Signal-to-noise Ratio Conditions Research Articles

Auditory P300 in Noise in Younger and Older Adults.

Older adults often report difficulty hearing in background noise which is not completely attributable to peripheral hearing loss. Although age-related declines in cognition and hearing in background noise occur, the underlying age-related changes in processing of auditory stimuli in background noise has yet to be fully understood. The auditory P300 has the potential to elucidate the effects of age on auditory and cognitive processing of stimuli in background noise, but additional research is warranted. The purpose of this study was to investigate age-related differences in cognitive processing of auditory stimuli by evoking the auditory P300 at multiple signal-to-noise ratios (SNRs). A two-group, repeated measures study design was used. A convenience sample of 35 participants, 15 older adults (mean age of 66.4 yr) and 20 younger adults (mean age of 21.1 yr), participated in the study. All participants had negative otologic and neurological histories. The auditory P300 was evoked using an oddball paradigm with 500 (frequent) and 1000 Hz (target) tonal stimuli in quiet and in the presence of background noise at +20, +10, and 0 SNRs. P300 amplitudes and latencies were measured in each condition for every participant. Repeated measures analyses of variance were conducted for the amplitude and latency measures of the P300 for each group. Results from this study demonstrated P300 latencies were significantly longer in older adults in noise at the most challenging condition (0 SNR) compared with the quiet condition and between the +10 SNR and 0 SNR conditions. Although older adults had significantly longer P300 latencies compared with younger adults, no significant group by listening condition interaction existed. No significant P300 amplitude differences were found for group, noise, or group × listening condition interactions. Results provide evidence that auditory cortical processing, regardless of age, is poorer at more difficult SNRs. However, results also demonstrate that older adults perform significantly poorer than younger adults. This supports the notion that some degree of age-related decline in synchronous firing and rate of transmission of the auditory cortical neurons contributing to the auditory P300 exists. Studies are needed to further understand the impact of noise on auditory cortical processing across populations.

Research on the optimal optical attenuation in a laser radar using a Geiger-mode APD

For a laser radar (LADAR) system using a Geiger-mode avalanche photodiode (GmAPD), attenuating echo and background noise simultaneously affect the original data output from the GmAPD and eventually affect detection performance. In this study, we established a model that applies to the GmAPD-based LADAR with optical attenuation and also applies to any typical single photon detector that has a dead time (e.g.,the photomultiplier tube); thus, a comprehensive and fundamental study is performed for the mathematical expectation of the number of signal detections (ES), the mathematical expectation of the number of noise detections (EN), the signal-to-noise ratio (SNR), and the range bias (absolute error, Rb) and precision (standard deviation, Rp) under various attenuation levels with different dead times and signal noise conditions. We observed the following: on the one hand, there exists an optimum attenuation level at which ES and SNR are maximized; on the other hand, there exists another optimum attenuation level for shorter dead times, at which Rp is minimized. The phenomenon of the maximum ES, SNR, or minimum Rp disappears gradually as the echo or noise decreases from high levels (e.g.,10 photoelectrons/echo or an equivalent background noise of 10 photoelectrons/range gate). Further, higher attenuation, which shows advantages under strong echo or noise conditions, yields a larger improvement in ES for longer dead times; and, with the reduction of the dead time or the noise, the maximum ES gradually increases, and the corresponding optimum attenuation level becomes slighter. Additionally, we found that, as the optical attenuation increases, EN decreases to 0, Rb changes from a negative value to 0, and Rp is minimized, becomes slightly worse, and reaches a constant. Moreover, the shorter dead times, which show advantages when they are shorter than the end time of the echo, lead to a larger ES, better Rb, and slightly worse Rp than the longer ones.

