V-SELP Vector Sum Excited Linear Prediction

V-SELP: Vector Sum Excited Linear Prediction

V-SELP (Vector Sum Excited Linear Prediction) is a speech coding algorithm used in digital speech communication systems and voice over IP (VoIP) applications. It is an enhancement of the conventional Code-Excited Linear Prediction (CELP) algorithm, which is widely used for speech compression in telecommunications.

Background:

Speech coding, also known as speech compression or voice coding, involves converting an analog audio signal (speech) into a digital format with reduced data size while maintaining acceptable speech quality. The primary objective of speech coding is to minimize the bit rate required for transmission or storage without significant degradation in perceived speech quality.

Linear Prediction in Speech Coding:

Linear Prediction (LP) is a fundamental technique used in speech coding. It models the speech signal as a linear combination of past samples and predicts the current sample based on the previous samples. The LP coefficients are used to synthesize the speech signal at the receiver.

Code-Excited Linear Prediction (CELP):

CELP is a widely used speech coding technique that exploits the correlation between speech samples to achieve high compression ratios with good speech quality. In CELP, an algebraic codebook and a perceptual weighting filter are used to generate the synthesized speech signal at the receiver. The algebraic codebook contains pre-recorded speech segments, and the encoder searches for the best match from the codebook to quantize the excitation signal. CELP offers good speech quality at low to medium bit rates.

Vector Sum Excited Linear Prediction (V-SELP):

V-SELP is an extension of CELP, aiming to further improve the quality of synthesized speech at low bit rates. It was developed by NEC Corporation and is used in some digital cellular communication systems.

Key Features of V-SELP:

1. Codebook Structure: In V-SELP, the algebraic codebook is structured differently compared to conventional CELP. It employs a vector sum of multiple excitation vectors instead of using a single excitation vector from the codebook. This enables better modeling of the spectral characteristics of the speech signal.
2. Vector Sum Search: The V-SELP encoder performs a vector sum search to find the combination of excitation vectors that best matches the input speech segment. The vector sum search improves the accuracy of the excitation signal, leading to higher speech quality.
3. Quality at Low Bit Rates: V-SELP is particularly designed to provide good speech quality at low bit rates, making it suitable for speech communication in bandwidth-constrained environments.
4. Complexity: V-SELP has a higher computational complexity compared to conventional CELP due to the vector sum search and additional processing involved.

Advantages and Limitations:

Advantages:

1. Improved Speech Quality: V-SELP's vector sum search leads to better excitation signal representation, resulting in enhanced speech quality at low bit rates.
2. Bandwidth Efficiency: V-SELP is suitable for speech communication over low-bandwidth channels or networks.

Limitations:

1. Complexity: The higher computational complexity of V-SELP compared to CELP may be a limitation in resource-constrained devices.
2. Proprietary: V-SELP is a proprietary speech coding algorithm developed by NEC Corporation, which may limit its adoption in standardization.

Conclusion:

V-SELP (Vector Sum Excited Linear Prediction) is a speech coding algorithm that builds upon the principles of CELP (Code-Excited Linear Prediction) to achieve improved speech quality at low bit rates. By using a vector sum search in the codebook and modeling the excitation signal more accurately, V-SELP is particularly suitable for speech communication over low-bandwidth channels or networks. However, its higher computational complexity and proprietary nature are aspects to consider when evaluating its use in practical applications.