7,540 research outputs found
Polyphonic Sound Event Detection by using Capsule Neural Networks
Artificial sound event detection (SED) has the aim to mimic the human ability
to perceive and understand what is happening in the surroundings. Nowadays,
Deep Learning offers valuable techniques for this goal such as Convolutional
Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has
been recently introduced in the image processing field with the intent to
overcome some of the known limitations of CNNs, specifically regarding the
scarce robustness to affine transformations (i.e., perspective, size,
orientation) and the detection of overlapped images. This motivated the authors
to employ CapsNets to deal with the polyphonic-SED task, in which multiple
sound events occur simultaneously. Specifically, we propose to exploit the
capsule units to represent a set of distinctive properties for each individual
sound event. Capsule units are connected through a so-called "dynamic routing"
that encourages learning part-whole relationships and improves the detection
performance in a polyphonic context. This paper reports extensive evaluations
carried out on three publicly available datasets, showing how the CapsNet-based
algorithm not only outperforms standard CNNs but also allows to achieve the
best results with respect to the state of the art algorithms
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition
Domain alignment in convolutional networks aims to learn the degree of
layer-specific feature alignment beneficial to the joint learning of source and
target datasets. While increasingly popular in convolutional networks, there
have been no previous attempts to achieve domain alignment in recurrent
networks. Similar to spatial features, both source and target domains are
likely to exhibit temporal dependencies that can be jointly learnt and aligned.
In this paper we introduce Dual-Domain LSTM (DDLSTM), an architecture that is
able to learn temporal dependencies from two domains concurrently. It performs
cross-contaminated batch normalisation on both input-to-hidden and
hidden-to-hidden weights, and learns the parameters for cross-contamination,
for both single-layer and multi-layer LSTM architectures. We evaluate DDLSTM on
frame-level action recognition using three datasets, taking a pair at a time,
and report an average increase in accuracy of 3.5%. The proposed DDLSTM
architecture outperforms standard, fine-tuned, and batch-normalised LSTMs.Comment: To appear in CVPR 201
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