2 research outputs found
Fast and Robust 3-D Sound Source Localization with DSVD-PHAT
This paper introduces a variant of the Singular Value Decomposition with
Phase Transform (SVD-PHAT), named Difference SVD-PHAT (DSVD-PHAT), to achieve
robust Sound Source Localization (SSL) in noisy conditions. Experiments are
performed on a Baxter robot with a four-microphone planar array mounted on its
head. Results show that this method offers similar robustness to noise as the
state-of-the-art Multiple Signal Classification based on Generalized Singular
Value Decomposition (GSVD-MUSIC) method, and considerably reduces the
computational load by a factor of 250. This performance gain thus makes
DSVD-PHAT appealing for real-time application on robots with limited on-board
computing power
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
Detecting sound source objects within visual observation is important for
autonomous robots to comprehend surrounding environments. Since sounding
objects have a large variety with different appearances in our living
environments, labeling all sounding objects is impossible in practice. This
calls for self-supervised learning which does not require manual labeling. Most
of conventional self-supervised learning uses monaural audio signals and images
and cannot distinguish sound source objects having similar appearances due to
poor spatial information in audio signals. To solve this problem, this paper
presents a self-supervised training method using 360{\deg} images and
multichannel audio signals. By incorporating with the spatial information in
multichannel audio signals, our method trains deep neural networks (DNNs) to
distinguish multiple sound source objects. Our system for localizing sound
source objects in the image is composed of audio and visual DNNs. The visual
DNN is trained to localize sound source candidates within an input image. The
audio DNN verifies whether each candidate actually produces sound or not. These
DNNs are jointly trained in a self-supervised manner based on a probabilistic
spatial audio model. Experimental results with simulated data showed that the
DNNs trained by our method localized multiple speakers. We also demonstrate
that the visual DNN detected objects including talking visitors and specific
exhibits from real data recorded in a science museum.Comment: Accepted for publication in 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS