Low bit rate speech coding methods and a new interframe differential coding scheme for line spectrum pairs

Ankara : Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 1992.Thesis (Master's) -- Bilkent University, 1992.Includes bibliographical references leaves 30-32.Low bit rate speech coding techniques and a new coding scheme for vocal tract parameters are presented. Linear prediction based voice coding techniques (linear predictive coding and code excited linear predictive coding) are examined and implemented. A new interframe differential coding scheme for line spectrum pairs is developed. The new scheme reduces the spectral distortion of the linear predictive filter while maintaining a high compression ratio.Erzin, EnginM.S

Energy Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains.

This invention relates to representation of one and multidimensional signal vectors in nonorgothonal domains and design of Vector Quantizers that can be chosen among these representations. There is presented a Vector Quantization technique in multiple nonorthogonal domains for both waveform and model based signal characterization. An iterative codebook accuracy enhancement algorithm, applicable to both waveform and model based Vector Quantization in multiple nonorthogonal domains, which yields further improvement in signal coding performance, is disclosed. Further, Vector Quantization in in nonorthogonal domains is applied to speech and exhibits clear performance improvements of reconstruction quality for the same bit rate compared to existing single domain Vector Quantization techniques. The technique disclosed herein can be easily extended to several other one and multidimensional signal classes

Interframe differential coding of line spectrum frequencies

Cataloged from PDF version of article.Line spectrum frequencies (LSF's) uniquely represent the linear predictive coding (LPC) filter of a speech frame. In many vocoders LSF's are used to encode the LPC parameters. In this paper, an inter-frame differential coding scheme is presented for the LSF's. The LSF's of the current speech frame are predicted by using both the LSF's of the previous frame and some of the LSF's of the current frame. Then, the difference resulting from prediction is quantized

Gaussian Mixture Model-based Quantization of Line Spectral Frequencies for Adaptive Multirate Speech Codec

In this paper, we investigate the use of a Gaussian MixtureModel (GMM)-based quantizer for quantization of the Line Spectral Frequencies (LSFs) in the Adaptive Multi-Rate (AMR) speech codec. We estimate the parametric GMM model of the probability density function (pdf) for the prediction error (residual) of mean-removed LSF parameters that are used in the AMR codec for speech spectral envelope representation. The studied GMM-based quantizer is based on transform coding using Karhunen-Loeve transform (KLT) and transform domain scalar quantizers (SQ) individually designed for each Gaussian mixture. We have investigated the applicability of such a quantization scheme in the existing AMR codec by solely replacing the AMR LSF quantization algorithm segment. The main novelty in this paper lies in applying and adapting the entropy constrained (EC) coding for fixed-rate scalar quantization of transformed residuals thereby allowing for better adaptation to the local statistics of the source. We study and evaluate the compression efficiency, computational complexity and memory requirements of the proposed algorithm. Experimental results show that the GMM-based EC quantizer provides better rate/distortion performance than the quantization schemes used in the referent AMR codec by saving up to 7.32 bits/frame at much lower rate-independent computational complexity and memory requirements

DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508

Masking of errors in transmission of VAPC-coded speech

A subjective evaluation is provided of the bit error sensitivity of the message elements of a Vector Adaptive Predictive (VAPC) speech coder, along with an indication of the amenability of these elements to a popular error masking strategy (cross frame hold over). As expected, a wide range of bit error sensitivity was observed. The most sensitive message components were the short term spectral information and the most significant bits of the pitch and gain indices. The cross frame hold over strategy was found to be useful for pitch and gain information, but it was not beneficial for the spectral information unless severe corruption had occurred

A robust low bit rate quad-band excitation LSP vocoder.

by Chiu Kim Ming.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 103-108).Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Speech production --- p.2Chapter 1.2 --- Low bit rate speech coding --- p.4Chapter Chapter 2 --- Speech analysis & synthesis --- p.8Chapter 2.1 --- Linear prediction of speech signal --- p.8Chapter 2.2 --- LPC vocoder --- p.11Chapter 2.2.1 --- Pitch and voiced/unvoiced decision --- p.11Chapter 2.2.2 --- Spectral envelope representation --- p.15Chapter 2.3 --- Excitation --- p.16Chapter 2.3.1 --- Regular pulse excitation and Multipulse excitation --- p.16Chapter 2.3.2 --- Coded excitation and vector sum excitation --- p.19Chapter 2.4 --- Multiband excitation --- p.22Chapter 2.5 --- Multiband excitation vocoder --- p.25Chapter Chapter 3 --- Dual-band and Quad-band excitation --- p.31Chapter 3.1 --- Dual-band excitation --- p.31Chapter 3.2 --- Quad-band excitation --- p.37Chapter 3.3 --- Parameters determination --- p.41Chapter 3.3.1 --- Pitch detection --- p.41Chapter 3.3.2 --- Voiced/unvoiced pattern generation --- p.43Chapter 3.4 --- Excitation generation --- p.47Chapter Chapter 4 --- A low bit rate Quad-Band Excitation LSP Vocoder --- p.51Chapter 4.1 --- Architecture of QBELSP vocoder --- p.51Chapter 4.2 --- Coding of excitation parameters --- p.58Chapter 4.2.1 --- Coding of pitch value --- p.58Chapter 4.2.2 --- Coding of voiced/unvoiced pattern --- p.60Chapter 4.3 --- Spectral envelope estimation and coding --- p.62Chapter 4.3.1 --- Spectral envelope & the gain value --- p.62Chapter 4.3.2 --- Line Spectral Pairs (LSP) --- p.63Chapter 4.3.3 --- Coding of LSP frequencies --- p.68Chapter 4.3.4 --- Coding of gain value --- p.77Chapter Chapter 5 --- Performance evaluation --- p.80Chapter 5.1 --- Spectral analysis --- p.80Chapter 5.2 --- Subjective listening test --- p.93Chapter 5.2.1 --- Mean Opinion Score (MOS) --- p.93Chapter 5.2.2 --- Diagnostic Rhyme Test (DRT) --- p.96Chapter Chapter 6 --- Conclusions and discussions --- p.99References --- p.103Appendix A Subroutine of pitch detection --- p.A-I - A-IIIAppendix B Subroutine of voiced/unvoiced decision --- p.B-I - B-VAppendix C Subroutine of LPC coefficients calculation using Durbin's recursive method --- p.C-I - C-IIAppendix D Subroutine of LSP calculation using Chebyshev Polynomials --- p.D-I - D-IIIAppendix E Single syllable word pairs for Diagnostic Rhyme Test --- p.E-

Adaptive Variable Degree-k Zero-Trees for Re-Encoding of Perceptually Quantized Wavelet-Packet Transformed Audio and High Quality Speech

A fast, efficient and scalable algorithm is proposed, in this paper, for re-encoding of perceptually quantized wavelet-packet transform (WPT) coefficients of audio and high quality speech and is called "adaptive variable degree-k zero-trees" (AVDZ). The quantization process is carried out by taking into account some basic perceptual considerations, and achieves good subjective quality with low complexity. The performance of the proposed AVDZ algorithm is compared with two other zero-tree-based schemes comprising: 1- Embedded Zero-tree Wavelet (EZW) and 2- The set partitioning in hierarchical trees (SPIHT). Since EZW and SPIHT are designed for image compression, some modifications are incorporated in these schemes for their better matching to audio signals. It is shown that the proposed modifications can improve their performance by about 15-25%. Furthermore, it is concluded that the proposed AVDZ algorithm outperforms these modified versions in terms of both output average bit-rates and computation times.Comment: 30 pages (Double space), 15 figures, 5 tables, ISRN Signal Processing (in Press