20,285 research outputs found
Rethinking Recurrent Latent Variable Model for Music Composition
We present a model for capturing musical features and creating novel
sequences of music, called the Convolutional Variational Recurrent Neural
Network. To generate sequential data, the model uses an encoder-decoder
architecture with latent probabilistic connections to capture the hidden
structure of music. Using the sequence-to-sequence model, our generative model
can exploit samples from a prior distribution and generate a longer sequence of
music. We compare the performance of our proposed model with other types of
Neural Networks using the criteria of Information Rate that is implemented by
Variable Markov Oracle, a method that allows statistical characterization of
musical information dynamics and detection of motifs in a song. Our results
suggest that the proposed model has a better statistical resemblance to the
musical structure of the training data, which improves the creation of new
sequences of music in the style of the originals.Comment: Published as a conference paper at IEEE MMSP 201
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
Sequential data often possesses a hierarchical structure with complex
dependencies between subsequences, such as found between the utterances in a
dialogue. In an effort to model this kind of generative process, we propose a
neural network-based generative architecture, with latent stochastic variables
that span a variable number of time steps. We apply the proposed model to the
task of dialogue response generation and compare it with recent neural network
architectures. We evaluate the model performance through automatic evaluation
metrics and by carrying out a human evaluation. The experiments demonstrate
that our model improves upon recently proposed models and that the latent
variables facilitate the generation of long outputs and maintain the context.Comment: 15 pages, 5 tables, 4 figure
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