2 research outputs found
The IOA System for Deep Noise Suppression Challenge using a Framework Combining Dynamic Attention and Recursive Learning
This technical report describes our system that is submitted to the Deep
Noise Suppression Challenge and presents the results for the non-real-time
track. To refine the estimation results stage by stage, we utilize recursive
learning, a type of training protocol which aggravates the information through
multiple stages with a memory mechanism. The attention generator network is
designed to dynamically control the feature distribution of the noise reduction
network. To improve the phase recovery accuracy, we take the complex spectral
mapping procedure by decoding both real and imaginary spectra. For the final
blind test set, the average MOS improvements of the submitted system in
noreverb, reverb, and realrec categories are 0.49, 0.24, and 0.36,
respectively.Comment: 4 pages, 2 figure
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
In this paper, we address the problem of multiple view data fusion in the
presence of noise and interferences. Recent studies have approached this
problem using kernel methods, by relying particularly on a product of kernels
constructed separately for each view. From a graph theory point of view, we
analyze this fusion approach in a discrete setting. More specifically, based on
a statistical model for the connectivity between data points, we propose an
algorithm for the selection of the kernel bandwidth, a parameter, which, as we
show, has important implications on the robustness of this fusion approach to
interferences. Then, we consider the fusion of audio-visual speech signals
measured by a single microphone and by a video camera pointed to the face of
the speaker. Specifically, we address the task of voice activity detection,
i.e., the detection of speech and non-speech segments, in the presence of
structured interferences such as keyboard taps and office noise. We propose an
algorithm for voice activity detection based on the audio-visual signal.
Simulation results show that the proposed algorithm outperforms competing
fusion and voice activity detection approaches. In addition, we demonstrate
that a proper selection of the kernel bandwidth indeed leads to improved
performance