3 research outputs found
Acoustic DoA Estimation by One Unsophisticated Sensor
We show how introducing known scattering can be used in direction of arrival estimation by a single sensor. We first present an analysis of the geometry of the underlying measurement space and show how it enables localizing white sources. Then, we extend the solution to more challenging non-white sources like speech by including a source model and considering convex relaxations with group sparsity penalties. We conclude with numerical simulations using an unsophisticated sensing device to validate the theory
Direction of Arrival with One Microphone, a few LEGOs, and Non-Negative Matrix Factorization
Conventional approaches to sound source localization require at least two
microphones. It is known, however, that people with unilateral hearing loss can
also localize sounds. Monaural localization is possible thanks to the
scattering by the head, though it hinges on learning the spectra of the various
sources. We take inspiration from this human ability to propose algorithms for
accurate sound source localization using a single microphone embedded in an
arbitrary scattering structure. The structure modifies the frequency response
of the microphone in a direction-dependent way giving each direction a
signature. While knowing those signatures is sufficient to localize sources of
white noise, localizing speech is much more challenging: it is an ill-posed
inverse problem which we regularize by prior knowledge in the form of learned
non-negative dictionaries. We demonstrate a monaural speech localization
algorithm based on non-negative matrix factorization that does not depend on
sophisticated, designed scatterers. In fact, we show experimental results with
ad hoc scatterers made of LEGO bricks. Even with these rudimentary structures
we can accurately localize arbitrary speakers; that is, we do not need to learn
the dictionary for the particular speaker to be localized. Finally, we discuss
multi-source localization and the related limitations of our approach.Comment: This article has been accepted for publication in IEEE/ACM
Transactions on Audio, Speech, and Language processing (TASLP