PSFM—A Probabilistic Source Filter Model for Noise Robust Glottal Closure Instant Detection

Accurate estimation of glottal closure instant (GCI) enables several pitch synchronous speech analysis, such as prosody modifications, glottal inverse filtering, and study of pathological speech. We propose a probabilistic source-filter model (PSFM) for voiced speech, where the source is modeled using the Bernoulli Gaussian distribution, which models the GCI locations and the all-pole filter coefficients are modeled using Gaussian distribution. The probability of GCIs at each speech sample is estimated using the Gibbs sampling. We propose a cost to estimate the exact GCI locations using the N-best dynamic programming. A key feature of the proposed PSFM is that it allows us to include the second-order statistics of the noise for estimating the GCI locations, thereby resulting in a noise robust GCI detection technique, although it has high computational complexity. Evaluation on archivable priority list actual-word database (APLAWD) database shows the proposed algorithm performs at par with the state-of-the-art GCI detection method on clean speech. However, when evaluated in noisy conditions using five types of noises at six different signal-to-noise ratio (SNR) levels, we observe that the proposed method performs better than the best of the existing GCI detection scheme, particularly at low SNR condition indicating the noise robustness of the proposed method.

The effect of different acoustic noise on speech signal formant frequency location

The presence of noise is one of the major challenges and concerns in speech recognition systems. There are in particular different kinds of noises (pink, white and leopard) that can adversely affect a speech signal in various ways and degrees. In this study, the extent of resistance of a speech signal’s formants or in other words, the displacement of the formants have been measured against being subjected to different conventional noises. The methodology adopted was to apply different noises to the original voice signal, then to measure and to investigate the amount of formant location displacement. In this paper, the mean square movement (MSM) parameter has been introduced. This represents the deviation and displacement amount of the frequencies of the formants caused by applying the various noises. All of the investigations were conducted under three different SNR conditions (5, 10 and 15 dB). This allowed for the assessment of the influence of the signal-to-noise ratio (SNR) on the MSM parameter and the extent of the displacements of the formants. The results indicate that the frequency of the formants under these three SNR amounts was resistant against the machine gun type of noise, whilst white noise caused the most measureable effect and displacement in the frequencies of the formants.

Joint signal-to-noise ratio and source number estimation based on hierarchical artificial intelligence units

Accurately measuring the direction of arrival (DOA) is one of the most important issues in multiple sensor/antenna array monitoring scenarios. However, as a necessary parameter of almost all state-of-the-art DOA estimation methods, the source number is always hard to be determined by using the traditional Akaike information criterion (AIC) or minimum description length (MDL) methods, especially in low or very low signal-to-noise ratio (SNR) conditions. In this paper, we propose to sequentially estimate the SNR and source number in a novel data-driven manner by employing artificial intelligence (AI) techniques. Specifically, a simulated uniform linear array (ULA) dataset with different source numbers and different SNRs is first generated. With this dataset, the artificial neural network (ANN) regression unit for SNR prediction is built. Then, a hierarchical support vector machine (SVM) classification unit is constructed to estimate the source number. Finally, with these hierarchical AI units, the SNR and source number were estimated simultaneously in an iterative way. Experimental results illustrated that the proposed method can estimate the source number stably and reliably even in the low SNR condition.

Vibration phases estimation based on multi-channel interferometry for ISAL.

Target vibration introduces unexpected vibration phases in the echo signal of inverse synthetic aperture LADAR (ISAL). The vibration phases cause the imaging results to be defocused. How to estimate the vibration phases has always been one of the difficulties in ISAL imaging, and there has not been an effective solution. In this paper, two methods are proposed to estimate the vibration phases in different situations. The method based on multi-channel along-track interferometry (MCATI method) is for some cases that the target velocity vector is parallel to some baselines of ISAL. The method based on orthogonal interferometry (OI method) is for other cases that the target velocity vector is not parallel to any baseline of ISAL. In addition, a high signal to noise ratio (SNR) is essential for the MCATI and OI methods to get high estimation accuracy. Therefore, a large power-aperture product should be carefully considered in the ISAL system design. Furthermore, on the condition of low SNR, range cell accumulation is also helpful to reduce the influence of noise. Take the ISAL, which is utilized to image the satellite in 500km orbit, as an example, the estimation accuracy can satisfy the demand of synthetic aperture imaging with 0dB single-pulse SNR and 300 range cell accumulation. The proposed methods are verified with simulation and experiment.

