Output Signal-to-noise Ratio Research Articles

AEEFCSR: an adaptive ensemble empirical feed-forward cascade stochastic resonance system for weak signal detection

Abstract A novel adaptive ensemble empirical feed-forward cascade stochastic resonance (AEEFCSR) method is proposed in this study for the challenges of detecting target signals from intense background noise. At first, we create an unsaturated piecewise self-adaptive variable-stable potential function to overcome the limitations of traditional potential functions. Subsequently, based on the foundation of a feed-forward cascaded stochastic resonance method, a novel weighted function and system architecture is created, which effectively addresses the issue of low-frequency noise enrichment through ensemble empirical mode decomposition. Lastly, inspired by the spider wasp algorithm and nutcracker optimization algorithm, the spider wasp nutcracker optimization algorithm is proposed to optimize the system parameters and overcome the problem of relying on manual experience. In this paper, to evaluate its performance, the output signal-to-noise ratio (SNR), spectral sub-peak difference, and time-domain recovery capability are used as evaluation metrics. The AEEFCSR method is demonstrated through theoretical analysis. To further illustrate the performance of the AEEFCSR method, Validate the adoption of multiple engineering datasets. The results show that compared with the compared algorithms, the output SNR of the AEEFCSR method is at least 6.2801 dB higher, the spectral subpeak difference is more than 0.25 higher, and the time-domain recovery effect is more excellent. In summary, the AEEFCSR method has great potential for weak signal detection in complex environments.

The Noise Reduction Algorithm May Not Compensate for the Degradation in Output Signal-to-Noise Ratio Caused by Wide Dynamic Range Compression.

Most modern hearing aids (HAs) employ wide dynamic range compression (WDRC) and noise reduction (NR) algorithms. It is known that the nonlinear effects of WDRC and NR cause changes to the output signal-to-noise ratio (SNR) of an HA. However, the relative contributions of WDRC and NR to the nonlinear effects are not fully understood. The current study investigated (a) whether WDRC or NR dominates the nonlinear effects measured at the output of a digital HA and (b) whether the electroacoustic effectiveness of NR depends on WDRC parameters while input SNR and background noise are systematically varied. Test stimuli were Connected Speech Test sentences in multitalker babble noise (2- or 20-talker), presented at input SNRs ranging from -10 to +10 dB. The HA was programmed using multiband WDRC set according to the National Acoustic Laboratories for Nonlinear HA fitting formula 2 prescriptive fits for four standard audiograms and two compression speeds. The NR algorithm of the HA was switched on or off in separate conditions. Nonlinear electroacoustic effects from the WDRC and NR algorithms were assessed by measuring the output SNR of the HA using a phase-inversion technique. To investigate whether there are other factors that may be important besides the output SNR, the Hearing Aid Speech Intelligibility Index and the Hearing Aid Speech Quality Index were applied to the recordings to generate inferences on aided speech intelligibility and perceived speech quality. Results showed that WDRC dominated the net nonlinear effect at low-input SNRs, and the net nonlinear effect of WDRC and NR was reduced at high-input SNRs. Results also showed that the effectiveness of NR depended on compression parameters. The effectiveness of NR was partially explained by the trend of Hearing Aid Speech Intelligibility Index and Hearing Aid Speech Quality Index scores, potentially indicating that the Hearing Aid Speech Intelligibility Index and Hearing Aid Speech Quality Index scores may capture factors that cannot be captured by the output SNR metric. Results suggest that the individual signal-processing stages in an HA should not be considered as independent. Electroacoustic evaluation of WDRC and NR algorithms in isolation is not sufficient to capture the combined nonlinear effect of the two algorithms. https://doi.org/10.23641/asha.25962541.

Discrimination of VOCs with e-nose consisting of a single MEMS sensor using lightweight deep learning model based on SqueezeNet

The detection of volatile organic compounds (VOCs) has numerous applications in environmental monitoring and biomedicine. Metal oxide semiconductor (MOS) gas sensors are widely used in gas detection due to their simple structure, low fabrication cost, and fast response speed. However, MOS gas sensors have broad-spectrum responsiveness to VOCs, making selectivity a challenge. The most commonly used method to obtain selectivity is the E-nose system consisting of multiple sensors. However, the size of E-noses has increased due to the number of sensors with high power consumption and complicated signal processing. In recent years, deep learning has flourished, allowing for accurate discrimination of VOCs and concentration prediction using E-nose consisting of a single sensor. This method significantly reduces power consumption and facilitates portable applications. In this work, we synthesize SnO2-ZnO composite oxide and fabricate a E-nose consisting of a single MEMS gas sensor. The response performance of the E-nose to VOCs is stable with a high output signal-noise ratio (SNR). We design a lightweight deep learning model based on SqueezeNet and train it using transfer learning. Linear regression is used to predict the concentration of VOCs. Our work achieves accurate identification and low-error concentration prediction of VOCs, which is valuable for subsequent portable VOC detection systems.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

