Acoustic Ambience Classification for Robust Speech Recognition
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Solution Overview
Problem
Background noise and adverse weather conditions can adversely affect the operation of computing systems, particularly automated speech recognition systems, in various environments.
Innovation Solution
An audio processing system that utilizes microphones, environmental sensors, and user input to analyze and classify acoustic ambience, adjusting the operation of electronic systems like speech recognition and media players based on sound and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated speech recognition systems operate in environments with background noise and adverse weather conditions, then the system can function in various physical environments, but the recognition accuracy and performance deteriorate
Solution Approach 1:
The system segments the audio signal into different components (speech signal, background noise, weather noise) and processes each separately. By dividing the complex acoustic environment into manageable segments, the system can apply specific noise reduction techniques to each component while preserving the speech signal quality.
Solution Approach 2:
The patent introduces an intermediary processing layer (noise reduction module) between the microphone input and speech recognition system. This intermediary component filters and cleans the audio signal by identifying and removing background noise and weather-related sounds before the speech recognition engine processes the cleaned signal.
2Reliability
If the system processes and classifies acoustic ambience in real-time, then the operation can be adjusted to improve performance, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary classification of acoustic environments using pre-defined categories and templates. By preparing classification models and noise profiles in advance, the real-time processing only requires matching the current audio signal against these pre-prepared categories, significantly reducing computational complexity during actual operation.
Solution Approach 2:
The noise reduction system automatically adjusts its parameters and settings based on the detected acoustic environment without requiring manual intervention. The system self-calibrates by analyzing the audio signal characteristics and automatically selecting appropriate noise reduction strategies, reducing the need for complex external control mechanisms.
Data Source
AI summary
Methods and systems for modification of electronic system operation based on acoustic ambience classification are presented. In an example method, at least one audio signal present in a physical environment of a user is detected. The at least one audio signal is analyzed to extract at least one audio feature from the audio signal. The audio signal is classified based on the audio feature to produce at least one classification of the audio signal. Operation of an electronic system interacting with the user in the physical environment is modified based on the classification of the audio signal.