Audio Environment Recognition for Adaptive Noise Filtering
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Solution Overview
Problem
Conventional computing devices face challenges in accurately filtering background noise and determining their current location due to varying environmental sounds, which can lead to inaccurate location identification and speech recognition errors.
Innovation Solution
The device captures audio data using microphones and compares it with audio models to identify the current environment, allowing for refined location adjustment, classification, and customized noise filtering for improved speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional noise filtering is applied, then some background noise is reduced, but intended audio input is inadvertently filtered causing speech recognition errors
Solution Approach 1:
The system changes the parameters of noise filtering by identifying the current environment type (e.g., restaurant, airport, café) and applying environment-specific filtering parameters. Different environments have different acoustic characteristics, so the filtering parameters are adjusted accordingly to preserve intended speech while removing appropriate background noise for each environment type.
Solution Approach 2:
The noise filtering system transitions from a static, one-size-fits-all approach to a dynamic system that adapts to different environments. The system continuously identifies the current environment type and adjusts filtering parameters in real-time, making the filtering process adaptive and context-aware rather than fixed.
2Device complexity
If generic noise filtering is used across all environments, then processing is simple, but location identification accuracy deteriorates due to varying environmental sounds
Solution Approach 1:
The system performs preliminary environment identification before applying noise filtering or location determination. By first capturing audio data and identifying the environment type (restaurant, airport, café, etc.), the system prepares the appropriate context-specific parameters for subsequent processing, enabling more accurate location identification and speech recognition.
Solution Approach 2:
The system applies different filtering and processing qualities to different environments. Instead of a uniform approach, each environment type receives customized processing parameters tailored to its acoustic characteristics, improving overall accuracy while maintaining manageable complexity through systematic categorization.
3Reliability
If environment-specific noise filtering is applied, then speech recognition accuracy improves, but device complexity increases due to multiple filtering techniques
Solution Approach 1:
The system segments the noise filtering process by dividing it into environment-specific modules. Each environment type (restaurant, airport, café, etc.) has its own dedicated filtering parameters and processing logic. This segmentation allows the system to manage complexity through modular organization while achieving high accuracy for each specific environment.
Solution Approach 2:
The environment identification system serves multiple functions: it not only identifies the current location type but also automatically selects the appropriate noise filtering parameters, speech recognition settings, and audio processing configurations. This multi-functionality reduces overall system complexity by using a single identification mechanism to drive multiple adaptive processes.
Data Source
AI summary
A computing device can capture audio data representative of audio content present in a current environment. The captured audio data can be compared with audio models to locate a matching audio model. The matching audio model can be associated with an environment. The current environment can be identified based on the environment associated with the matching audio model. In some embodiments, information about the identified current environment can be provided to at least one application executing on the computing device. The at least one the application can be configured to adjust at least one functional aspect based at least in part upon the determined current environment. In some embodiments, one or more computing tasks performed by the computing device can be improved based on information relating to the identified current environment. These computing tasks can include location refinement, location classification, and speech recognition.