Research and Application of Two-Dimensional Time-Delayed Tri-Stable Stochastic Resonance System for Bearing Fault Detection

The time-delayed feedback term can improve the output of a system, while the two-dimensional stochastic resonance (SR) system has a stronger signal amplification capability. To improve the output signal-to-noise ratio (SNR) of the system, this paper proposes a two-dimensional time-delayed tri-stable stochastic resonance system (TDTDTSR) based on the advantages of the above two systems. First, the steady-state probability density function (SPD), the mean first-pass time (MFPT), and the output SNR are derived under adiabatic approximation theory, and the effects of different system parameters on them are investigated. Next, TDTDTSR and the classical two-dimensional tri-stable stochastic resonance system (CTDTSR) system are simulated numerically. The results show that the mean signal-to-noise gain (MSNRG) of TDTDTSR system is higher than that of the CTDTSR system. Finally, the system parameters are optimized using a genetic algorithm, and the application of TDTDTSR to bearing fault detection is compared with CTDTSR and the novel piecewise symmetric two-dimensional tri-stable stochastic resonance (NPSTDTSR) systems. The experimental results demonstrate that TDTDTSR system has better performance, providing valuable theoretical support and practical engineering applications for the system in subsequent analyses.

Impact of potential function asymmetry on the performance of a novel stochastic resonance system

Due to output saturation and parameter coupling, the traditional classical bistable stochastic resonance (CBSR) system is often unable to effectively handle the extraction of weak signal features encountered in many engineering applications. To overcome this limitation, an asymmetric decoupled unsaturated bistable stochastic resonance (ADUBSR) system is proposed in this paper. Subsequently, the impact of barrier asymmetry on system output has been analyzed by taking signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and the optimal noise required for obtaining PSNR as evaluation indicators. The results indicate that: (1) The output SNR is effectively enhanced by barrier-width asymmetry only when the left barrier is narrower than the right. (2) Barrier-height asymmetry fails to effectively improve the output SNR. (3) In the case of barrier-height-width asymmetry, the barrier width asymmetry predominantly enhances the output SNR, resulting in the best performance. (4) For all three asymmetric structures, regardless of noise type, the optimal noise intensity depends greatly on barrier height and width values, and is less influenced by asymmetry. Finally, the proposed system is used for engineering data, and the results show that the signal processed by the ADUBSR system achieves a higher output SNR and the amplitude at the fault frequency is 10 times greater than that of the signal processed by the CBSR system.

The Output Signal-to-Noise Ratio and Speech Perception in Noise: The Effect of Multichannel and Free-Channel Hearing Aids

Background and Aim: The output Signal-to-Noise Ratio (SNR) is one of the essential factors in hearing aid benefits. There is limited evidence regarding SNR improvement by the Channel-Free (CFHA) and Multi-Channel Hearing Aid (MCHA) and the speech understanding in noise through them. This study aimed to investigate the extent to which output SNR was modified by CFHA and MCHA processing and the variation in aided speech recognition abilities with a change in output SNR. Methods: Thirty-six participants aged 50–65 years were included. A chosen CFHA and MCHA were used to obtain the output SNR and sentence recognition in noise in four different processing algorithms (linear, linear+noise reduction, WDRC, WDRC+noise reduction). Hagerman’s phase inversion technique was used to measure the attenuation of noise and, in turn, to obtain the output SNR of the hearing aid. Results: In all hearing aid processing algorithms among those with normal hearing and people with hearing loss, the output of CFHA revealed higher attenuation values than that of MCHA. There was a significant effect of the hearing aids and processing algorithms in both normal and individuals with hearing impairment on the mean SNR. Further, multiple linear regression analysis results showed that whether the hearing is channel-free or multi- channel significantly predicted speech recognition scores, while output SNR and processing algorithms did not. Conclusion: The signal processing algorithms in CFHA had greater noise attenuation values, better output SNR, and speech recognition scores, showing an advantage over the modern MCHA among individuals with hearing impairment. Keywords: Channel-free hearing aids; multi-channel hearing aids; sensorineural hearing loss; speech recognition

Enhanced fault diagnosis via stochastic resonance in a piecewise asymmetric bistable system.

