319 research outputs found
Adaptive DCTNet for Audio Signal Classification
In this paper, we investigate DCTNet for audio signal classification. Its
output feature is related to Cohen's class of time-frequency distributions. We
introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature
extraction. The A-DCTNet applies the idea of constant-Q transform, with its
center frequencies of filterbanks geometrically spaced. The A-DCTNet is
adaptive to different acoustic scales, and it can better capture low frequency
acoustic information that is sensitive to human audio perception than features
such as Mel-frequency spectral coefficients (MFSC). We use features extracted
by the A-DCTNet as input for classifiers. Experimental results show that the
A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art
performance in bird song classification rate, and improve artist identification
accuracy in music data. They demonstrate A-DCTNet's applicability to signal
processing problems.Comment: International Conference of Acoustic and Speech Signal Processing
(ICASSP). New Orleans, United States, March, 201
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to
address the challenges of planning for generating coherent multi-sentence
stories for the visual storytelling task. Within our framework, the task of
generating a story given a sequence of images is divided across a two-level
hierarchical decoder. The high-level decoder constructs a plan by generating a
semantic concept (i.e., topic) for each image in sequence. The low-level
decoder generates a sentence for each image using a semantic compositional
network, which effectively grounds the sentence generation conditioned on the
topic. The two decoders are jointly trained end-to-end using reinforcement
learning. We evaluate our model on the visual storytelling (VIST) dataset.
Empirical results from both automatic and human evaluations demonstrate that
the proposed hierarchically structured reinforced training achieves
significantly better performance compared to a strong flat deep reinforcement
learning baseline.Comment: Accepted to AAAI 201
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