438 research outputs found
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Many success stories involving deep neural networks are instances of
supervised learning, where available labels power gradient-based learning
methods. Creating such labels, however, can be expensive and thus there is
increasing interest in weak labels which only provide coarse information, with
uncertainty regarding time, location or value. Using such labels often leads to
considerable challenges for the learning process. Current methods for
weak-label training often employ standard supervised approaches that
additionally reassign or prune labels during the learning process. The
information gain, however, is often limited as only the importance of labels
where the network already yields reasonable results is boosted. We propose
treating weak-label training as an unsupervised problem and use the labels to
guide the representation learning to induce structure. To this end, we propose
two autoencoder extensions: class activity penalties and structured dropout. We
demonstrate the capabilities of our approach in the context of score-informed
source separation of music
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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