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Multichannel Audio Upmixing by Time-Frequency Filtering Using Non-Negative Tensor Factorization
The expanding use of portable multimedia devices has intensified the need for better forms of scalable spatial audio coding (SAC) that match the connectivity rate and multichannel playback capabilities of the receiving device. A new SAC method is based on the parameterization of multichannel audio by representing it as a linear combination of objects composed of fixed spectral bases with time-varying gain and channel-dependent spatial gain. Spatial parameters can be estimated from the original multichannel signal using psychoacoustic properties of sound source localization. The base audio can be monophonic or downmixed stereophonic. Listening tests showed that the proposed SAC algorithm achieved the performance of conventional spatial audio coding methods with similar bit rates. The sound separation performance was evaluated and found applicable for separating sound sources in the coding domain directly.
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