Optimal Step Size Technique for Frequency Domain and Partition Block Adaptive Filters for PEM based Acoustic Feedback Cancellation

The adaptive filtering approach has been commonly used to perform acoustic feedback cancellation (AFC) in digital hearing-aids due to its reliable performance and feasibility. Because the loudspeaker and microphone are close together in hearing aids, the corresponding signals are highly correlated, resulting in biased estimation if adaptive filters are used. This problem can be addressed with the help of the decorrelation prefilter by incorporating the Prediction Error Method (PEM) technique into AFC. Frequency-Domain Adaptive Filters (FDAF) are preferable over the time-domain implementation to achieve better performance in terms of convergence and computational complexity. In addition, Partition-Block Frequency-Domain Adaptive Filters (PBFDAF) offers low processing delay. However, because of their fixed step-size, there is a trade-off between initial convergence and steady-state misalignment in the widely used frequency-domain algorithms. While Variable Step-Size (VSS) algorithms can help with this issue, VSS techniques for frequency-domain algorithms have not been extensively studied in the context of PEM-AFC. Hence, in this paper, we presented an Optimal Step-Size (OSS) technique for both the FDAF-PEM_AFC and PBFDAF-PEM_AFC algorithms to simultaneously accomplish fast convergence and minimal steady-state error. A Feedback Path Change Detector (FPCD) was also incorporated into the proposed algorithms to address the problem of convergence in non-stationary feedback paths. The results of simulations show that the proposed algorithms are clearly superior, and they are encouraging

A Systematic Algorithm for Denoising Audio Signal Using Savitzky - Golay Method

Audio signal noise reduction is a great task to acquiring noiseless sounds in the noisy environment. But, naturally the audio signals are naturally very noise, which is produced from analog or digital recorders. In this research paper, an efficient noiseless audio reduction system using Savitzky-Golay method is proposed for eliminating unwanted echoes, ripples, etc. Three different audio signals are taken for this work. First one is a human voice in the mobile conversation, which is used for telecommunication purpose. Second is one vehicle sound for usages in security applications. The last one is a music instrumental sound, which used in recording lab. Finally, the proposed system will be compared with the existing noise filters and proved their efficiency with high accuracy, high signal to noise ratio and low elapsed time

Acoustic, psychophysical, and neuroimaging measurements of the effectiveness of active cancellation during auditory functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is one of the principal neuroimaging techniques for studying human audition, but it generates an intense background sound which hinders listening performance and confounds measures of the auditory response. This paper reports the perceptual effects of an active noise control (ANC) system that operates in the electromagnetically hostile and physically compact neuroimaging environment to provide significant noise reduction, without interfering with image quality. Cancellation was first evaluated at 600 Hz, corresponding to the dominant peak in the power spectrum of the background sound and at which cancellation is maximally effective. Microphone measurements at the ear demonstrated 35 dB of acoustic attenuation [from 93 to 58 dB sound pressure level (SPL)], while masked detection thresholds improved by 20 dB (from 74 to 54 dB SPL). Considerable perceptual benefits were also obtained across other frequencies, including those corresponding to dips in the spectrum of the background sound. Cancellation also improved the statistical detection of sound-related cortical activation, especially for sounds presented at low intensities. These results confirm that ANC offers substantial benefits for fMRI research

Pre-echo detection & reduction

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.Includes bibliographical references (leaves 46-47).by Kyle K. Iwai.M.S