Ambient Noise Compensation for Automatic Audio Volume Control
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Solution Overview
Problem
Existing automatic volume control systems for audio content in listening areas, such as living rooms, fail to differentiate between human conversation and ambient noise, leading to inadequate adjustments in audio output volume, and are not suitable for dynamic acoustic environments.
Innovation Solution
An automatic volume control apparatus and method using a processor with a microphone and stored profiles, employing deep learning algorithms to analyze sound inputs and adjust audio content volume based on whether the input includes noise or an ambient voice, automatically raising or lowering the volume to maintain optimal listening conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automatic volume control systems are used, then volume adjustment is provided, but the system cannot differentiate between human conversation and ambient noise
Solution Approach 1:
A deep learning-based sound classification model is introduced as an intermediary between the microphone input and volume control decision. This model processes the raw sound signal and outputs a classified result (conversation or noise), enabling accurate differentiation without requiring complex manual feature engineering or multiple sensors.
Solution Approach 2:
The patent replaces traditional mechanical or electronic sound analysis methods with a data-driven deep learning approach. Instead of using fixed thresholds or simple frequency analysis, the system employs a trained neural network model that automatically learns discriminative features from audio data, achieving superior classification accuracy.
2Ease of operation
If manual volume adjustment is required for different acoustic conditions, then volume control is available, but user convenience is reduced
Solution Approach 1:
The system performs self-service by automatically detecting the acoustic environment, classifying the sound type, and adjusting the volume without user intervention. The deep learning model enables the system to autonomously determine whether to increase volume (for noise) or decrease volume (for conversation), making the device adapt to user needs without requiring manual control.
Solution Approach 2:
The system implements a feedback loop where the microphone continuously monitors the acoustic environment, the deep learning model processes the sound input, and the volume control adjusts based on the classification result. This closed-loop feedback mechanism ensures automatic adaptation to changing acoustic conditions while maintaining ease of operation.
3Adaptability or versatility
If simple noise detection is used, then volume adjustment is provided, but the system is not suitable for dynamic acoustic environments
Solution Approach 1:
The system dynamically adapts to changing acoustic environments by continuously processing sound inputs through the deep learning model. Unlike static systems with fixed thresholds, the deep learning approach can handle varying sound levels, frequencies, and patterns, enabling the system to maintain accurate classification and appropriate volume control in dynamic conditions.
Solution Approach 2:
The deep learning model automatically adjusts its internal parameters and decision boundaries based on the characteristics of the input sound. This allows the system to differentiate between conversation and noise across various acoustic conditions, including different volumes, frequencies, and background environments, thereby achieving both adaptability and precision.
Data Source
AI summary
An electronic device and method that automatically adjusts an audio output volume level based on a live environmental acoustic scenario input via a microphone using a machine learning algorithm trained with Human Activity Recognition (HAR). Equipped with such an intelligence the electronic device classifies ambient sounds occurring in the environment of the listening area in which the device is situated into different acoustic scenario mappings such a voice or conversation, for an ambient human conversation detected event, and noise, such as for example a vacuum cleaner or dish washer noise detected event, and automatically adjust the audio output volume accordingly.