Acoustic Signal Parameter Prediction via Continuous Environmental Modeling
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Solution Overview
Problem
Existing techniques for acoustic signal processing require extensive time and effort to prepare optimal control parameter sets for various environments and applications, with reduced accuracy near environmental group boundaries due to discrete classification.
Innovation Solution
A parameter prediction device that acquires environmental characteristics and target evaluation values to input into a prediction model, predicting a control parameter set suitable for both environment and application, reducing the need for pre-prepared parameter sets and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control parameter sets are prepared for each environmental group through classification, then the performance of acoustic signal processing is improved, but a great amount of time is taken for preparation of control parameter sets
Solution Approach 1:
The patent changes the fundamental parameter representation from discrete environmental group classifications to continuous environmental characteristic quantities. By using prediction models that take environmental characteristics (such as noise level, reverberation time, distance) as inputs and directly output optimal control parameters, the system eliminates the time-consuming process of manual parameter preparation for each group while maintaining processing performance. This continuous parameter approach allows for seamless adaptation without discrete boundaries.
2Measurement precision
If control parameter sets are prepared for each environmental group, then optimization for specific environments is achieved, but it is difficult to predict an optimal control parameter set in a sound collection environment corresponding to a vicinity of a boundary between groups
Solution Approach 1:
The patent implements a dynamic prediction model that continuously adapts to environmental changes without fixed boundaries. Instead of static parameter sets assigned to discrete groups, the system uses prediction models that dynamically calculate optimal parameters based on real-time environmental characteristics. This allows smooth transitions and optimal performance even in boundary regions where environmental conditions fall between traditional group classifications.
3Device complexity
If discrete environmental groups are used for classification, then control parameter optimization is simplified, but prediction accuracy is reduced in boundary regions between groups
Solution Approach 1:
The patent transitions from a zero-dimensional discrete classification system to a multi-dimensional continuous parameter space. By representing environmental conditions as continuous variables (noise level in dB, reverberation time in seconds, distance in meters) rather than discrete group labels, the system maintains simplicity while dramatically improving accuracy in boundary regions. The prediction models operate in this continuous space, naturally handling boundary cases without loss of precision.
Data Source
AI summary
A parameter prediction device includes: an environmental characteristic acquirer that acquires an environmental characteristic quantity set which quantifies one or more characteristics of a sound collection environment for an acoustic signal; a target setter that sets a target evaluation value set which provides one or more values obtained by quantifying one or more performances of processing of the acoustic signal, or one or more evaluation values of a processed acoustic signal; and a first predictor that inputs the environmental characteristic quantity set and the target evaluation value set as independent variables to a first prediction model, and predicts a control parameter set for controlling the acoustic signal processing.