A subspace based progressive coding method for speech compression

Abstract

In this study, two novel methods, which are based on Karhunen Loeve Transform (KLT) and Independent Component Analysis (ICA), are proposed for coding of speech signals. Instead of immediately dealing with eigenvalue magnitudes, the KLT- and ICA-based methods use eigenvectors of covariance matrices (or independent components for ICA) by geometrically grouping these vectors into fewer numbers of vectors. In this way, a data representation compaction is achieved. Further compression is achieved through discarding autocovariance eigenvectors corresponding to the small eigenvalues and applying vector quantization on the remaining eigenvectors. Additionally, this study proposes an iterative error refinement process, which uses the rest of the available bandwidth in order to transmit an efficient representation of the description error for better SNR. The overall process constitutes a new approach to efficient speech coding, with ICA being used in subspace speech coding for the first time. Constant bit rate (CBR) and variable bit rate (VBR) coding algorithms are employed with the proposed methods. TIMIT speech database is used in the experimental studies. Speech signals are synthesized at 2.4 kbps, 8 kbps, 12.2 kbps, 16 kbps, 16.4kbps and 19.85 kbps rates by using various frame lengths. The qualities of synthesized speech signals are compared to those of available speech codecs, i.e., LPC (2.4 kbps), G.728 (LD-CELP, 16 kbps), G.729A (CS-CELP, 8 kbps), EVS (16.4 kbps), AMR-NB (12.2 kbps) and AMR-WB (19.85 kbps). (C) 2017 Elsevier B.V. All rights reserved.