Algorithm for Robust Training of Adaptive Systems Using Correntropy

Software to Improve Adaptive Systems in Noise-Cancelling Microphones

This algorithm trains adaptive systems to efficiently identify and filter undesired audio (noise) during audio capture. More than 300 million cell phones are in use in the United States right now. That accounts for only 20 percent of the world’s cell phone usage. Noise impeding cell phone use can come from a variety of sources, including machine engines, vacuum cleaners, or other people. Current noise-cancelling microphones utilize differential microphone topology, including two microphones, one closer to the audio source to identify the primary audio signal and the other identifying ambient noise. This technology often has a narrow range of filtration and does not adapt easily to a sudden change in the noise signal. Researchers at the University of Florida have developed an algorithm using a correntropy cost function to improve the accuracy and efficiency of adaptive systems in noise-cancelling microphones. This technology trains adaptive systems to recognize and adjust for real-time changes, reducing the detrimental effects of outliers and impulsive noise. The adaptive system is useful in a variety of signal processing applications including channel equalization, noise cancellation, and system modeling.

Application

Algorithm using correntropy provides robust training of adaptive systems to improve signal processing applications

Advantages

• Filters a broader range of signals, enabling devices to recognize a wider variety of interfering noises
• Continuously learns and adjusts to changes in signals, providing real-time noise filtration for a variety of sounds
• May be incorporated into existing microphone designs, reducing design costs
• Clarifies speech in any microphone, improving the quality of conversations via phone, webchat, and more

Technology

This algorithm is used for robust training of adaptive systems in noise-cancelling microphones. The adaptive system is configured to learn the parameters of the filter by using a correntropy measure between a primary input and the output of the filter. Correntropy is a measure of the similarity of two random variables within a small neighborhood. By implementing a cost function (i.e. criterion function), learning algorithm, and adaptive filter, this algorithm processes a reference signal through two separate filters. One reference filter is combined with the primary signal to identify the desired signal, which is compared to the second reference filter and yields a cost function signal. This is used to make adjustments to the adaptive filter to optimize the desired signal, eliminating unwanted noise.