14,212 research outputs found
Byte-based Language Identification with Deep Convolutional Networks
We report on our system for the shared task on discriminating between similar
languages (DSL 2016). The system uses only byte representations in a deep
residual network (ResNet). The system, named ResIdent, is trained only on the
data released with the task (closed training). We obtain 84.88% accuracy on
subtask A, 68.80% accuracy on subtask B1, and 69.80% accuracy on subtask B2. A
large difference in accuracy on development data can be observed with
relatively minor changes in our network's architecture and hyperparameters. We
therefore expect fine-tuning of these parameters to yield higher accuracies.Comment: 7 pages. Adapted reviewer comments. arXiv admin note: text overlap
with arXiv:1609.0705
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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