29,222 research outputs found
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
A Neural Network Approach for Mixing Language Models
The performance of Neural Network (NN)-based language models is steadily
improving due to the emergence of new architectures, which are able to learn
different natural language characteristics. This paper presents a novel
framework, which shows that a significant improvement can be achieved by
combining different existing heterogeneous models in a single architecture.
This is done through 1) a feature layer, which separately learns different
NN-based models and 2) a mixture layer, which merges the resulting model
features. In doing so, this architecture benefits from the learning
capabilities of each model with no noticeable increase in the number of model
parameters or the training time. Extensive experiments conducted on the Penn
Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a
significant reduction of the perplexity when compared to state-of-the-art
feedforward as well as recurrent neural network architectures.Comment: Published at IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP) 2017. arXiv admin note: text overlap with
arXiv:1703.0806
- …