Speech enhancement based on adaptive filter with variable step size for wideband and periodic noise

Adaptive speech enhancement using directional microphone in a 4-T MRI scanner

“…Moreover, the application of differential directional microphones enhances the signal-to-noise ratio of the reference signal. The novel processing algorithm proposed in our ASE system demonstrates the improved performance as compared to previous studies [21][22][23][24][25]29]. While other approaches reported in the previous work [21] required that .…”

“…In addition, the unique differential directional microphone structure in the adaptive algorithm guarantees a robust and enhanced speech recovering due to its ability of separation the reference and desired signals. The proposed control scheme is very promising for speech enhancement in MRI environment and yields a unique performance as compared to methods adopted by other researchers [21,29].…”

“…However, this traditional single-channel approach is not inherently robust since the speech signal cannot be totally de-correlated and can be distorted by the adaptive filter. Therefore, several researchers proposed a multi-channel ASE system to tackle this issue [21][22][23][24][25]29]. In their approaches, an additional reference microphone or an array of microphones was applied to pick up the speech signal and MRI noise separately.…”

LMS Adaptive Filters for Noise Cancellation: A Review

“…The application of this technique can be found in various industrial and communication appliances, such as machineries, hands-free phones and transformers [3,4]. Additionally, noise cancellation has also been implemented in the field of image processing, biomedical signal, speech enhancement and echo cancellation [5][6][7]. As the noise from the surrounding environment severely reduces the quality of speech and audio signals it is quite necessary to suppress noise and enhance speech and audio signal quality, hence the acoustics applications of noise cancellation has become the thrust area of research.…”

A negentropy based adaptive line enhancer for single-channel noise reduction at low SNR conditions

“…1, typically in ALE, a filter is adapted based on minimizing a cost function of the error (residual) signal e(n) = x(n) −x(n) where x(n) is a discrete time domain signal at the input, andx(n) is the filtered signal. Traditionally, the mean square error (MSE) is the criterion which is chosen in ALE as the cost function to be minimized [7], [8], [9].…”