Tracking and detecting highs maneuvering weak targets

Modern military targets as aircraft are able to perform high maneuvers due to their complex design. This ability of maneuvers includes sudden changes in acceleration and high-G turns that are not achievable from traditional military targets. Moreover, recent military targets generally have a low radar cross-section or low signal-to-noise ratio (SNR) profile, which makes the detection and tracking of those maneuvering targets a complicated dynamic state estimation problem. In this case, the track-before-detect filter (TBDF) that uses unthresholded measurements is considered as an effective method for tracking and detecting a single maneuvering target under low SNR conditions. Nevertheless, the performance of the algorithm will be affected by severe loss because of the mismatching of target model during maneuver. To resolve the target maneuvers, we propose an application of particle filtering, which depends on TBD. We employ the constant acceleration model and coordinate turn model. Our simulation results show that the detection and tracking of maneuvering target performance of TBDF has been improved using the proposed algorithm.

Periodic component analysis as a spatial filter for SSVEP-based brain–computer interface

BackgroundTraditional spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. New methodIn this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. ResultsData from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. Comparison with existing methodsThe experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. ConclusionsThe results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain–computer interface (BCI).

Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines

Condition-based monitoring of rotating machines requires robust features for accurate fault diagnosis, which is indeed directly linked to the quality of the features extracted from the signals. This is especially true for vibration data, whose quasi-stationary nature implies that the quality of frequency domain extracted features depends on the signal-to-noise ratio (SNR) condition, operating condition variations, and data segmentation. This paper presents a novel statistical spectral analysis, which leads to highly robust fault diagnosis with poor SNR conditions, different time-window segmentation, and different operating conditions. The amplitudes of spectral contents of the quasi-stationary time vibration signals are sorted and transformed into statistical spectral images. The sort operation leads to the knowledge of the empirical cumulative distribution function (ECDF) of the amplitudes of each frequency band. The ECDF provides a robust statistical information of the distribution of the amplitude under different SNR and operating conditions. Statistical metrics have been adopted for fault classification, by using the ECDFs obtained from the spectral images as fault features. By applying simple statistical metrics, it is possible to achieve fault diagnosis without classifier training, saving both time and computational costs. The proposed algorithm has been tested using a vibration data benchmark: comparison with state-of-the-art fault diagnosis algorithms shows promising results.

The Effect of Signal to Noise Ratio on Cortical Auditory-Evoked Potentials Elicited to Speech Stimuli in Infants and Adults With Normal Hearing.

Identification and discrimination of speech sounds in noisy environments is challenging for adults and even more so for infants and children. Behavioral studies consistently report maturational differences in the influence that signal to noise ratio (SNR) and masker type have on speech processing; however, few studies have investigated the neural mechanisms underlying these differences at the level of the auditory cortex. In the present study, we investigated the effect of different SNRs on speech-evoked cortical auditory-evoked potentials (CAEPs) in infants and adults with normal hearing. A total of 10 adults (mean age 24.1 years) and 15 infants (mean age 30.7 weeks), all with normal hearing, were included in the data analyses. CAEPs were evoked to /m/ and /t/ speech stimuli (duration: 79 ms) presented at 75 dB SPL in the sound field with a jittered interstimulus interval of 1000-1200 ms. Each of the stimuli were presented in quiet and in the presence of white noise (SNRs of 10, 15, and 20 dB). Amplitude and latency measures were compared for P1, N1, and P2 for adults and for the large positivity (P) and following negativity (N: N250 and/or N450) for infants elicited in quiet and across SNR conditions. Infant P-N responses to /t/ showed no statistically significant amplitude and latency effects across SNR conditions; in contrast, infant CAEPs to /m/ were greatly reduced in amplitude and delayed in latency. Responses were more frequently absent for SNRs of 20 dB or less. Adult P1-N1-P2 responses were present for all SNRs for /t/ and most SNRs for /m/ (two adults had no responses to /m/ for SNR 10); significant effects of SNR were found for P1, N1, and P2 amplitude and latencies. The findings of the present study support that SNR effects on CAEP amplitudes and latencies in infants cannot be generalized across different types of speech stimuli and cannot be predicted from adult data. These findings also suggest that factors other than energetic masking are contributing to the immaturities in the SNR effects for infants. How these CAEP findings relate to an infant's capacity to process speech-in-noise perceptually has yet to be established; however, we can be confident that the presence of CAEPs to a speech stimulus in noise means that the stimulus is detected at the level of the auditory cortex. The absence of a response should be interpreted with caution as further studies are needed to investigate a range of different speech stimuli and SNRs, in conjunction with behavioral measures, to confirm that infant CAEPs do indeed reflect functional auditory capacity to process speech stimuli in noise.

