Sound Signal Explanation Generation Using Contrast Learning
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Solution Overview
Problem
Existing methods struggle to accurately generate character strings explaining changes in non-localized signals, such as sound or vibration of equipment, due to the lack of clear focus on specific changes, leading to inadequate training data and inaccurate explanations.
Innovation Solution
A generation device and method that includes a storage unit, signal encoding, language encoding, and updating units to learn and generate explanatory sentences using contrast learning, emphasizing onomatopoeias to highlight changes in sound signals, utilizing neural networks for feature extraction and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general sound signals are analyzed without focusing on specific changes, then comprehensive signal coverage is achieved, but the complexity of identifying and explaining changes increases significantly
Solution Approach 1:
The patent segments the sound signal analysis by dividing the spectrum into multiple frequency bands and analyzing temporal patterns in segments. This allows comprehensive coverage of different signal characteristics while managing complexity through structured analysis of discrete portions rather than the entire signal at once.
Solution Approach 2:
The patent applies local quality by identifying and emphasizing specific local characteristics of sound changes, such as particular frequency bands or temporal patterns that indicate abnormalities. This focuses the explanation on locally significant features rather than treating the entire signal uniformly, making change identification more manageable.
2Measurement precision
If manual annotation of sound changes is performed, then accurate training data can be created, but the time and labor required increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate explanations of sound changes using the trained generative model. The system processes sound signals and produces natural language explanations autonomously without requiring manual annotation for each new case, thereby maintaining accuracy while significantly reducing time consumption.
Solution Approach 2:
The patent uses feedback mechanisms where the generative model is trained on initially annotated data, then generates explanations that can be used to create training data for subsequent models. This iterative feedback process improves accuracy over time while reducing the need for manual annotation of each individual case.
3Loss of information
If detailed explanation of sound changes is generated, then useful inspection clues are provided, but the complexity of processing and interpreting results increases
Solution Approach 1:
The patent applies parameter changes by transforming the sound signal into different feature representations (spectral, temporal, and combined features) during processing. This allows detailed information to be extracted and explained while managing processing complexity through efficient feature transformation and selection rather than brute-force analysis.
Data Source
AI summary
A generation device includes: a storage unit that stores a set of training data sets each being a combination of a sound signal indicating a state and an explanatory sentence explaining the state in a character string; a signal encoding unit configured to encode, based on a first learning parameter, the sound signal to generate a sound feature vector; a language encoding unit configured to encode, based on a second learning parameter, the explanatory sentence to generate a language feature vector; a language decoding unit configured to decode, based on a third learning parameter, the sound feature vector into a text indicating the state; and an updating unit configured to update the first and second learning parameters by contrast learning using a combination of sound feature and language feature vectors, and updates the third learning parameter based on a difference between the explanatory sentence and the decoded text.