11,743 research outputs found
A recurrent neural network for classification of unevenly sampled variable stars
Astronomical surveys of celestial sources produce streams of noisy time
series measuring flux versus time ("light curves"). Unlike in many other
physical domains, however, large (and source-specific) temporal gaps in data
arise naturally due to intranight cadence choices as well as diurnal and
seasonal constraints. With nightly observations of millions of variable stars
and transients from upcoming surveys, efficient and accurate discovery and
classification techniques on noisy, irregularly sampled data must be employed
with minimal human-in-the-loop involvement. Machine learning for inference
tasks on such data traditionally requires the laborious hand-coding of
domain-specific numerical summaries of raw data ("features"). Here we present a
novel unsupervised autoencoding recurrent neural network (RNN) that makes
explicit use of sampling times and known heteroskedastic noise properties. When
trained on optical variable star catalogs, this network produces supervised
classification models that rival other best-in-class approaches. We find that
autoencoded features learned on one time-domain survey perform nearly as well
when applied to another survey. These networks can continue to learn from new
unlabeled observations and may be used in other unsupervised tasks such as
forecasting and anomaly detection.Comment: 23 pages, 14 figures. The published version is at Nature Astronomy
(https://www.nature.com/articles/s41550-017-0321-z). Source code for models,
experiments, and figures at
https://github.com/bnaul/IrregularTimeSeriesAutoencoderPaper (Zenodo Code
DOI: 10.5281/zenodo.1045560
Efficient Estimation of Word Representations in Vector Space
We propose two novel model architectures for computing continuous vector
representations of words from very large data sets. The quality of these
representations is measured in a word similarity task, and the results are
compared to the previously best performing techniques based on different types
of neural networks. We observe large improvements in accuracy at much lower
computational cost, i.e. it takes less than a day to learn high quality word
vectors from a 1.6 billion words data set. Furthermore, we show that these
vectors provide state-of-the-art performance on our test set for measuring
syntactic and semantic word similarities
A Recurrent Encoder-Decoder Approach with Skip-filtering Connections for Monaural Singing Voice Separation
The objective of deep learning methods based on encoder-decoder architectures
for music source separation is to approximate either ideal time-frequency masks
or spectral representations of the target music source(s). The spectral
representations are then used to derive time-frequency masks. In this work we
introduce a method to directly learn time-frequency masks from an observed
mixture magnitude spectrum. We employ recurrent neural networks and train them
using prior knowledge only for the magnitude spectrum of the target source. To
assess the performance of the proposed method, we focus on the task of singing
voice separation. The results from an objective evaluation show that our
proposed method provides comparable results to deep learning based methods
which operate over complicated signal representations. Compared to previous
methods that approximate time-frequency masks, our method has increased
performance of signal to distortion ratio by an average of 3.8 dB
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
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