Voice Signal Isolation Using High-Frequency Attack Release Control
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Solution Overview
Problem
Existing audio technologies face challenges in effectively isolating and enhancing speech signals in noisy environments, particularly in crowded spaces, where multiple speakers are present, due to limitations in directional microphones and voice print identification systems.
Innovation Solution
The system analyzes audio signals by separating frequency bands, enhancing speech signals within the vocal range (300 Hz to 3400 Hz), and extracting a unique voice profile key for biometric identification, while filtering out noise and adjusting signal levels based on environmental conditions and device modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If directional microphones are employed to focus on an individual speaker, then speech signal isolation is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting audio processing parameters including attack and release times, frequency band gains, and compression ratios based on detected sound event characteristics. This allows the system to adapt to different acoustic environments and speech patterns without requiring complex hardware configurations, thereby improving speech isolation while maintaining system simplicity.
2Adaptability or versatility
If voice print identification is performed in crowded spaces with multiple speakers, then biometric identification capability is tested, but identification accuracy deteriorates due to background noise and multiple speakers
Solution Approach 1:
The patent segments the audio spectrum into multiple frequency bands and processes each band independently with customized compression ratios and attack/release times. This segmentation allows the system to isolate speech signals from background noise and multiple speakers by focusing on specific frequency ranges where human speech predominates, thereby improving identification accuracy in crowded environments.
Solution Approach 2:
The system dynamically adjusts processing parameters based on real-time analysis of sound events, including detecting speech presence, estimating background noise levels, and adapting compression ratios and attack/release times accordingly. This dynamic adaptation enables the system to maintain high identification accuracy across varying acoustic conditions and speaker configurations.
3Extent of automation
If audio signals are processed to extract voice profile keys, then biometric identification is enabled, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on processing only the frequency bands and time segments where speech signals are detected, rather than processing the entire audio spectrum continuously. The system uses voice activity detection to identify relevant segments and applies compression and parameter adjustment only to those segments, reducing overall processing time while maintaining identification accuracy.
Data Source
AI summary
Systems and methods for isolating audio content and biometric authentication include receiving, with an audio receiver, an audio signal spanning a plurality of frequency bands, identifying a speech signal carried by a voice frequency band selected from the plurality of frequency bands, enhancing the speech signal relative to other audio content within the audio signal, and extracting a voice profile key that uniquely identifies the speech signal, wherein enhancing the first speech signal comprises adjusting attack and release times of the speech signal based on sound events within the speech signal, the attack time being associated with very high frequency sounds that are not phase-shifted.