP (Vector Sum Excited Linear Prediction) is a speech coding technique used in various cellular communication standards. It belongs to the class of speech coders known as CELP (Code Excited Linear Prediction). Here's a detailed technical explanation of V-SELP:

Core Principles:

  • Analysis-by-Synthesis: V-SELP employs an analysis-by-synthesis approach. It analyzes the input speech signal to extract key features and then synthesizes a new speech signal based on those features.
  • Linear Prediction (LP) Analysis: V-SELP utilizes Linear Prediction (LP) to model the vocal tract characteristics of the speaker. The LP analysis estimates the filter that represents the vocal tract and removes its influence from the speech signal.
  • Excitation Signal: The remaining signal, which contains the pitch and vocal cord information, is called the residual signal. V-SELP uses a codebook of pre-defined excitation signals to find the one that best matches the characteristics of the residual signal.

V-SELP Components:

  • Long Term Predictor (LTP): Models the pitch information in the speech signal by exploiting the repetitiveness of the vocal cord vibration. The LTP predicts a portion of the current speech sample based on a previous sample that is a multiple of the pitch period.
  • VSELP Excitation Codebook: This codebook stores a collection of pre-designed vector pulses that represent different excitation patterns. These patterns can capture the characteristics of voiced and unvoiced speech segments.
  • Gain Quantizer: Quantizes (reduces the number of bits) the gain factor associated with the chosen excitation vector. The gain factor scales the amplitude of the excitation signal to match the energy of the residual signal.
  • Synthesis Filter: This filter, modeled using Linear Prediction coefficients, post-processes the selected and scaled excitation signal to reconstruct the synthesized speech.

V-SELP Advantages:

  • High Speech Quality: V-SELP offers high speech quality at relatively low bit rates (around 8 kbps) compared to other coding techniques.
  • Computational Efficiency: The structured codebook search in V-SELP allows for efficient implementation, making it suitable for real-time applications on battery-powered devices.
  • Robustness to Channel Errors: V-SELP exhibits some level of robustness to channel errors due to the use of vector quantization for the excitation signal.

V-SELP Disadvantages:

  • Increased Complexity: Compared to simpler speech coding techniques, V-SELP has a more complex design due to the codebook search and gain quantization processes.
  • Sensitivity to Pitch Tracking Errors: The performance of V-SELP can be impacted by inaccuracies in estimating the pitch information.

Overall, V-SELP is a powerful speech coding technique that offers a good balance between speech quality, bit rate, and computational complexity. It has played a significant role in enabling efficient voice communication in cellular networks.

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