TY - UNPB
T1 - Description of algorithms for Ben-Gurion University Submission to the LOCATA challenge
AU - Madmoni, Lior
AU - Beit-On, Hanan
AU - Morgenstern, Hai
AU - Rafaely, Boaz
PY - 2018/12/1
Y1 - 2018/12/1
N2 - This paper summarizes the methods used to localize the sources recorded
for the LOCalization And TrAcking (LOCATA) challenge. The tasks of
stationary sources and arrays were considered, i.e., tasks 1 and 2 of
the challenge, which were recorded with the Nao robot array, and the
Eigenmike array. For both arrays, direction of arrival (DOA) estimation
has been performed with measurements in the short time Fourier transform
domain, and with direct-path dominance (DPD) based tests, which aim to
identify time-frequency (TF) bins dominated by the direct sound. For the
recordings with Nao, a DPD test which is applied directly to the
microphone signals was used. For the Eigenmike recordings, a DPD based
test designed for plane-wave density measurements in the spherical
harmonics domain was used. After acquiring DOA estimates with TF bins
that passed the DPD tests, a stage of k-means clustering is performed,
to assign a final DOA estimate for each speaker.
AB - This paper summarizes the methods used to localize the sources recorded
for the LOCalization And TrAcking (LOCATA) challenge. The tasks of
stationary sources and arrays were considered, i.e., tasks 1 and 2 of
the challenge, which were recorded with the Nao robot array, and the
Eigenmike array. For both arrays, direction of arrival (DOA) estimation
has been performed with measurements in the short time Fourier transform
domain, and with direct-path dominance (DPD) based tests, which aim to
identify time-frequency (TF) bins dominated by the direct sound. For the
recordings with Nao, a DPD test which is applied directly to the
microphone signals was used. For the Eigenmike recordings, a DPD based
test designed for plane-wave density measurements in the spherical
harmonics domain was used. After acquiring DOA estimates with TF bins
that passed the DPD tests, a stage of k-means clustering is performed,
to assign a final DOA estimate for each speaker.
KW - Computer Science - Sound
KW - Electrical Engineering and Systems Science - Audio and Speech Processing
M3 - גרסה מוקדמת
BT - Description of algorithms for Ben-Gurion University Submission to the LOCATA challenge
ER -