Very Low Bitrate Spatial Audio Coding with Dimensionality Reduction

Abstract : In this paper, we show that tensor compression techniques based on randomization and partial observations are very useful for spatial audio object coding. In this application, we aim at transmitting several audio signals called objects from a coder to a decoder. A common strategy is to transmit only the downmix of the objects along some small information permitting reconstruction at the decoder. In practice , this is done by transmitting compressed versions of the objects spectrograms and separating the mix with Wiener filters. Previous research used nonnegative tensor factorizations in this context, with bitrates as low as 1 kbps per object. Building on recent advances on tensor compression, we show that the computation time for encoding can be extremely reduced. Then, we demonstrate how the mixture can be exploited at the de-coder to avoid the transmission of many parameters, permitting bi-trates as low as 0.1 kbps per object for comparable performance.
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Communication dans un congrès
42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States. 2017
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Christian Rohlfing, Jeremy Cohen, Antoine Liutkus. Very Low Bitrate Spatial Audio Coding with Dimensionality Reduction. 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States. 2017. 〈hal-01515954〉

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