24,650 research outputs found
Embedding Multi-Task Address-Event- Representation Computation
Address-Event-Representation, AER, is a communication protocol that is
intended to transfer neuronal spikes between bioinspired chips. There are
several AER tools to help to develop and test AER based systems, which may
consist of a hierarchical structure with several chips that transmit spikes
among them in real-time, while performing some processing. Although these
tools reach very high bandwidth at the AER communication level, they require
the use of a personal computer to allow the higher level processing of the
event information. We propose the use of an embedded platform based on a
multi-task operating system to allow both, the AER communication and
processing without the requirement of either a laptop or a computer. In this
paper, we present and study the performance of an embedded multi-task AER
tool, connecting and programming it for processing Address-Event
information from a spiking generator.Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
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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