Methods, systems, and apparatus, including computer programs encoded on computer storage media, for audio processing using neural networks. One of the systems includes multiple neural network layers, wherein the neural network system is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output for the audio sample, the plurality of neural network layers comprising: a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features; and one or more other neural network layers having respective layer parameters, wherein the one or more neural network layers are configured to process frequency domain features to generate a neural network output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a neural network that includes a plurality of neural network layers on training data, wherein the neural network is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output from the audio sample, wherein the neural network comprises (i) a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features and (ii) one or more other neural network layers having respective layer parameters and are configured to process the frequency domain features to generate the neural network output, and wherein the method comprises: obtaining training data comprising, for each of a plurality of training audio samples, time domain features of the training audio sample and a known output for the training audio sample; and training the neural network on the training data to adjust the values of the parameters of the other neural network layers and to adjust the values of the F-T layer parameters to define an optimal transformation from time-domain features to frequency-domain features. 2. The method of claim 1, wherein the transformation defined by the set of F-T layer parameters that transforms a window of time domain features into frequency domain features is represented by a discrete Fourier transform (DFT) matrix. 3. The method of claim 1, wherein the set of F-T layer parameters comprise a transformation matrix, wherein entries of each row of the transformation matrix comprise a respective sampled basis function. 4. The method of claim 3, wherein a first parameter for each row determines a frequency of each of the entries in the row and a second parameter for each row determines an amplitude of each of the entries in the row. 5. The method of claim 3, wherein the number of rows in the transformation matrix equal the number of frequencies that are extracted. 6. The method of claim 1, wherein the set of F-T layer parameters comprise a transformation matrix, wherein the transformation matrix comprises a windowing function. 7. The method of claim 1, wherein the neural network is configured to apply a windowing function to an F-T layer input. 8. The method of claim 1, wherein the training data comprises a plurality of multi-channel training data. 9. The method of claim 8, wherein the neural network comprises multiple F-T layers that are each configured to apply a transformation defined by a respective set of F-T layer parameters that transforms a window of time domain features into frequency domain features, and wherein training the neural network comprises concatenating frequency domain features generated by each of the multiple F-T layers to perform a multi-timescale DFT. 10. The method of claim 9, wherein the multiple F-T layers are each configured to apply a transformation defined by a shared set of F-T layer parameters. 11. The method of claim 8, further comprising merging the plurality of multi-channel training data to a single training data input. 12. The method of claim 1, wherein training the neural network on the training data comprises, for each window of time domain features and for the F-T layer: receiving a back-propagated gradient for the window of time domain features from a neural network layer above the F-T layer; computing the gradient of an error function for the neural network, wherein the error function is dependent on a product of the F-T layer parameters and the received back-propagated gradient; and updating the F-T layer parameters using the computed gradient. 13. The method of claim 1, wherein the window of time domain features in which the frequency-transform (F-T) layer operates is dependent on the number of received time domain features. 14. The method of claim 1, wherein the F-T layer is a fully connected neural network layer. 15. The method of claim 1, wherein the neural network is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output that classifies the audio sample and the training data comprises, for each of a plurality of training audio samples, time domain features of the training audio sample and a known classification of the training audio sample. 16. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to perform operations to implement: a neural network system, the neural network system comprising a plurality of neural network layers, wherein the neural network system is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output for the audio sample, the plurality of neural network layers comprising: a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features; and one or more other neural network layers having respective layer parameters, wherein the one or more neural network layers are configured to process frequency domain features to generate a neural network output. 17. The system of claim 16, wherein the F-T layer parameters are trained F-T layer parameters that define an optimal transformation from time domain to frequency domain. 18. The system of claim 16, wherein the time domain features of the audio sample comprise a plurality of multi-channel time domain features. 19. The system of claim 18, wherein the neural network system comprises multiple F-T layers that are each configured to apply a respective transformation defined by a respective set of F-T layer parameters that transforms a window of time domain features into frequency domain features, and wherein processing the time domain features comprises concatenating frequency domain features generated by each of the multiple F-T layers to perform a multi-timescale DFT. 20. The system of claim 18, wherein the multiple F-T layers are each configured to apply a transformation defined by a shared set of F-T layer parameters. 21. A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to perform operations for training a neural network that includes a plurality of neural network layers on training data, wherein the neural network is configured to receive time domain features of an audio sample and to process the time domain features to generate a neural network output from the audio sample, wherein the neural network comprises (i) a frequency-transform (F-T) layer that is configured to apply a transformation defined by a set of F-T layer parameters that transforms a window of time domain features into frequency domain features and (ii) one or more other neural network layers having respective layer parameters and are configured to process the frequency domain features to generate the neural network output, and wherein the operations comprise: obtaining training data comprising, for each of a plurality of training audio samples, time domain features of the training audio sample and a known output for the training audio sample; and training the neural network on the training data to adjust the values of the parameters of the other neural network layers and to adjust the values of the F-T layer parameters to define an optimal transformation from time-domain features to frequency-domain features.