196,920 research outputs found
Single-Channel Speech Enhancement with Deep Complex U-Networks and Probabilistic Latent Space Models
In this paper, we propose to extend the deep, complex U-Network architecture
for speech enhancement by incorporating a probabilistic (i.e., variational)
latent space model. The proposed model is evaluated against several ablated
versions of itself in order to study the effects of the variational latent
space model, complex-value processing, and self-attention. Evaluation on the
MS-DNS 2020 and Voicebank+Demand datasets yields consistently high performance.
E.g., the proposed model achieves an SI-SDR of up to 20.2 dB, about 0.5 to 1.4
dB higher than its ablated version without probabilistic latent space, 2-2.4 dB
higher than WaveUNet, and 6.7 dB above PHASEN. Compared to real-valued
magnitude spectrogram processing with a variational U-Net, the complex U-Net
achieves an improvement of up to 4.5 dB SI-SDR. Complex spectrum encoding as
magnitude and phase yields best performance in anechoic conditions whereas real
and imaginary part representation results in better generalization to (novel)
reverberation conditions, possibly due to the underlying physics of sound
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network
It is crucial to ask how agents can achieve goals by generating action plans
using only partial models of the world acquired through habituated
sensory-motor experiences. Although many existing robotics studies use a
forward model framework, there are generalization issues with high degrees of
freedom. The current study shows that the predictive coding (PC) and active
inference (AIF) frameworks, which employ a generative model, can develop better
generalization by learning a prior distribution in a low dimensional latent
state space representing probabilistic structures extracted from well
habituated sensory-motor trajectories. In our proposed model, learning is
carried out by inferring optimal latent variables as well as synaptic weights
for maximizing the evidence lower bound, while goal-directed planning is
accomplished by inferring latent variables for maximizing the estimated lower
bound. Our proposed model was evaluated with both simple and complex robotic
tasks in simulation, which demonstrated sufficient generalization in learning
with limited training data by setting an intermediate value for a
regularization coefficient. Furthermore, comparative simulation results show
that the proposed model outperforms a conventional forward model in
goal-directed planning, due to the learned prior confining the search of motor
plans within the range of habituated trajectories.Comment: 30 pages, 19 figure
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