Highly accurate adaptive TOF determination method for ultrasonic thickness measurement

Determining the time of flight (TOF) is very critical for precise ultrasonic thickness measurement. However, the relatively low signal-to-noise ratio (SNR) of the received signals would induce significant TOF determination errors. In this paper, an adaptive time delay estimation method has been developed to improve the TOF determination’s accuracy. An improved variable step size adaptive algorithm with comprehensive step size control function is proposed. Meanwhile, a cubic spline fitting approach is also employed to alleviate the restriction of finite sampling interval. Simulation experiments under different SNR conditions were conducted for performance analysis. Simulation results manifested the performance advantage of proposed TOF determination method over existing TOF determination methods. When comparing with the conventional fixed step size, and Kwong and Aboulnasr algorithms, the steady state mean square deviation of the proposed algorithm was generally lower, which makes the proposed algorithm more suitable for TOF determination. Further, ultrasonic thickness measurement experiments were performed on aluminum alloy plates with various thicknesses. They indicated that the proposed TOF determination method was more robust even under low SNR conditions, and the ultrasonic thickness measurement accuracy could be significantly improved.

Listeners Experience Linguistic Masking Release in Noise-Vocoded Speech-in-Speech Recognition.

The purpose of this study was to evaluate whether listeners with normal hearing perceiving noise-vocoded speech-in-speech demonstrate better intelligibility of target speech when the background speech was mismatched in language (linguistic release from masking [LRM]) and/or location (spatial release from masking [SRM]) relative to the target. We also assessed whether the spectral resolution of the noise-vocoded stimuli affected the presence of LRM and SRM under these conditions. In Experiment 1, a mixed factorial design was used to simultaneously manipulate the masker language (within-subject, English vs. Dutch), the simulated masker location (within-subject, right, center, left), and the spectral resolution (between-subjects, 6 vs. 12 channels) of noise-vocoded target-masker combinations presented at +25 dB signal-to-noise ratio (SNR). In Experiment 2, the study was repeated using a spectral resolution of 12 channels at +15 dB SNR. In both experiments, listeners' intelligibility of noise-vocoded targets was better when the background masker was Dutch, demonstrating reliable LRM in all conditions. The pattern of results in Experiment 1 was not reliably different across the 6- and 12-channel noise-vocoded speech. Finally, a reliable spatial benefit (SRM) was detected only in the more challenging SNR condition (Experiment 2). The current study is the first to report a clear LRM benefit in noise-vocoded speech-in-speech recognition. Our results indicate that this benefit is available even under spectrally degraded conditions and that it may augment the benefit due to spatial separation of target speech and competing backgrounds.

Bayesian Bistatic ISAR Imaging for Targets with Complex Motion under Low SNR Condition.

