Cross-Modal Signal Generation Using Shared Audio-Visual Codebooks
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Solution Overview
Problem
Existing technologies struggle to effectively translate audio signals to visual images and vice versa due to the different modalities of information, such as spatial and temporal information in images, and acoustic information in audio, which cannot be represented by single-channel recordings.
Innovation Solution
A deep neural network, specifically a Siamese Variational Auto-Encoder (SVAE) network, is trained using a large database of videos to learn the correlation between audio and visual signals, creating separate codebooks for visual, audio, and correlation embeddings, enabling bi-directional translation of signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If audio signals are translated to visual images using existing technologies, then translation capability is achieved, but the quality and realism of the generated images are insufficient due to modality differences
Solution Approach 1:
The patent introduces a latent space as an intermediary representation that bridges audio and visual modalities. The audio-to-image translation model encodes audio signals into latent representations, which then serve as intermediate inputs for generating visual images. This latent space acts as a mediator that handles the modality transformation, allowing the system to overcome the direct incompatibility between audio and visual data structures while maintaining translation capability and improving image quality through learned correlations.
2Reliability
If visual images are translated to audio signals, then cross-modality translation is achieved, but temporal information representation is insufficient
Solution Approach 1:
The patent transforms the temporal audio signal into a different dimensional representation in the latent space, which can then be mapped to visual spatial information. The audio-to-image model processes temporal sequences by encoding them into latent vectors that capture temporal patterns, then uses these representations to generate spatial visual images. This dimensional transformation allows temporal information to be preserved and represented in a form suitable for visual generation, overcoming the limitation of representing temporal data in a single frame.
3Device complexity
If a single-channel audio recording is used to represent spatial and color information, then simplicity is maintained, but information representation capability is insufficient
Solution Approach 1:
The patent fundamentally changes the parameters and dimensions of the audio representation by transforming single-channel audio signals into multi-dimensional latent space representations. The audio encoding process converts monophonic temporal signals into rich, multi-parameter latent vectors that capture spectral, temporal, and spatial characteristics. This parameter transformation enables the system to represent spatial and color information that would be impossible to encode in a single-channel recording, while the learned mappings ensure that the transformation preserves essential information.
4Device complexity
If a single frame image is used to represent temporal audio information, then simplicity is maintained, but temporal dynamics are lost
Solution Approach 1:
The patent applies preliminary encoding actions to capture temporal information before the final image generation. The audio encoding process performs preliminary extraction of temporal patterns, rhythmic structures, and dynamic characteristics, storing this information in the latent representation. This preliminary action ensures that temporal information is preserved and encoded into the latent space before being used to generate the visual image, preventing information loss that would occur if temporal dynamics were simply discarded for simplicity.
Data Source
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AI summary
Training a neural network for creating an output signal of different modality from an input signal is described. A first modality is a sound signal or a visual image and where the output signal is a visual image or a sound signal, respectively. In embodiments a model is trained using a pair of visual and audio networks to train a set of codebooks using known visual and audio signals and using a second pair of visual and audio networks to further train the set of codebooks using the augmented visual signals and the augmented audio signals. The visual and audio networks may be equally weighted, respectively. In aspects of the present disclosure, the set of codebooks comprise a visual codebook, an audio codebook and a correlation codebook, which are then used to create a visual image from a sound signal and/or a sound signal from a visual image.