Neural Network Audio Environment Description Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for automatically describing environments rely heavily on visual cues, which are insufficient for effective object detection using auditory cues.
Innovation Solution
A method for training a neural network to describe environments based on audio signals, using simultaneous audio and image training signals, and comparing the network's output with target descriptions to refine its performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual cues are used for environment description, then object detection capability is improved, but reliance on auditory cues remains insufficient
Solution Approach 1:
The patent combines visual and auditory data streams into a unified training framework. The neural network simultaneously processes image data and audio signals to learn correlated features, enabling the system to leverage both visual and auditory cues for environment description. This merging allows the model to achieve robust object detection while adapting to conditions where one modality may be insufficient.
Solution Approach 2:
The trained neural network achieves multi-functionality by being capable of both visual-based object detection and auditory-based environment description. The model learns a unified representation that can be applied across different sensing modalities, making the system versatile for various detection scenarios including those relying primarily on auditory cues.
2Device complexity
If audio signals alone are used to describe environment, then system complexity is reduced, but detection precision deteriorates
Solution Approach 1:
The patent employs preliminary action by pre-training the neural network using both visual and auditory data before deployment. During this training phase, the model learns the correlations between visual scenes and corresponding audio signals. Once trained, the network can operate using audio signals alone, achieving reasonable detection precision without requiring real-time visual input, thus reducing operational system complexity while maintaining acceptable performance.
3Measurement precision
If multiple sound acquisition devices are used, then spatial detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent addresses spatial detection by incorporating temporal and spectral dimensions into the audio processing pipeline. Instead of simply adding more spatial devices, the model processes audio signals across multiple time steps and frequency bands, extracting spatial information through temporal correlations and spectral analysis. This approach improves spatial detection accuracy while avoiding the complexity increase associated with adding multiple physical sound acquisition devices.
Data Source
AI summary
A neural network, a system using this neural network and a method for training a neural network to output a description of the environment in the vicinity of at least one sound acquisition device on the basis of an audio signal acquired by the sound acquisition device, the method including: obtaining audio and image training signals of a scene showing an environment with objects generating sounds, obtaining a target description of the environment seen on the image training signal, inputting the audio training signal to the neural network so that the neural network outputs a training description of the environment, and comparing the target description of the environment with the training description of the environment.