Speech separation is the task of separating target speech from background interference.
Traditionally, speech separation is studied as a signal processing problem. A more recent …

Local and non-local attention operations are two ubiquitous operations in the domain of
speech enhancement (SE), and they are effective to generate more discriminative patterns …

This study proposes a fully convolutional network (FCN) model for raw waveform-based
speech enhancement. The proposed system performs speech enhancement in an end-to …

For supervised speech enhancement, contextual information is important for accurate mask
estimation or spectral mapping. However, commonly used deep neural networks (DNNs) are …

This paper proposes a signal-to-noise-ratio (SNR) aware convolutional neural network
(CNN) model for speech enhancement (SE). Because the CNN model can deal with local …

In recent years, deep learning models have been employed for speech enhancement. Most
of the existing methods based on deep learning use fully Convolutional Neural Network …

Neural networks can be used to identify and remove noise from noisy speech spectrum
(denoisisng autoencoders, DAEs). The DAEs are typically implemented using the fully …

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological,
physical and social changes. Changes that cannot be observed visually may be trackable …

The steered response power (SRP) methods can be used to build a map of sound direction
likelihood. In the presence of interference and reverberation, the map will exhibit multiple …

In supervised speech separation, deep neural networks (DNNs) are typically employed to
predict an ideal time-frequency (TF) mask in order to remove background interference …