Weak fault signals are often overwhelmed by strong noise or interference. The key issue in fault diagnosis is to accurately extract useful fault characteristics. Stochastic resonance is an important signal processing method that utilizes noise to enhance weak signals. In this paper, to address the issues of output saturation and imperfect optimization of potential structure models in classical bistable stochastic resonance (CBSR), we propose a piecewise asymmetric stochastic resonance system. A two-state model is used to theoretically derive the output signal-to-noise ratio (SNR) of the bistable system under harmonic excitations, which is compared with the SNR of CBSR to demonstrate the superiority of the method. The method is then applied to fault data. The results indicate that it can achieve a higher output SNR and higher spectral peaks at fault characteristic frequencies/orders, regardless of whether the system operates under fixed or time-varying speed conditions. This study provides new ideas and theoretical guidance for improving the accuracy and reliability of fault diagnosis technology.

Performance evaluation of L‐maximal ratio combining diversity with estimation error for Shadowed Beaulieu‐Xie fading channels

SummaryTo provide the increasing demand for wireless technologies, high capacity with lower latency is vital. New emerging technologies like 5G, millimeter wave, THz, and so forth are able to characterize such complex fading and shadowed environments. Spatial diversity is one of the techniques to minimize fading in the wireless communication channel. The performance of the wireless communication system can be further optimized by using estimators at the receivers. The influence of imperfect channel estimation on the performance over shadowed Beaulieu‐Xie (SBX) fading channels with L‐maximal ratio combining (MRC) diversity is studied. The probability density function (PDF) of the received output signal‐to‐noise ratio (SNR) with L‐MRC over SBX fading channels is derived. Further PDF of the received output SNR, outage probability, and average bit error rate (ABER) using the derived PDF is evaluated for L‐MRC diversity with channel estimation error over SBX fading channels. ABER expression for Gray‐coded rectangular quadrature amplitude modulation over the SBX channel is also evaluated. The study also includes how the system performs under the effect of different shadowing and fading parameters.

Power-line interference and baseline wander elimination in ECG using VMD and EWT

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD.

Raman/EDFA Hybrid System to Enhance the Optical Signal in the Optical Network

In this paper, a hybrid optical signal amplification system that includes Raman scattering and Erbium-doped fiber amplifier (EDFA) was simulated to take advantage of the amplification properties of the optical signal transmitted through the optical fiber network that connects the first and third campuses of the University of Mosul. The two types of optical signal amplifiers were studied individually and together, and the effect was observed in each case on the optical signal parameters such as the Q-factor, noise figure, signal-to-noise ratio, and gain. Optisystem ver.7.0 software was used to simulate the proposed system. The results showed that the Raman scattering and EDFA hybrid systems functioned effectively. The output signal power level is higher than the input signal power level that increased from (16 microwatts) to (83 mill watts). For each frequency, the gain is positive and the noise figure is relatively high. Although, this indicates that some noise introduced into the system. The output signal to noise ratio (OSNR) is high (55 dB). Moreover, the system is still able to maintain a high signal-to-noise ratio. The obtained results showed that the Raman scattering and EDFA hybrid system is a worthwhile technique for amplifying optical signals for the optical network connecting the first and third campuses of the University of Mosul.

Piecewise time-delay tri-stable stochastic resonance system and its application in bearing fault diagnosis

Current studies of time-delay based systems are compared to classical tri-stable stochastic resonance (CTSR) system and do not reflect the effect of adding a delay term on unsaturated systems. In this paper, a piecewise delayed tri-stable stochastic resonance (PTTSR) system is proposed. Firstly, the equivalent Langevin for PTTSR system is derived and compared with the output of CTSR and piecewise tri-stable stochastic resonance (PTSR) systems, where the addition of the time-delay term can further improve the output amplitude. Secondly, mean first pass time (MFPT) and output signal-to-noise ratio (SNR) are derived, and the effects of different parameters on MFPT and SNR are investigated. increasing the feedback intensity and time-delay length improved the output SNR. Then, the input period signal is then numerically simulated using the signal-to-noise ratio gain (SNRG) as a measurement, which increases by 2.75 dB for PTTSR system compared to PTSR system. Finally, the three systems are used for bearing fault detection and the system parameters are optimized by genetic algorithm. The results showed that the output amplitude of PTTSR system is more than 12 times that of PTSR system and the SNRG increased by more than 2 dB. This demonstrates the superior performance of PTTSR system in detecting weak signals and provides good feasibility in engineering applications.

