  We propose a method using a long short-term memory (LSTM) network to estimate
the noise power spectral density (PSD) of single-channel audio signals
represented in the short time Fourier transform (STFT) domain. An LSTM network
common to all frequency bands is trained, which processes each frequency band
individually by mapping the noisy STFT magnitude sequence to its corresponding
noise PSD sequence. Unlike deep-learning-based speech enhancement methods that
learn the full-band spectral structure of speech segments, the proposed method
exploits the sub-band STFT magnitude evolution of noise with a long time
dependency, in the spirit of the unsupervised noise estimators described in the
literature. Speaker- and speech-independent experiments with different types of
noise show that the proposed method outperforms the unsupervised estimators,
and generalizes well to noise types that are not present in the training set.
