JSTSP Volume 13 Issue 2

Given the recent surge in developments of deep learning, this paper 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.

We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying geometric structure. This process is done through a deep encoder-decoder-based neural network architecture.

Given the recent surge in developments of deep learning, this paper 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 IEEE Signal Processing Society congratulates the following recipients who will receive the 2018 IEEE Signal Processing Society Paper Award for their papers published in the IEEE Journal of Selected Topics in Signal Processing. Presentation of the paper awards will take place at ICASSP 2019 in Brighton, UK.