MIMO Audio De-reverberation Using Adaptive LMS Filter
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Solution Overview
Problem
Existing reverberation reduction methods for audio signals in noisy environments are computationally complex and memory-intensive, making them impractical for real-time use in applications like speech recognition and VoIP, and they often fail to produce the same number of de-reverberated signals as microphones in an array, limiting their effectiveness.
Innovation Solution
The implementation of an adaptive Least Mean Squares (LMS) filter with a frequency-dependent adaptive step size and voice activity detection, which transforms multichannel audio signals into the subband frequency domain, estimates a prediction filter online, and applies linear and nonlinear filtering to reduce reverberation while maintaining low memory consumption and real-time processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional de-reverberation methods are used, then reverberation reduction is achieved, but computational complexity and memory consumption increase, making them impractical for real-time use
Solution Approach 1:
The patent segments the audio signal processing into multiple independent frequency bins, where each bin is processed separately through the de-reverberation algorithm. This division allows parallel processing of frequency bins, reducing the computational burden on the main processor and enabling real-time operation while maintaining effective reverberation reduction across the entire audio spectrum
Solution Approach 2:
The patent transforms the audio signal from the time domain to the frequency domain using Fourier transforms, changing the parameter space in which processing occurs. This transformation enables the de-reverberation algorithm to operate more efficiently by working with frequency-based parameters rather than time-domain samples, reducing computational complexity while preserving signal integrity
2Reliability
If existing de-reverberation methods are applied, then some reverberation reduction is achieved, but processing speed is too slow for real-time applications
Solution Approach 1:
By segmenting the signal into frequency bins that can be processed independently and in parallel, the patent achieves faster overall processing throughput while maintaining the quality of reverberation reduction. Each frequency bin undergoes the same de-reverberation process simultaneously, multiplying the effective processing speed without sacrificing accuracy
Solution Approach 2:
The patent implements a periodic processing scheme where audio samples are continuously transformed, processed, and transformed back in regular intervals. This periodic action enables real-time processing by maintaining a steady pipeline of operations, ensuring that processing speed meets the requirements for live audio applications
3Reliability
If microphone arrays are used to improve signal processing performance, then more de-reverberated signals are needed, but existing methods do not produce the same number of outputs as microphones
Solution Approach 1:
The patent designs a universal de-reverberation system that processes signals from any number of microphones and produces an equal number of de-reverberated output signals. The algorithm is configured to accept multichannel input and generate corresponding multichannel output, making it adaptable to various microphone array configurations and enabling its use in MIMO applications for tasks such as sound source localization and beam forming
Data Source
AI summary
Audio signal processing for adaptive de-reverberation uses a least mean squares (LMS) filter that has improved convergence over conventional LMS filters, making embodiments practical for reducing the effects of reverberation for use in many portable and embedded devices, such as smartphones, tablets, laptops, and hearing aids, for applications such as speech recognition and audio communication in general. The LMS filter employs a frequency-dependent adaptive step size to speed up the convergence of the predictive filter process, requiring fewer computational steps compared to a conventional LMS filter applied to the same inputs. The improved convergence is achieved at low memory consumption cost. Controlling the updates of the prediction filter in a high non-stationary condition of the acoustic channel improves the performance under such conditions. The techniques are suitable for single or multiple channels and are applicable to microphone array processing.