Context-Aware Audio Processing for Noise Suppression
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Solution Overview
Problem
Existing technologies face challenges in effectively processing user-generated content (UGC) audio, particularly in handling different sound types in various environments while maintaining the creative intent of content creators.
Innovation Solution
A context-aware audio processing system that utilizes a combination of sensors, machine learning models, and audio analysis techniques to identify the recording context and adjust processing parameters accordingly, thereby enhancing audio quality while preserving the original content characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional audio signal analysis or AI-based noise reduction is applied to UGC, then noise can be reduced, but the creative intent of content creators may be lost and different sound types in different environments cannot be effectively handled
Solution Approach 1:
The patent implements dynamic adaptation by using machine learning models that automatically adjust audio processing parameters based on the detected recording environment and sound type. The system dynamically selects from multiple processing profiles (e.g., indoor, outdoor, windy, noisy) to optimize noise reduction while preserving creative intent for each specific context.
Solution Approach 2:
The patent changes processing parameters based on environmental context by analyzing sensor data (accelerometer, gyroscope, microphone inputs) to determine recording conditions, then selecting appropriate audio processing profiles that adjust parameters such as noise reduction intensity, equalization, and compression to match the detected environment and sound type.
2Object-affected harmful factors
If aggressive noise reduction is applied to enhance audio quality, then listening experience improves, but the original content characteristics and creative intent are compromised
Solution Approach 1:
The patent applies partial noise reduction by selectively processing only the noise components identified through spectral analysis and machine learning, rather than uniformly processing the entire audio signal. This allows aggressive noise reduction targeted at specific frequency ranges and time segments where noise is present, while leaving the creative content untouched.
Solution Approach 2:
The patent uses feedback mechanisms where the audio processing system continuously monitors the processed output and compares it with the original input, adjusting the degree of noise reduction in real-time to preserve original content characteristics. The machine learning models are trained with feedback loops that minimize distortion of creative intent while maximizing noise removal.
Data Source
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AI summary
Embodiments are disclosed for context aware audio processing. In an embodiment, an audio processing method comprises: receiving, with one or more sensors of a device, environment information about an audio recording captured by the device; detecting, with at least one processor of the device, a context of the audio recording based on the audio recording and the environment information; determining, with the at least one processor, a model based on the context; processing, with the at least one processor, the audio recording based on the model to produce a processed audio recording with suppressed noise; determining, with the at least one processor, an audio processing profile based on the context; and combining, with the at least one processor, the audio recording and the processed audio recording based on the audio processing profile.