Abstract
Perceived audio quality is an important metric to measure the perception degradation of multi-channel audio signals especially for coding and rendering systems. Conventional objective quality measurement such as PEAQ (Perceptual Evaluation of Audio Quality) is limited to describe both the basic audio quality and the spatial impression. A novel prediction model is proposed to predict the subjective quality of 5.1-channels audio systems. Two attributes are included in the evaluation including basic quality and surround effects. Multiple Linear Regression (MLR) combined with Principal Component Analysis (PCA) is used to establish the prediction model from the objective parameters to subjective audio quality. Data set for model training and testing is obtained from formal listening tests under different coding conditions. Preliminary experiment results with 5.1-channels audio show that the proposed model can predict multi-channel audio quality more accurately than the conventional PEAQ method considering both the basic audio quality and the surround effects.
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References
ISO/IEC 13818-3: Information technology-Generic coding of moving pictures and associated audio information – Part 3: Audio (1998)
ISO/IEC 13818-7: Information technology - Generic coding of moving pictures and associated audio information-Part 7: Advanced Audio Coding (AAC) (2006)
ISO/IEC JTC1/SC29/WG11: Information technology - report on the verification tests of MPEG-D MPEG surround (2007)
ITU-R BS.775-2: Multichannel stereophonic sound system with and without accompanying picture (2006)
Cheng, Y., Ruimin, H., Liuyue, S., et al.: A 3D audio coding technique based on extracting the distance parameter. In: IEEE International Conference on Multimedia and Expo, pp. 1–6. IEEE Press, California (2014)
Bin, C., Christian, R., Ian, S.B., Xiguang, Z.: A general compression approach to multi-channel three-dimensional audio. IEEE Trans. Audio Speech Lang. Process. 21(8), 1676–1688 (2013)
ITU-R BS.1116-1: Methods for the subjective assessment of small impairments in audio systems including multichannel sound systems, Geneva, Switzerland (1997)
ITU-R BS.1285: Pre-selection methods for the subjective assessment of small impairments in audio systems, Geneva, Switzerland (1997)
ITU-R BS.1534: Method for the subjective assessment of intermediate quality level of coding systems, Geneva, Switzerland (2001)
ITU-R BS.1387-1: Method for objective measurements of perceived audio quality, Geneva, Switzerland (2001)
Inyong, C., Shinn-Cunningham, B.G., Sang, B.C., Sung, K.-M.: Objective measurement of perceived auditory quality in multichannel audio compression coding systems. J. Audio Eng. Soc. 56, 3–17 (2008)
Schafer, M., Bahram, M., Vary, P.: An extension of the PEAQ measure by a binaural hearing model. In: International Conference on Acoustics, Speech and Signal Processing, pp. 8164–8168. IEEE Press, Vancouver (2013)
Smimite, A., Beghdadi, A., Chen, K., Jafjaf, O.: A new approach for spatial audio quality assessment. In: International Conference on Telecommunications and Multimedia, pp. 46–51. IEEE Press, Greece (2014)
Jeroen, B., Par, S.V.D., Armin, K., Erik, S., Jeroen, B., Erik, S.: Parametric coding of stereo audio. EURASIP J. Adv. Signal Process. 9, 1305–1322 (2005)
Faller, C., Baumgarte, F.: Binaural cue coding: a novel and efficient representation of spatial audio. In: International Conference on Acoustics, Speech and Signal Processing, pp. 1841–1844. IEEE Press, Florida (2002)
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Wang, J., Zhao, Y., Li, W., Wang, F., Fei, Z., Xie, X. (2015). Prediction Model of Multi-channel Audio Quality Based on Multiple Linear Regression. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_66
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DOI: https://doi.org/10.1007/978-3-319-24075-6_66
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