A Spatial Squeezing approach to Ambisonic audio compression

Abstract

Spatially squeezed surround audio coding (S3AC) has been previously shown to provide efficient coding with perceptually accurate soundfield reconstruction when applied to ITU 5.1 multichannel audio. This paper investigates the application of S3AC to the coding of Ambisonic audio recordings. Traditional ambisonics achieve compression and backward compatibility through the use of the UHJ matrixing approach to obtain a stereo signal. In this paper the relationship to Ambisonic B-format signals is described and alternative approaches that derive a stereo or mono-downmix signal based on S3AC are presented and evaluated. The mono-downmix approach utilizes side information consisting of spatial cues that are quantized based on novel source localization listening experiments. Objective and subjective tests demonstrate significant improvements in the localization of sound sources resulting from decoding the compressed B-format signals to a 5.1 speaker playback. A SPATIAL SQUEEZING APPROACH TO AMBISONIC AUDIO COMPRESSION Bin Cheng, Christian Ritz and Ian BurnettWhisper Laboratories, University of Wollongong, Wollongong, NSW, Australia bc362@uow.edu.au, critz@uow.edu.au, ianb@uow.edu.au ABSTRACTSpatially Squeezed Surround Audio Coding (S 3 AC) has been previously shown to provide efficient coding with perceptually accurate soundfield reconstruction when applied to ITU 5.1 multichannel audio. This paper investigates the application of S 3 AC to the coding of Ambisonic audio recordings. Traditional Ambisonics achieve compression and backward compatibility through the use of the UHJ matrixing approach to obtain a stereo signal. In this paper the relationship to Ambisonic B-format signals is described and alternative approaches that derive a stereo or mono-downmix signal based on S 3 AC are presented and evaluated. The mono-downmix approach utilizes side information consisting of spatial cues that are quantized based on novel source localization listening experiments. Objective and subjective tests demonstrate significant improvements in the localization of sound sources resulting from decoding the compressed B-format signals to a 5.1 speaker playback.

Binaural reproduction of Spatially Squeezed Surround Audio

“…lTV 5.1 [8]) or a microphone recording (e.g. Ambisonics [10]), to identify virtual sound sources and their location (in general, one virtual source is identified or generated for each frequency band). The S3AC spatial squeezing process is then used to represent the original surround soundfield with a stereo signal.…”

“…This approach has the advantage that the source localisation derived in the S3AC soundfield analysis directly represents the location of virtual sources. This source localisation information can be saved as side information for further bit reduction, and the stereo signal then reduced to a monophonic downmix signal [10].…”

“…AAC [12] to further reduce the bit-rate. A mono downmix can be also created by using the S3AC virtual source with the localisation information saved as side information for surround sound recovery [10].…”

Three-dimensional audio parametric encoding based on perceptual characteristics of spatial cue1

“…Cheng, et al [3] designed a method of spatial localization quantification points (SLQP), dividing into high precision and low precision modes. Figure 8 shows the quantification value of the low precision mode, with every blue dot representing a quantification value, 658 in total, requiring 9.4-bit coding.…”

Expanded three-channel mid/side coding for three-dimensional multichannel audio systems

“…In 2007, Goodwin and Jot proposed a PCA-based multichannel compression framework for parametric coding [13], which can enhance specific audio scenarios and provide robust spatial audio coding. In 2008, Cheng et al proposed the Spatially Squeezed Surround Audio Coding (S 3 AC) for parametrically compressing the Ambisonics signal [14]. In 2009, Hellerud used an inter-channel prediction-based coding method to remove the redundancy between Ambisonics channels [15], which has low algorithm delay but high computational complexity.…”