DCT based densely connected convolutional GRU for real-time speech enhancement

Over the past ten years, deep learning has enabled significant advancements in the improvement of noisy speech. Due to the short time stability of speech signal, previous speech enhancement (SE) methods concentrated only on magnitude estimation, and these methods added a phase of the mixture in reconstructing the speech. The performance is limited in these approaches since the phase will also carry some of the speech information. Some of the speech enhancement approaches were developed later to jointly estimate both magnitudes as well as phases. Recently, complex-valued models, like deep complex convolution recurrent network (DCCRN), are proposed, but the computation of the model is very huge. In this work, we propose a Discrete Cosine Transform-based Densely Connected Convolutional Gated Recurrent Unit (DCTDCCGRU) model using dilated dense block and stacked GRU. The dense connectivity strengthens the gradient propagation by concatenating features from previous layers at the input. The advantage of the dense block is that at various resolutions, the dilated convolutions aid with context aggregation, and the dense connectivity provides a feature map with more precise target information by passing through multiple layers. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of STOI (short-time objective intelligibility), PESQ (perceptual evaluation of the speech quality), and output SNR (signal-to-noise ratio).

Pre-Processing Based ECG Signal Analysis Using Emerging Tools

The objective of the paper is to count the clear beats (R-peaks or QRS complexes) in the Electrocardiogram (ECG) signal after the pre-processing step. Among the three selected techniques, Independent Principal Component Analysis (IPCA), Fractional Fourier Transform (FrFT) and Fractional Wavelet Transform (FrWT) have Sensitivity (Se) of 99.92%, 99.95% and 99.98%, Accuracy (Acc) of 99.88%, 99.93% and 99.96%, Detection Error Rate (DER) of 0.110%, 0.066% and 0.036% along with the disturbance of 2.3%, 2% and 1.7% respectively. The lowest DER in FrWT indicates that it outperforms the IPCA and FrFT. The False Negative plus False Positive (FN + FP) by using the three techniques was 32, 25 and 13 while the output Signal-to-Noise Ratio (SNR) was found to be 24.42, 26.19 and 34.37 dB respectively. These results were figured out on Real Time Database (RT DB). The values of output SNR, Se and Acc were recorded to be 34.37 dB, 99.98% and 99.96% while using FrWT based pre-processing on the data from RT DB. The same technique when used on the data from MIT-BIH arrhythmia Database (MB DB) gave the values of 24.81 dB, 99.96% and 99.93% respectively. It has been observed that all the attributes of the components of ECG signals were preserved when filtered with FrFT. On the other hand, the FrWT segregates the motion artefacts and ECG signal in a better way than FrFT. This is due to the fact that in FrWT decomposition takes place on the signal rotated at a selected angle resulting in less computationally complex decompositions at various levels. It reveals that FrWT retains important biology-related information as the originally recorded ECG signal has.

The polarization filtering line spectrum extraction algorithm of underwater target signal

In order to increase the performance of underwater target line spectrum extraction, an adaptive polarization filtering line spectrum extraction method is researched in the paper. By using of the polarization characteristics of underwater target signals, the polarization characteristics of underwater target line spectrum signal is analyzed via singular value decomposition (SVD) method in frequency domain. Firstly, the line spectrum signal is presented by the eigenimage corresponding to the maximum singular value to increase the signal-to-noise ratio (SNR) of the signal. Then the polarization parameters are extracted according to the polarization characteristics of the line spectrum signals. And the line spectrum signal is filtered by constructing the polarization filtering function. The simulation results show that the output SNR of the proposed method are larger than that of conventional FFT analysis method when the SNR of the signal is larger than -9dB, and the detection probability of the proposed method is 100% when the SNR of the signal is -1 dB, while the detection probability of the conventional FFT analysis method is 27.73%. It shows that the proposed method provides a new approach for underwater line spectrum signal extraction techniques.

Single line noise cancellation using derivative of normalized least mean square algorithm

Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original error data normalized step size (EDNSS) algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.

