Target Signal In Noise Research Articles

Intensity discrimination and neural representation of a masked tone in the presence of three types of masking release.

Hearing ability is usually evaluated by assessing the lowest detectable intensity of a target sound, commonly referred to as a detection threshold. Detection thresholds of a masked signal are dependent on various auditory cues, such as the comodulation of the masking noise, interaural differences in phase, and temporal context. However, considering that communication in everyday life happens at sound intensities well above the detection threshold, the relevance of these cues for communication in complex acoustical environments is unclear. Here, we investigated the effect of three cues on the perception and neural representation of a signal in noise at supra-threshold levels. First, we measured the decrease in detection thresholds produced by three cues, referred to as masking release. Then, we measured just-noticeable difference in intensity (intensity JND) to quantify the perception of the target signal at supra-threshold levels. Lastly, we recorded late auditory evoked potentials (LAEPs) with electroencephalography (EEG) as a physiological correlate of the target signal in noise at supra-threshold levels. The results showed that the overall masking release can be up to around 20 dB with a combination of these three cues. At the same supra-threshold levels, intensity JND was modulated by the masking release and differed across conditions. The estimated perception of the target signal in noise was enhanced by auditory cues accordingly, however, it did not differ across conditions when the target tone level was above 70 dB SPL. For the LAEPs, the P2 component was more closely linked to the masked threshold and the intensity discrimination than the N1 component. The results indicate that masking release affects the intensity discrimination of a masked target tone at supra-threshold levels, especially when the physical signal-to-noise is low, but plays a less significant role at high signal-to-noise ratios.

A Robust System for Detection of Artifacts from EEG Brain Recordings

Epilepsy is a chronic disorder and has the propensity of two or more brain. Analysis of EEG is the primary method for the diagnosis of epilepsy. Contamination of eye movement and blink artifacts presence in EEG data becomes more complicated to the doctors during the diagnosis period. Earlier detection of these artifacts gives a significant advantage of refining the Epilepsy identification process. In this regard, a robust subspace detection method is applied to detect the target signal in noise with possible interference-artifacts, then a dimensionality reduction model, with the combination of fast Independent and Robust Principal Component Analysis (FICA and rPCA) is implemented for identification of artifacts from EEG brain recordings. To perform this the proposed detection method uses synthetic data and artifact contaminated data. The extracted target subspace signal is considered as the input for rPCA and FICA. The ROC analysis is developed as a standard methodology to quantify the detectors' ability to correctly distinguish the target of interest (artifacts) from the background noise in the system.

Musical training during early childhood enhances the neural encoding of speech in noise

For children, learning often occurs in the presence of background noise. As such, there is growing desire to improve a child’s access to a target signal in noise. Given adult musicians’ perceptual and neural speech-in-noise enhancements, we asked whether similar effects are present in musically-trained children. We assessed the perception and subcortical processing of speech in noise and related cognitive abilities in musician and nonmusician children that were matched for a variety of overarching factors. Outcomes reveal that musicians’ advantages for processing speech in noise are present during pivotal developmental years. Supported by correlations between auditory working memory and attention and auditory brainstem response properties, we propose that musicians’ perceptual and neural enhancements are driven in a top-down manner by strengthened cognitive abilities with training. Our results may be considered by professionals involved in the remediation of language-based learning deficits, which are often characterized by poor speech perception in noise.

The CFAR adaptive subspace detector is a scale-invariant GLRT

The constant false alarm rate (CFAR) matched subspace detector (CFAR MSD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. Previously, the CFAR adaptive subspace detector (CFAR ASD), or adaptive coherence estimator (ACE), was proposed for detecting a target signal in noise whose covariance structure and level are both unknown and whose covariance structure is estimated with a sample covariance matrix based on training data. We show here that the CFAR ASD is GLRT when the test measurement is not constrained to have the same noise level as the training data, As a consequence, this GLRT is invariant to a more general scaling condition on the test and training data than the well-known GLRT of Kelly (1986).