Directional Detection Device for Speech Separation
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Solution Overview
Problem
Conventional electronic devices struggle to accurately separate human speech from noise in noisy environments due to limitations in microphone design and frequency range, leading to inefficient voice recognition and battery life issues in portable devices.
Innovation Solution
A method and apparatus using a directional detection system with multiple microphones and algorithms to determine the direction of an audible signal, employing cardioid and beamforming signal processing techniques to separate noise from speech across the full speech frequency range, thereby enhancing signal-to-noise ratio and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional single-microphone systems are used, then device complexity is reduced, but the ability to separate speech from noise in noisy environments deteriorates
Solution Approach 1:
The system segments the audio signal processing task by assigning different frequency ranges to different processing paths. The first signal processing technique handles lower frequency noise, while the second technique handles higher frequency noise, allowing each path to be optimized independently for its specific frequency range.
Solution Approach 2:
The system transitions from single-microphone spatial processing to multi-microphone array processing, adding spatial dimensionality. By using multiple microphones arranged in specific geometries (linear, circular, rectangular), the system creates directional sensitivity and enables spatial noise rejection through beamforming and directional processing techniques.
2Measurement precision
If the full speech frequency range (100 Hz to 8,000 Hz) is processed, then voice recognition accuracy is improved, but power consumption increases
Solution Approach 1:
The frequency spectrum is segmented into different bands, with the first signal processing technique handling lower frequencies and the second technique handling higher frequencies. This segmentation allows the system to process the full speech range while optimizing computational resources for each frequency band separately, reducing overall power consumption.
Solution Approach 2:
The system applies different levels of processing intensity to different frequency ranges based on their importance for speech recognition. By prioritizing processing for frequencies most critical to speech intelligibility while using lighter processing for less critical bands, the system maintains high recognition accuracy with reduced power consumption.
3Object-affected harmful factors
If conventional filtering methods are used to reduce noise, then noise components are removed, but speech components are also suppressed along with the noise
Solution Approach 1:
The system applies different signal processing characteristics to different frequency regions. The first signal processing technique is optimized for lower frequency noise reduction, while the second technique is optimized for higher frequency noise reduction. This localized optimization ensures that speech components in each frequency band are preserved while removing the corresponding noise components.
Solution Approach 2:
The system introduces directional processing and beamforming as intermediary steps between noise detection and noise removal. By first determining the direction of the desired speech signal and then applying directional filtering, the system can distinguish between speech and noise based on their spatial characteristics before applying frequency-based filtering, preventing speech suppression.
4Measurement precision
If multiple signal processing techniques are applied to separate noise from speech, then signal-to-noise ratio is improved, but device complexity increases
Solution Approach 1:
The signal processing pipeline is segmented into distinct stages: direction detection, frequency band separation, and targeted noise reduction. Each stage handles a specific aspect of the processing task, allowing for modular implementation and optimization. This segmentation reduces overall complexity by breaking down the complex task into manageable, independent modules.
Data Source
AI summary
Embodiments of the disclosure generally include a method and apparatus for receiving and separating unwanted external noise from an audible input received from an audible source using an audible signal processing system that contains a plurality of audible signal sensing devices that are arranged and configured to detect an audible signal that is received from any position or angle within three dimensional space. The audible signal processing system is configured to analyze the received audible signals using a first signal processing technique that is able to separate unwanted low frequency range noise from the received audible signal and a second signal processing technique that is able to separate unwanted higher frequency range noise from the received audible signal. The audible signal processing system can then combine the signals processed by the first and second signal processing techniques to form a desired audible signal that has a high signal-to-noise ratio throughout the full speech range.