This paper proposes a novel bistatic inverse synthetic aperture radar (ISAR) imaging algorithm for the target with complex motion under low signal to noise ratio (SNR) condition. Note the bistatic ISAR system generally suffers from a lower SNR than the monostatic one because of its non-mirror reflection geometry. A de-noising method, therefore, is proposed to improve SNR of range profiles, which accumulates the aligned range profiles non-coherently to obtain a window for noise suppression. Additionally, since the complex motion of target induces nonstationary Doppler, which is destructive to ISAR imaging, an optimal coherent processing interval (CPI) selection algorithm is further proposed to find out the interval where the Doppler is relatively stationary, so as to produce well-focused ISAR images. It utilizes the reassigned time-frequency (TF) method to obtain the high resolution instantaneous Doppler spectrum, and the minimum entropy criterion to select the optimal CPI, respectively. Note the selected CPI often contains too limited pulses to produce ISAR images with high resolution. A sparse aperture ISAR imaging method within the Bayesian framework is further proposed, which introduces the Laplacian scale mixture (LSM) model as the sparse prior, so as to reconstruct well-focused ISAR images with high resolution and low side lobes from the limited data. Compared with the traditional sparse Bayesian learning method, the proposed LSM based ISAR imaging performs superiorly on resolution improvement and noise reduction. Experimental results based on both simulated and measured data validate the effectiveness of the proposed algorithms.

The Improved Adaptive Silence Period Algorithm over Time-Variant Channels in the Cognitive Radio System

In the field of cognitive radio spectrum sensing, the adaptive silence period management mechanism (ASPM) has improved the problem of the low time-resource utilization rate of the traditional silence period management mechanism (TSPM). However, in the case of the low signal-to-noise ratio (SNR), the ASPM algorithm will increase the probability of missed detection for the primary user (PU). Focusing on this problem, this paper proposes an improved adaptive silence period management (IA-SPM) algorithm which can adaptively adjust the sensing parameters of the current period in combination with the feedback information from the data communication with the sensing results of the previous period. The feedback information in the channel is achieved with frequency resources rather than time resources in order to adapt to the parameter change in the time-varying channel. The Monte Carlo simulation results show that the detection probability of the IA-SPM is 10–15% higher than that of the ASPM under low SNR conditions.

A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions

Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average <i>p</i> norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.

A High-Precision Method of Phase-Derived Velocity Measurement and Its Application in Motion Compensation of ISAR Imaging

The existing methods for motion compensation in inverse synthetic aperture radar (ISAR) imaging are generally limited to the low-order target motion model, and require iterative optimization with limited velocity estimate precision and heavy computational burdens. This paper proposes a high-precision method of phase-derived velocity measurement (PDVM) and applies it to motion compensation of ISAR imaging. The method applies PDVM based on range profiles cross correlation to the translational velocity estimation of targets, and converts the velocity measurement results to the corresponding range increment. The equivalent phase-derived range measurement precision can reach the order of magnitude of millimeter (mm) or even sub-mm, which can satisfy the precision requirements of both envelope alignment and phase adjustment. The key to realizing PDVM is resolving phase ambiguity. The traditional method for resolving ambiguity has very high requirements for the signal-to-noise ratio (SNR). This work resolves ambiguity by combining multiframe data, i.e., by resolving ambiguity of multiframe data simultaneously instead of resolving ambiguity of single-frame data independently and correcting the above ambiguity-resolving results using a minimum-entropy method. Therefore, phase ambiguity can be correctly resolved under a relatively low SNR. Experimental results of an ISAR imaging of an airplane show that the method proposed in this paper can obtain high-quality ISAR imagery, and can efficiently realize robust imaging under the conditions of low SNR.

Impact of light-adaptive mechanisms on mammalian retinal visual encoding at high light levels.

A persistent change in illumination causes light-adaptive changes in retinal neurons. Light adaptation improves visual encoding by preventing saturation and by adjusting spatiotemporal integration to increase the signal-to-noise ratio (SNR) and utilize signaling bandwidth efficiently. In dim light, the visual input contains a greater relative amount of quantal noise, and vertebrate receptive fields are extended in space and time to increase SNR. Whereas in bright light, SNR of the visual input is high, the rate of synaptic vesicle release from the photoreceptors is low so that quantal noise in synaptic output may limit SNR postsynaptically. Whether and how reduced synaptic SNR impacts spatiotemporal integration in postsynaptic neurons remains unclear. To address this, we measured spatiotemporal integration in retinal horizontal cells and ganglion cells in the guinea pig retina across a broad illumination range, from low to high photopic levels. In both cell types, the extent of spatial and temporal integration changed according to an inverted U-shaped function consistent with adaptation to low SNR at both low and high light levels. We show how a simple mechanistic model with interacting, opponent filters can generate the observed changes in ganglion cell spatiotemporal receptive fields across light-adaptive states and postulate that retinal neurons postsynaptic to the cones in bright light adopt low-pass spatiotemporal response characteristics to improve visual encoding under conditions of low synaptic SNR.

