A Multi-Frame PCA-Based Stereo Audio Coding Method

Abstract

With the increasing demand for high quality audio, stereo audio coding has become more and more important. In this paper, a multi-frame coding method based on Principal Component Analysis (PCA) is proposed for the compression of audio signals, including both mono and stereo signals. The PCA-based method makes the input audio spectral coefficients into eigenvectors of covariance matrices and reduces coding bitrate by grouping such eigenvectors into fewer number of vectors. The multi-frame joint technique makes the PCA-based method more efficient and feasible. This paper also proposes a quantization method that utilizes Pyramid Vector Quantization (PVQ) to quantize the PCA matrices proposed in this paper with few bits. Parametric coding algorithms are also employed with PCA to ensure the high efficiency of the proposed audio codec. Subjective listening tests with Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) have shown that the proposed PCA-based coding method is efficient at processing stereo audio.

Special Issue on Modelling, Simulation and Data Analysis in Acoustical Problems

“…Noncontact audio recording and a multi-frame Principal Component Analysis (PCA) based stereo audio coding method are proposed respectively by Sato et al [28] and Wang et al [29].…”

An Efficient Automatic Midsagittal Plane Extraction in Brain MRI

“…Therefore, by considering all these observations and assuming that the head is an ellipsoid-like 3-D solid object, a PCA-based algorithm is designed for MSP extraction. PCA is a fundamental and prevailing statistical technique also known as Hotelling transform substantially used in digital image processing for data dimension reduction [34], feature pattern recognition [35], quality control [36], data decorrelation [37], data compression [38], and segmentation [39]. It is also acknowledged as a low-level digital image processing tool for tasks such as the orientation assessment and alignment of particular shape objects [40,41].…”

“…Visual comparison of the proposed algorithm in extracting the symmetric axis (MSP) from real brain MRIs, (a) the NFBS database[38], (b) images of the same subjects with Gaussian noise, (c)…”

“…Visual comparison of the proposed algorithm in extracting the symmetric axis (MSP) from real brain MRIs, (a) the NFBS database[38], (b) images of the same subjects with Gaussian noise, (c) the IBSR database[39], (d) the MNI BITE database Group 2, and (e) the MNI BITE database Group 3[40].…”