A computing system comprising: a processor having associated memory (14, 16) storing instructions that cause the processor to execute, at training time of a neural network, for each of a plurality of input images: an encoder of the neural network, configured to receive an input image of the plurality of input images and encode the input image into real image features; a decoder of the neural network, configured to receive from the encoder the real image features and decode the real image features into a reconstructed image; a generator of the neural network, configured to receive first audio data corresponding to the input image and generate first synthetic image features from the first audio data, and to receive second audio data and generate second synthetic image features from the second audio data; a discriminator of the neural network, configured to receive the real image features and first synthetic image features and to output a determination of whether a target feature of the real image features and the first synthetic image features, currently processed by the discriminator, is real or synthetic; and the classifier of the neural network, configured to receive the second synthetic image features and classify a scene of the second audio data based on the second synthetic image features; wherein the decoder is further configured to construct a first synthetic image from the first synthetic image features and a second synthetic image from the second synthetic image features; wherein the processor is further configured to loop through: training the encoder and the decoder to increase a correlation of each of the reconstructed image and the first synthetic image to the respective input image; training the generator, based on the determination output by the discriminator; and training the discriminator while the encoder is fixed; and wherein the processor is further configured to train the classifier while the encoder, decoder, generator, and discriminator are fixed. a processor having associated memory (14, 16) storing instructions that cause the processor to execute, at training time of a neural network, for each of a plurality of input images: an encoder of the neural network, configured to receive an input image of the plurality of input images and encode the input image into real image features; a decoder of the neural network, configured to receive from the encoder the real image features and decode the real image features into a reconstructed image; a generator of the neural network, configured to receive first audio data corresponding to the input image and generate first synthetic image features from the first audio data, and to receive second audio data and generate second synthetic image features from the second audio data; a discriminator of the neural network, configured to receive the real image features and first synthetic image features and to output a determination of whether a target feature of the real image features and the first synthetic image features, currently processed by the discriminator, is real or synthetic; and the classifier of the neural network, configured to receive the second synthetic image features and classify a scene of the second audio data based on the second synthetic image features; an encoder of the neural network, configured to receive an input image of the plurality of input images and encode the input image into real image features; a decoder of the neural network, configured to receive from the encoder the real image features and decode the real image features into a reconstructed image; a generator of the neural network, configured to receive first audio data corresponding to the input image and generate first synthetic image features from the first audio data, and to receive second audio data and generate second synthetic image features from the second audio data; a discriminator of the neural network, configured to receive the real image features and first synthetic image features and to output a determination of whether a target feature of the real image features and the first synthetic image features, currently processed by the discriminator, is real or synthetic; and the classifier of the neural network, configured to receive the second synthetic image features and classify a scene of the second audio data based on the second synthetic image features; wherein the decoder is further configured to construct a first synthetic image from the first synthetic image features and a second synthetic image from the second synthetic image features; wherein the processor is further configured to loop through: training the encoder and the decoder to increase a correlation of each of the reconstructed image and the first synthetic image to the respective input image; training the generator, based on the determination output by the discriminator; and training the discriminator while the encoder is fixed; and wherein the processor is further configured to train the classifier while the encoder, decoder, generator, and discriminator are fixed. training the encoder and the decoder to increase a correlation of each of the reconstructed image and the first synthetic image to the respective input image; training the generator, based on the determination output by the discriminator; and training the discriminator while the encoder is fixed; and wherein the processor is further configured to train the classifier while the encoder, decoder, generator, and discriminator are fixed.