Sound Model Generation via Feature Concatenation
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Solution Overview
Problem
Existing sound model generation techniques require human expertise to determine suitable features for learning, making the process skill-intensive and knowledge-dependent.
Innovation Solution
A sound model generation device and method that concatenate multiple features of a sound signal to generate a concatenated feature, which is then learned to distinguish sound events, using a concatenating unit and a learning unit for machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used to determine suitable features for sound model learning, then the quality and accuracy of the sound model can be improved, but the complexity and skill requirement of the process increases
Solution Approach 1:
The system automatically determines suitable features for sound model learning by having the sound model generation device itself perform feature selection and concatenation based on the training data, eliminating the need for human expertise in feature determination. The device autonomously extracts and processes multiple features from sound signals, concatenates them, and generates the sound model without requiring external human intervention or specialized knowledge.
2Measurement precision
If multiple features are concatenated to improve sound model performance, then the distinction capability of the sound model is enhanced, but the processing complexity increases
Solution Approach 1:
The patent combines multiple features extracted from sound signals by concatenating them into a single comprehensive feature set. The sound model generation device extracts various features (such as spectral features, temporal features, and other acoustic characteristics) and merges them through concatenation to form an enhanced input for the sound model, thereby improving distinction capability while managing processing complexity through systematic feature integration.
Data Source
AI summary
Provided are a sound model generation device and the like that make it possible to more easily generate a sound model capable of distinguishing sound events using a plurality of features. A concatenating unit concatenates a plurality of features of sound signals that are learning data and generates a concatenated feature. A learning unit learns the generated concatenated feature for generating a sound model for distinguishing a sound event from a sound signal.