  In this paper, a Blind Source Separation (BSS) algorithm for multichannel
audio contents is proposed. Unlike common BSS algorithms targeting stereo audio
contents or microphone array signals, our technique is targeted at multichannel
audio such as 5.1 and 7.1ch audio. Since most multichannel audio object sources
are panned using the Inter-channel Loudness Difference (ILD), we employ the
ILVS (Inter-channel Loudness Vector Sum) concept to cluster common signals
(such as background music) from each channel. After separating the common
signals from each channel, we employ an Expectation Maximization (EM) algorithm
with a von-Mises distribution to successfully classify the clustering of sound
source objects and separate the audio signals from the original mixture. Our
proposed method can therefore separate common audio signals and object source
signals from multiple channels with reasonable quality. Our multichannel audio
content separation technique can be applied to an upmix system or a cinema
audio system requiring multichannel audio source separation.
