10,024 research outputs found
DeFT-AN: Dense Frequency-Time Attentive Network for Multichannel Speech Enhancement
In this study, we propose a dense frequency-time attentive network (DeFT-AN)
for multichannel speech enhancement. DeFT-AN is a mask estimation network that
predicts a complex spectral masking pattern for suppressing the noise and
reverberation embedded in the short-time Fourier transform (STFT) of an input
signal. The proposed mask estimation network incorporates three different types
of blocks for aggregating information in the spatial, spectral, and temporal
dimensions. It utilizes a spectral transformer with a modified feed-forward
network and a temporal conformer with sequential dilated convolutions. The use
of dense blocks and transformers dedicated to the three different
characteristics of audio signals enables more comprehensive enhancement in
noisy and reverberant environments. The remarkable performance of DeFT-AN over
state-of-the-art multichannel models is demonstrated based on two popular noisy
and reverberant datasets in terms of various metrics for speech quality and
intelligibility.Comment: 5 pages, 2 figures, 3 table
Statistical Analysis of the Metropolitan Seoul Subway System: Network Structure and Passenger Flows
The Metropolitan Seoul Subway system, consisting of 380 stations, provides
the major transportation mode in the metropolitan Seoul area. Focusing on the
network structure, we analyze statistical properties and topological
consequences of the subway system. We further study the passenger flows on the
system, and find that the flow weight distribution exhibits a power-law
behavior. In addition, the degree distribution of the spanning tree of the
flows also follows a power law.Comment: 10 pages, 4 figure
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks
As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a
variety of fields, there is an increasing interest in understanding the complex
internal mechanisms of DNNs. In this paper, we propose Relative Attributing
Propagation (RAP), which decomposes the output predictions of DNNs with a new
perspective of separating the relevant (positive) and irrelevant (negative)
attributions according to the relative influence between the layers. The
relevance of each neuron is identified with respect to its degree of
contribution, separated into positive and negative, while preserving the
conservation rule. Considering the relevance assigned to neurons in terms of
relative priority, RAP allows each neuron to be assigned with a bi-polar
importance score concerning the output: from highly relevant to highly
irrelevant. Therefore, our method makes it possible to interpret DNNs with much
clearer and attentive visualizations of the separated attributions than the
conventional explaining methods. To verify that the attributions propagated by
RAP correctly account for each meaning, we utilize the evaluation metrics: (i)
Outside-inside relevance ratio, (ii) Segmentation mIOU and (iii) Region
perturbation. In all experiments and metrics, we present a sizable gap in
comparison to the existing literature. Our source code is available in
\url{https://github.com/wjNam/Relative_Attributing_Propagation}.Comment: 8 pages, 7 figures, Accepted paper in AAAI Conference on Artificial
Intelligence (AAAI), 202
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