5 research outputs found
Robust binaural localization of a target sound source by combining spectral source models and deep neural networks
Despite there being a clear evidence for top–down (e.g., attentional) effects in biological spatial hearing, relatively few machine hearing systems exploit the top–down model-based knowledge in sound localization. This paper addresses this issue by proposing a novel framework for the binaural sound localization that combines the model-based information about the spectral characteristics of sound sources and deep neural networks (DNNs). A target source model and a background source model are first estimated during a training phase using spectral features extracted from sound signals in isolation. When the identity of the background source is not available, a universal background model can be used. During testing, the source models are used jointly to explain the mixed observations and improve the localization process by selectively weighting source azimuth posteriors output by a DNN-based localization system. To address the possible mismatch between the training and testing, a model adaptation process is further employed the on-the-fly during testing, which adapts the background model parameters directly from the noisy observations in an iterative manner. The proposed system, therefore, combines the model-based and data-driven information flow within a single computational framework. The evaluation task involved localization of a target speech source in the presence of an interfering source and room reverberation. Our experiments show that by exploiting the model-based information in this way, the sound localization performance can be improved substantially under various noisy and reverberant conditions
Comparison of Binaural RTF-Vector-Based Direction of Arrival Estimation Methods Exploiting an External Microphone
In this paper we consider a binaural hearing aid setup, where in addition to
the head-mounted microphones an external microphone is available. For this
setup, we investigate the performance of several relative transfer function
(RTF) vector estimation methods to estimate the direction of arrival(DOA) of
the target speaker in a noisy and reverberant acoustic environment. More in
particular, we consider the state-of-the-art covariance whitening (CW) and
covariance subtraction (CS) methods, either incorporating the external
microphone or not, and the recently proposed spatial coherence (SC) method,
requiring the external microphone. To estimate the DOA from the estimated RTF
vector, we propose to minimize the frequency-averaged Hermitian angle between
the estimated head-mounted RTF vector and a database of prototype head-mounted
RTF vectors. Experimental results with stationary and moving speech sources in
a reverberant environment with diffuse-like noise show that the SC method
outperforms the CS method and yields a similar DOA estimation accuracy as the
CW method at a lower computational complexity.Comment: Submitted to EUSIPCO 202