Quantization of high dimensional Gaussian vector using permutation modulation with application to information reconciliation in continuous variable QKD

This paper is focused on the problem of Information Reconciliation (IR) for continuous variable Quantum Key Distribution (QKD). The main problem is quantization and assignment of labels to the samples of the Gaussian variables observed at Alice and Bob. Trouble is that most of the samples, assuming that the Gaussian variable is zero mean which is de-facto the case, tend to have small magnitudes and are easily disturbed by noise. Transmission over longer and longer distances increases the losses corresponding to a lower effective Signal-to-Noise Ratio (SNR) exasperating the problem. Quantization over higher dimensions is advantageous since it allows for fractional bit per sample accuracy which may be needed at very low SNR conditions whereby the achievable secret key rate is significantly less than one bit per sample. In this paper, we propose to use Permutation Modulation (PM) for quantization of Gaussian vectors potentially containing thousands of samples. PM is applied to the magnitudes of the Gaussian samples and we explore the dependence of the sign error probability on the magnitude of the samples. At very low SNR, we may transmit the entire label of the PM code from Bob to Alice in Reverse Reconciliation (RR) over public channel. The side information extracted from this label can then be used by Alice to characterize the sign error probability of her individual samples. Forward Error Correction (FEC) coding can be used by Bob on each subset of samples with similar sign error probability to aid Alice in error correction. This can be done for different subsets of samples with similar sign error probabilities leading to an Unequal Error Protection (UEP) coding paradigm.

Noise‐robust HRRP target recognition method via sparse‐low‐rank representation

A novel target recognition method is proposed for high-range resolution profiles (HRRPs) of radar targets under low signal-to-noise ratio (SNR) conditions. This method achieves good recognition performance for noisy HRRPs with discriminative sparse-low-rank representation. The framework of this method is constructed based on sparse representation and low-rank representation, which are applied to extract the local and global characteristics of target HRRPs. To guarantee the noise-robust and highly discriminative features of the HRRPs, dictionary learning is adopted. In the training stage, a discriminative dictionary is produced based on hinge loss theory to improve the recognition performance. Denoising dictionary optimisation is implemented for noise suppression during the testing stage. Experimental results on measured HRRP data demonstrate that the proposed method can recover the original HRRPs and significantly improve the recognition performance for HRRP test samples under relatively low SNR conditions.

Comparison of Single-Task versus Dual-Task for Listening Effort.

Background and ObjectivesDepending on the kind of task and/or material, listeners sometimes need to pay attention to understand communication. The present study aimed to estimate a listener’s amount of effort needed to understand communication by using recognition score and response time as a function of signal-to-noise ratio (SNR) and to confirm his/ her task dependency for listening effort.Subjects and MethodsForty-eight young adults with normal hearing participated in the study. As stimuli, Korean Speech Perception in Noise test without a question tag and three consecutive digits were used for sentence recognition (or single-task) and arithmetic (or dual-task), respectively. Both tasks were measured in quiet and under four SNR (i.e., 0, -4, -8, -12 dB) conditions. Their outcomes were analyzed and compared in terms of percent correct and response time.ResultsSentence recognition scores and arithmetic scores decreased as the level of noise increased. Response time for sentence recognition decreased as noise increased, whereas the response time for arithmetic increased as noise increased. In addition, there was a negative correlation between error percent and response time in the sentence recognition. Conversely, a positive correlation between error percent and response time appeared in the arithmetic test.ConclusionsListening effort showed a different pattern based on the kind of task, single vs. dual, while the dual-task required greater effort from the listener.