Sound source separation algorithm using phase difference and angle distribution modeling near the target
Abstract
In this paper we present a novel two-microphone sound source
separation algorithm, which selects the signal from the target
direction while suppressing signals from other directions. In
this algorithm, which is referred to as Power Angle Information
Near Target (PAINT), we first calculate phase difference
for each time-frequency bin. From the phase difference, the angle
of a sound source is estimated. For each frame, we represent
the source angle distribution near the expected target location as
a mixture of a Gaussian and a uniform distributions and obtain
binary masks using hypothesis testing. Continuous masks are
calculated from the binary masks using the Channel Weighting
(CW) technique, and processed speech is synthesized using
IFFT and the OverLap-Add (OLA) method. We demonstrate
that the algorithm described in this paper shows better speech
recognition accuracy compared to conventional approaches and
our previous approaches
separation algorithm, which selects the signal from the target
direction while suppressing signals from other directions. In
this algorithm, which is referred to as Power Angle Information
Near Target (PAINT), we first calculate phase difference
for each time-frequency bin. From the phase difference, the angle
of a sound source is estimated. For each frame, we represent
the source angle distribution near the expected target location as
a mixture of a Gaussian and a uniform distributions and obtain
binary masks using hypothesis testing. Continuous masks are
calculated from the binary masks using the Channel Weighting
(CW) technique, and processed speech is synthesized using
IFFT and the OverLap-Add (OLA) method. We demonstrate
that the algorithm described in this paper shows better speech
recognition accuracy compared to conventional approaches and
our previous approaches
Research Areas
