Multi-Buffer Signal Processing for Time-Frequency Resolution
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Solution Overview
Problem
Conventional time-series signal analysis techniques face a trade-off between time and frequency analysis resolution due to Heisenberg's uncertainty principle, and the selection of window width and function requires specialized knowledge.
Innovation Solution
A signal processing apparatus that models a neural cell network to extract sound features at different time scales, utilizing an AI model to perform both time and frequency domain analysis by emulating the human auditory neural system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional STFT with fixed window width is used for frequency analysis, then frequency resolution is improved, but time resolution deteriorates
Solution Approach 1:
The patent segments the time-series signal into multiple segments with different buffering times (e.g., 10ms, 5ms, 1ms). Each segment is processed separately through the neural network, allowing the system to capture both long-term frequency patterns and short-term temporal changes simultaneously, thus resolving the trade-off between frequency and time resolution
Solution Approach 2:
The patent transforms the 1D time-series signal into a 2D representation by creating multiple frequency spectra at different buffering times. This multi-dimensional approach allows the neural network to analyze both frequency composition and temporal evolution concurrently, overcoming the limitations of conventional single-window STFT
2Device complexity
If conventional STFT with fixed window function is used, then analysis simplicity is maintained, but adaptability to different signal types deteriorates
Solution Approach 1:
The patent employs dynamic buffering times that can be adjusted based on the characteristics of the input signal. The system processes signals at multiple buffering times (e.g., 10ms for speech, 1ms for music), allowing adaptive optimization for different signal types without requiring manual window function selection, thus maintaining simplicity while improving versatility
Solution Approach 2:
The patent creates multiple copies of the signal processing pipeline, each operating with different buffering times. These parallel processing paths are then integrated by the neural network, enabling the system to adapt to various signal types through multiple pre-configured processing modes rather than requiring complex adaptive algorithms
Data Source
AI summary
A signal processing apparatus includes: an acceptance unit that accepts a time-series signal; an information acquisition unit that, for each buffering time of two or more buffering times, acquires information having a time length corresponding to the buffering time from the time-series signal; a frequency conversion unit that, for each buffering time of the two or more buffering times, performs frequency conversion on the information acquired by the information acquisition unit to acquire an image; a signal transmission unit that, for each buffering time of the two or more buffering times, passes the image acquired by the frequency conversion unit to a neural network, and acquires output information that is based on a signal output from the neural network; and an information output unit that outputs the output information.