Wearable Audio Device Sound Identification Module
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Solution Overview
Problem
Current wearable audio devices lack effective methods to differentiate between the wearer's speech and external speech, and to adapt settings based on environmental sounds, leading to suboptimal noise cancellation and user experience during conversations or in various environments.
Innovation Solution
Incorporating a sound identification module with multiple microphones and classifiers, such as neural networks or hidden Markov models, to distinguish between the wearer's speech and external speech, and adjust settings like noise cancellation or music playback based on detected sounds, including environmental characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If active noise cancellation is continuously applied, then noise reduction is improved, but speech communication with external environment deteriorates
Solution Approach 1:
The noise cancellation system dynamically adjusts its operation based on detected speech conditions. When external speech is detected, the system reduces or disables noise cancellation to allow natural communication. When no external speech is present, full noise cancellation is applied for optimal noise reduction.
Solution Approach 2:
The system uses microphones to continuously monitor the acoustic environment and detect external speech. This feedback mechanism triggers automatic adjustment of the noise cancellation settings, creating a closed-loop control system that adapts to communication needs.
2Measurement precision
If sound identification module is always active, then speech differentiation capability is improved, but power consumption increases
Solution Approach 1:
The sound identification module operates periodically rather than continuously. It activates at intervals to analyze the acoustic environment and detect speech patterns, then enters a low-power state between activations. This periodic operation maintains speech differentiation capability while significantly reducing average power consumption.
Solution Approach 2:
The system uses the existing audio input signals already being processed by the wearable device for additional speech analysis. By leveraging the same microphone inputs and processing pipelines, the sound identification module avoids duplicating hardware and reduces overall system power requirements.
3Measurement precision
If multiple microphones and classifiers are added, then speech detection accuracy is improved, but device complexity increases
Solution Approach 1:
The microphone array serves multiple functions: primary audio input for media playback, active noise cancellation, and speech detection for environment adaptation. The same hardware components are reused across different functions, reducing the need for additional dedicated sensors and minimizing overall device complexity.
Solution Approach 2:
The speech detection functionality is integrated into the existing audio processing pipeline of the wearable device. The sound identification module shares processing resources with other audio functions, and the classifier algorithms are implemented within the device's existing processor architecture, avoiding the need for separate dedicated processing units.
Data Source
AI summary
Broadly speaking, embodiments of the present invention provide a wearable audio device including one or a plurality of microphones, a sound recognition systems and a controller to control the device based on one or more recognized sounds or classes of sound. Embodiments use stored sound models.