General Algorithms For Estimating Spectrogram And Transfer Functions Of Target Signal For Blind Suppression Of Diffuse Noise
Nobutaka Ito, Emmanuel Vincent, Nobutaka Ono, Shigeki Sagayama

Abstract:
We propose two algorithms for jointly estimating the power spectrogram
and the room transfer functions of a target signal in diffuse
noise. These estimates can be used to design a multichannel
Wiener filter, and thereby separate a target signal from an unknown
direction from diffuse noise. We express a diffuse noise model as a
subspace of a matrix linear space, which consists of Hermitian matrices
instead of Euclidean vectors. This general framework enables
the design of new general algorithms applicable to all specific noise
models, instead of multiple specific algorithms each applicable to a
single model. The more general proposed algorithms resulted in superior
noise suppression performance to our previous algorithms in
terms of an output signal-to-noise ratio (SNR).