Classifying audio scene using synthetic image features

1. A computing system comprising:

a processor having associated memory storing instructions that cause the processor to execute, at training time, for each of a plurality of input images:

an encoder configured to receive an input image of the plurality of input images and encode the input image into real image features;

a decoder configured to receive from the encoder the real image features and decode the real image features into a reconstructed image;

a generator 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 configured to receive the real image features and first synthetic image features and to output a determination of whether a target feature is real or synthetic; and

a classifier configured to receive the second synthetic image features and classify a scene of the second audio data based on the second synthetic image features.

2. The computing system of claim 1 , 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.

3. The computing system of claim 2 , 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.

4. The computing system of claim 3 , wherein the processor is further configured to train the classifier while the encoder, decoder, generator, and discriminator are fixed.

5. The computing system of claim 1 , wherein

the first audio data corresponds to the input image in an audio-visual pair recorded together,

the second audio data is not paired with an image, and

the first audio data and the second audio data are recordings generated at substantially different geographical locations.

6. The computing system of claim 1 , wherein

the encoder, the decoder, the generator, the discriminator, and the classifier constitute an audio-visual generative adversarial network,

the encoder and the decoder include vector quantized variational autoencoder architecture, and

the classifier includes convolutional neural network (CNN) architecture.

7. The computing system of claim 1 , wherein the processor is further configured to execute, at runtime:

the generator, which is further configured to generate third synthetic image features from third audio data; and

the classifier, which is further configured to classify a scene of the third audio data based on the third synthetic image features.

8. The computing system of claim 7 , wherein the processor is further configured to, at runtime:

execute the decoder, which is further configured to receive the third synthetic image features and construct a third synthetic image from the third synthetic image features; and

display the third synthetic image as a background image of a participant in a video chat, the third synthetic image including generic features relating to the classified scene of the third audio data and lacking private identifying features of a real-world background of the participant.

9. The computing system of claim 7 , wherein the processor is further configured to use the classified scene of the third audio data as a factor in authentication of a user.

10. The computing system of claim 7 , wherein the processor is further configured to augment a navigation service based on comparing the classified scene of the third audio data to a scene of one or more known locations.

11. A method comprising, at a processor at training time of a neural network, for each of a plurality of input images:

receiving an input image of the plurality of input images and encoding the input image into real image features;

decoding the real image features into a reconstructed image;

receiving first audio data corresponding to the input image and generating first synthetic image features from the first audio data, and receiving second audio data and generating second synthetic image features from the second audio data;

outputting a determination of whether a target feature, of the real image features and first synthetic image features, is real or synthetic; and

classifying a scene of the second audio data based on the second synthetic image features.

12. The method of claim 11 , further comprising constructing a first synthetic image from the first synthetic image features and a second synthetic image from the second synthetic image features.

13. The method of claim 12 , further comprising looping through:

training an encoder and a decoder to increase a correlation of each of the reconstructed image and the first synthetic image to the respective input image;

training a generator to create the first synthetic image features, based on the determination output by a discriminator; and

training the discriminator while the encoder is fixed.

14. The method of claim 13 , further comprising training a classifier to classify the scene while the encoder, decoder, generator, and discriminator are fixed.

15. The method of claim 14 , wherein

the encoder, the decoder, the generator, the discriminator, and the classifier constitute an audio-visual generative adversarial network,

the encoder and the decoder include vector quantized variational autoencoder architecture, and

the classifier includes convolutional neural network (CNN) architecture.

16. The method of claim 11 , wherein

the first audio data corresponds to the input image in an audio-visual pair recorded together,

the second audio data is not paired with an image, and

the first audio data and the second audio data are recordings generated at substantially different geographical locations.

17. The method of claim 11 , further comprising at the processor, at runtime:

generating third synthetic image features from third audio data; and

classifying a scene of the third audio data based on the third synthetic image features.

18. The method of claim 17 , further comprising, at runtime:

constructing a third synthetic image from the third synthetic image features; and

displaying the third synthetic image as a background image of a participant in a video chat, the third synthetic image including generic features relating to the classified scene of the third audio data and lacking private identifying features of a real-world background of the participant.

19. The method of claim 17 , further comprising using the classified scene of the third audio data as a factor in authentication of a user.