IDBD-Based Beamforming Algorithm for Improving the Performance of Phased Array Radar in Nonstationary Environments

Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the tap weights is fixed, leading to errors in the beam pattern and a reduced output signal-to-noise ratio (SNR). In this paper, we use the incremental delta-bar-delta (IDBD) algorithm, which has been widely used for system identification problems in nonstationary environments, to control the time-varying learning rates of the tap weights. The designed iteration formula for the learning rate ensures that the tap weights adaptively track the Wiener solution. The results of numerical simulations show that in a nonstationary environment, the traditional gradient descent algorithm with a fixed learning rate has a distorted beam pattern and reduced output SNR; however, the IDBD-based beamforming algorithm, in which a secondary control mechanism is used to adaptively update the learning rates, showed a similar beam pattern and output SNR to a traditional beamformer in a Gaussian white noise background; that is, the main beam and null satisfied the pointing constraints, and the optimal output SNR was obtained. Although the proposed algorithm contains a matrix inversion operation, which has considerable computational complexity, this operation could be replaced by the Levinson-Durbin iteration due to the Toeplitz characteristic of the matrix; therefore, the computational complexity could be decreased to O(n), so additional computing resources are not required. Moreover, according to some intuitive interpretations, the reliability and stability of the algorithm are guaranteed.

Large Spin Hall Angle in Sputtered BiSb Topological Insulator on Top of Various Ferromagnets With In-Plane Magnetization for SOT Reader Application

A spin-orbit torque (SOT) reader using the inverse spin Hall effect was recently proposed as a candidate for magnetic recording beyond 4 Tbit/in2. However, SOT readers using conventional heavy metals have small output voltage and low signal-to-noise ratio (SNR) due to the small spin Hall angles of heavy metals. Demonstrated in this work is a large spin Hall angle of 2.6–5.1 in sputtered BiSb, a topological insulator, on top of various ferromagnets with in-plane magnetization. The spin Hall angle of BiSb is much larger than that of heavy metals, which is promising for SOT readers with large output and high SNR.

Large inverse spin Hall effect in BiSb topological insulator for 4 Tb/in2 magnetic recording technology

It is technically challenging to shrink the size of a tunneling magnetoresistance reader to below 20 nm for magnetic recording technology beyond 4 Tb/in2 due to its complex film stack. Recently, we proposed a reader architecture based on the inverse spin Hall effect to resolve those challenges, referred below as spin–orbit torque (SOT) reader, whose structure consists of a SOT layer and a ferromagnetic layer. However, the heavy metal-based SOT reader has small output voltage and low signal-to-noise ratio (SNR) due to the limited spin Hall angle θSH (< 1) of heavy metals. In this Letter, we demonstrate the integration of BiSb topological insulator with strong inverse spin Hall effect into the SOT reader that can significantly improve the output voltage and SNR. First, we theoretically calculate the noises in a 20 × 20 nm2 BiSb-based SOT reader to establish the relationships between SNR and θSH at various bias currents. We then demonstrate proof-of-concept BiSb-based SOT readers using CoFe/MgO/BiSb stack, which show large output voltages up to 15 mV at an input current of 9.4 kA/cm2 at room temperature. We project a giant θSH = 61 for BiSb. Our work demonstrates the potential of BiSb for SOT reader beyond 4 Tb/in2 magnetic recording technology.

Verification of Estimated Output Signal-to-Noise Ratios From a Phase Inversion Technique Using a Simulated Hearing Aid.

The signal-to-noise ratio (SNR) for speech presented in background noise may vary after being processed by digital hearing aids with nonlinear signal processing algorithms, such as wide dynamic range compression (WDRC). A phase inversion technique has been previously developed to assess the output SNR of hearing aids. However, systematic validations of this technique have not been conducted. This study aims to validate the phase inversion technique. A simulated hearing aid with multichannel WDRC was implemented, from which the output SNRs, computed via shadow filtering, for connected speech in background noise were directly computed. The agreement between the shadow filter output SNRs and those estimated using the phase inversion technique for the same stimuli was utilized to validate the phase inversion technique. The background noise was 2- or 20-talker babble noise, and the speech stimuli were presented at SNRs of -10 to +10 dB at the input of the simulated hearing aid. The simulated hearing aid was configured to provide amplification for four representative audiograms, and the WDRC was set to be fast or slow acting. To investigate the effects of additive noise, independent of the presented noise stimulus, on the phase inversion estimated output SNR, the same simulated hearing aid was implemented with an additive Gaussian noise at its input (45 and 60 dB SPL). Results showed that the phase inversion technique could either overestimate or underestimate output SNR, depending on the test condition; the estimation errors tended to coincide with temporal landmarks, such as natural pauses between consecutive sentences or fricatives; and increasing the simulated noise led to poorer estimates of output SNR. Results imply that the accuracy of the phase inversion technique is dependent on the test conditions. Thus, the phase inversion technique should be used with caution, and its validity should be evaluated further.