A method of audio processing, comprising: receiving, using at least one processor, bands of power spectra of an input audio signal and a microphone covariance for each band, wherein the microphone covariance is based on a configuration of microphones used to capture the input audio signal; for each band: estimating, using a machine learning classifier, respective probabilities of speech and noise; estimating, using a directionality model, a set of means for speech and noise, or a set of means and covariances for speech and noise, based on the microphone covariance for the band and the probabilities; estimating, using a level model, a mean and covariance of noise power based on the probabilities and the power spectra; determining, using the at least one processor, a first noise suppression gain based on the set of means for speech and noise, or the set of means and covariances for speech and noise, respectively, estimated using the directionality model; determining, using the at least one processor, a second noise suppression gain based on the mean and covariance estimated using the level model; selecting, using the at least one processor, one of the first noise suppression gain or the second noise suppression gain, or a sum of the first noise suppression gain and the second noise suppression gain, based on a signal to noise ratio of the input audio signal; scaling, using the at least one processor, a time-frequency representation of the input signal by the selected first or second noise suppression gain or the sum thereof for the band; and converting, using the at least one processor, the time-frequency representation into an output audio signal. receiving, using at least one processor, bands of power spectra of an input audio signal and a microphone covariance for each band, wherein the microphone covariance is based on a configuration of microphones used to capture the input audio signal; for each band: estimating, using a machine learning classifier, respective probabilities of speech and noise; estimating, using a directionality model, a set of means for speech and noise, or a set of means and covariances for speech and noise, based on the microphone covariance for the band and the probabilities; estimating, using a level model, a mean and covariance of noise power based on the probabilities and the power spectra; determining, using the at least one processor, a first noise suppression gain based on the set of means for speech and noise, or the set of means and covariances for speech and noise, respectively, estimated using the directionality model; determining, using the at least one processor, a second noise suppression gain based on the mean and covariance estimated using the level model; selecting, using the at least one processor, one of the first noise suppression gain or the second noise suppression gain, or a sum of the first noise suppression gain and the second noise suppression gain, based on a signal to noise ratio of the input audio signal; estimating, using a machine learning classifier, respective probabilities of speech and noise; estimating, using a directionality model, a set of means for speech and noise, or a set of means and covariances for speech and noise, based on the microphone covariance for the band and the probabilities; estimating, using a level model, a mean and covariance of noise power based on the probabilities and the power spectra; determining, using the at least one processor, a first noise suppression gain based on the set of means for speech and noise, or the set of means and covariances for speech and noise, respectively, estimated using the directionality model; determining, using the at least one processor, a second noise suppression gain based on the mean and covariance estimated using the level model; selecting, using the at least one processor, one of the first noise suppression gain or the second noise suppression gain, or a sum of the first noise suppression gain and the second noise suppression gain, based on a signal to noise ratio of the input audio signal; scaling, using the at least one processor, a time-frequency representation of the input signal by the selected first or second noise suppression gain or the sum thereof for the band; and converting, using the at least one processor, the time-frequency representation into an output audio signal.