  We propose an algorithm to denoise speakers from a single microphone in the
presence of non-stationary and dynamic noise. Our approach is inspired by the
recent success of neural network models separating speakers from other speakers
and singers from instrumental accompaniment. Unlike prior art, we leverage
embedding spaces produced with source-contrastive estimation, a technique
derived from negative sampling techniques in natural language processing, while
simultaneously obtaining a continuous inference mask. Our embedding space
directly optimizes for the discrimination of speaker and noise by jointly
modeling their characteristics. This space is generalizable in that it is not
speaker or noise specific and is capable of denoising speech even if the model
has not seen the speaker in the training set. Parameters are trained with dual
objectives: one that promotes a selective bandpass filter that eliminates noise
at time-frequency positions that exceed signal power, and another that
proportionally splits time-frequency content between signal and noise. We
compare to state of the art algorithms as well as traditional sparse
non-negative matrix factorization solutions. The resulting algorithm avoids
severe computational burden by providing a more intuitive and easily optimized
approach, while achieving competitive accuracy.
