Pre-trained Spatial Priors on Multichannel NMF for Music Source
  Separation
Pablo Cabanas-Molero Antonio J. Munoz-Montoro Julio Carabias-Orti Pedro Vera-Candeas
This paper presents a novel approach to sound source separation that
leverages spatial information obtained during the recording setup. Our method
trains a spatial mixing filter using solo passages to capture information about
the room impulse response and transducer response at each sensor location. This
pre-trained filter is then integrated into a multichannel non-negative matrix
factorization (MNMF) scheme to better capture the variances of different sound
sources. The recording setup used in our experiments is the typical setup for
orchestra recordings, with a main microphone and a close "cardioid" or
"supercardioid" microphone for each section of the orchestra. This makes the
proposed method applicable to many existing recordings. Experiments on
polyphonic ensembles demonstrate the effectiveness of the proposed framework in
separating individual sound sources, improving performance compared to
conventional MNMF methods.