A hearing device configured to be worn by a user at or in an ear of the user, the hearing device comprising ∘ an input unit for providing at least one electric input signal (IN(k,m)) in a time frequency representation k, m, where k and m are frequency and time indices, respectively, and k represents a frequency channel, the at least one electric input signal being representative of sound and comprising target signal components and noise components; and ∘ a signal processor comprising ∘ an SNR estimator (SNR-EST) for providing a target signal-to-noise ratio estimate for said at least one electric input signal in said time frequency representation (k,m); and ∘ an SNR-to-gain converter (SNR2G) for converting said target signal-to-noise ratio estimates to respective gain values (G(k,m)) in said time frequency representation (k,m), CHARACTERIZED IN THAT said SNR-to-gain converter (SNR2G) comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals (SNR p (k,m), G-OPT p (k,m)), wherein ▪ the neural network comprises a convolutional neural network, a feed-forward neural network, or a recurrent neural network, ▪ the plurality of training signals comprises examples of estimated signal-to-noise ratios as input obtained from a noisy input mixture and its corresponding output as a vector across frequency of a noise-reduced input signal mainly containing the desired signal, ▪ said training is performed by comparing optimal and estimated gains (G-OPT p (k,m), G-EST p (k,m)) using a cost function (LOSS), which provides a measure of a current cost or error estimate, wherein the cost or error estimate is iteratively fed back to the neural network (SNR2G) to modify the neural network parameters until an acceptable error estimate is achieved. ∘ an input unit for providing at least one electric input signal (IN(k,m)) in a time frequency representation k, m, where k and m are frequency and time indices, respectively, and k represents a frequency channel, the at least one electric input signal being representative of sound and comprising target signal components and noise components; and ∘ a signal processor comprising ∘ an SNR estimator (SNR-EST) for providing a target signal-to-noise ratio estimate for said at least one electric input signal in said time frequency representation (k,m); and ∘ an SNR-to-gain converter (SNR2G) for converting said target signal-to-noise ratio estimates to respective gain values (G(k,m)) in said time frequency representation (k,m), CHARACTERIZED IN THAT said SNR-to-gain converter (SNR2G) comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals (SNR p (k,m), G-OPT p (k,m)), wherein ▪ the neural network comprises a convolutional neural network, a feed-forward neural network, or a recurrent neural network, ▪ the plurality of training signals comprises examples of estimated signal-to-noise ratios as input obtained from a noisy input mixture and its corresponding output as a vector across frequency of a noise-reduced input signal mainly containing the desired signal, ▪ said training is performed by comparing optimal and estimated gains (G-OPT p (k,m), G-EST p (k,m)) using a cost function (LOSS), which provides a measure of a current cost or error estimate, wherein the cost or error estimate is iteratively fed back to the neural network (SNR2G) to modify the neural network parameters until an acceptable error estimate is achieved. ∘ an SNR estimator (SNR-EST) for providing a target signal-to-noise ratio estimate for said at least one electric input signal in said time frequency representation (k,m); and ∘ an SNR-to-gain converter (SNR2G) for converting said target signal-to-noise ratio estimates to respective gain values (G(k,m)) in said time frequency representation (k,m), CHARACTERIZED IN THAT said SNR-to-gain converter (SNR2G) comprises a neural network, wherein the weights of the neural network have been trained with a plurality of training signals (SNR p (k,m), G-OPT p (k,m)), wherein ▪ the neural network comprises a convolutional neural network, a feed-forward neural network, or a recurrent neural network, ▪ the plurality of training signals comprises examples of estimated signal-to-noise ratios as input obtained from a noisy input mixture and its corresponding output as a vector across frequency of a noise-reduced input signal mainly containing the desired signal, ▪ said training is performed by comparing optimal and estimated gains (G-OPT p (k,m), G-EST p (k,m)) using a cost function (LOSS), which provides a measure of a current cost or error estimate, wherein the cost or error estimate is iteratively fed back to the neural network (SNR2G) to modify the neural network parameters until an acceptable error estimate is achieved. ▪ the neural network comprises a convolutional neural network, a feed-forward neural network, or a recurrent neural network, ▪ the plurality of training signals comprises examples of estimated signal-to-noise ratios as input obtained from a noisy input mixture and its corresponding output as a vector across frequency of a noise-reduced input signal mainly containing the desired signal, ▪ said training is performed by comparing optimal and estimated gains (G-OPT p (k,m), G-EST p (k,m)) using a cost function (LOSS), which provides a measure of a current cost or error estimate, wherein the cost or error estimate is iteratively fed back to the neural network (SNR2G) to modify the neural network parameters until an acceptable error estimate is achieved.