Time-Shifting Based Primary-Ambient Extraction for Spatial Audio Reproduction

One of the key issues in spatial audio analysis and reproduction is to decompose a signal into primary and ambient components based on their directional and diffuse spatial features, respectively. Existing approaches employed in primary-ambient extraction (PAE), such as principal component analysis (PCA), are mainly based on a basic stereo signal model. The performance of these PAE approaches has not been well studied for the input signals that do not satisfy all the assumptions of the stereo signal model. In practice, one such case commonly encountered is that the primary components of the stereo signal are partially correlated at zero lag, referred to as the primary-complex case. In this paper, we take PCA as a representative of existing PAE approaches and investigate the performance degradation of PAE with respect to the correlation of the primary components in the primary-complex case. A time-shifting technique is proposed in PAE to alleviate the performance degradation due to the low correlation of the primary components in such stereo signals. This technique involves time-shifting the input signal according to the estimated inter-channel time difference of the primary component prior to the signal decomposition using conventional PAE approaches. To avoid the switching artifacts caused by the varied time-shifting in successive time frames, overlapped output mapping is suggested. Based on the results from our experiments, PAE approaches with the proposed time-shifting technique are found to be superior to the conventional PAE approaches in terms of extraction accuracy and spatial accuracy.