dB Signal-to-noise Ratio Research Articles

The Auditory Environment at Early Intervention Groups for Young Children With Hearing Loss: Signal to Noise Ratio, Background Noise, and Reverberation.

One important aspect in facilitating language access for children with hearing loss (HL) is the auditory environment. An optimal auditory environment is characterized by high signal to noise ratios (SNRs), low background noise levels, and low reverberation times. In this study, the authors describe the auditory environment of early intervention groups specifically equipped for young children with HL. Seven early intervention groups for children with HL were included in the study. A total of 26 young children (22 to 46 months) visiting those groups participated. Language Environmental Analysis recorders were used to record all sounds around a child during one group visit. The recordings were analyzed to estimate SNR levels and background noise levels during the intervention groups. The unoccupied noise levels and reverberation times were measured in the unoccupied room either directly before or after the group visit. The average SNR encountered by the children in the intervention groups was +13 dB SNR. The detected speech of the attending professionals achieved the +15 dB SNR recommended by the American Speech-Language-Hearing Association in approximately 42% of the time. The unoccupied noise levels were between 29 and 39 dBA, complying with acoustic norms for classroom environments (≤35 dBA, by ANSI/ASA 12.60-2010 Part 1) for six out of seven groups. Reverberation time was between 0.3 and 0.6 sec for all groups, which complies to the acoustic norms for classroom environments for children without HL (0.6 or 0.7 sec, depending on the room size), while only one group complied to the stricter norm for children with HL (0.3 sec). The current findings show characteristics of the auditory environment of a setting that is specifically equipped and designed for groups of children with HL. Maintaining favorable SNRs seems to be the largest challenge to achieve within the constraints of an environment where young children gather, play, and learn. The results underscore the importance of staying attentive to keep spoken language accessible for children with HL in a group setting.

Characterization of A2G UAV communication channels under rician fading conditions

The variation in the k-factor value significantly influences the performance of unmanned aerial vehicle (UAV) air-to-ground point-to-point line of sight (A2G PTP LOS) communications over a Rician channel at 1,800 MHz using quadrature phase shift keying (QPSK) modulation and orthogonal frequency division multiplexing (OFDM) techniques. The research emphasizes the impact of the k-factor, which quantifies the dominance of the line-of-sight component over multipath scattering. The variation in the k-factor significantly influences UAV A2G PTP LOS communication performance for the empirical model (EM), as it involves precise measurements of the received power level in dBm from UAV to ground control station (GCS) across varying distances and altitudes. We introduce a method to compute the k-factor by assessing the ratio of the line-of-sight signal power to the multipath signal power, thereby enhancing channel modeling accuracy. Empirical analysis shows a strong correlation between bit error rate (BER) and signal-to-noise ratio (SNR) with differing k-factor values; a higher k-factor of 16.3 markedly improves performance, virtually eliminating errors at a 10 dB SNR, while a lower k-factor of 2.39 still shows significant errors at a 30 dB SNR. These results highlight the necessity of optimizing the k-factor in UAV A2G PTP LOS systems to ensure stable and reliable communication under diverse operational conditions.

Simulation of high signal-to-noise ratio resonant photodetector for homodyne measurement and its verification.

In this paper, two models for simulating the shot noise and electronic noise performances of resonant photodetectors designed for homodyne measurements are presented. One is based on a combination of a buffer and a low-noise amplifier, and the other is based on an operational amplifier. Through the comparisons between the numerical simulation results and the experimentally obtained data, excellent agreements are achieved, which show that the models provide a highly efficient guide for the development of a high signal-to-noise ratio (SNR) resonant photodetector. Furthermore, we demonstrate a high SNR resonant photodetector for homodyne measurements at the 147MHz optical sideband, achieving a 20.8 dB SNR of the shot noise to the electronic noise with a 2 mW optical signal input, utilizing a combination of a buffer and a low-noise amplifier. Concurrently, we have obtained another resonant photodetector at the 1.14GHz optical sideband, which exhibits a 13 dB SNR based on an operational amplifier.

Research on Fault Diagnosis Method for Marine Diesel Engines Based on Multi-Scale Attention Mechanism Transformer

In modern intelligent shipping, ensuring the stable and reliable technical condition of marine diesel engines is critical for safe and efficient vessel operations. Conventional fault diagnosis approaches and many existing Transformer-based methods often focus on single-scale features, potentially overlooking subtle fault indicators and reducing diagnostic accuracy under complex working conditions. To address these limitations, this paper proposes a Multi-Scale Attention Transformer (MSAT) model that integrates both high- and low-resolution attention mechanisms. This multi-scale strategy enhances the extraction of detailed and coarse-grained features, improving the model’s capacity to detect and characterize complex diesel engine faults. Additionally, an optimized Nadam optimizer is employed to refine convergence speed and accuracy, surpassing the Adam-based baseline by 0.71%. Rigorous testing on a publicly available diesel engine fault dataset demonstrates that the MSAT model achieves a diagnostic accuracy of 99.86% at a 60 dB signal-to-noise ratio (SNR), outperforming established models such as GRU and LSTM by more than 1%. Even under severe noise interference (0 dB SNR), the model maintains a high accuracy of 96.86%, highlighting its robustness and suitability for real-time monitoring in challenging marine environments. By quantitatively validating these improvements in diagnostic accuracy and noise resistance, this work offers a novel and effective solution for predictive maintenance and operational condition assessment of marine diesel engines, contributing to the reliability and safety of intelligent shipping systems.

Hybrid Multiple Access Techniques Performance Analysis Of Dynamic Resource Allocation

The Non-Orthogonal Multiple Access (NOMA) and the Orthogonal Frequency Division Multiple Access (OFDMA) are promising techniques for next-generation wireless systems. Although much attention in the literature considered these techniques, an effective combination between such systems as hybrid multiple access (HMA) needs more attention. For this reason, this paper devotes to proposing two different scenarios of HMA using multi-user OFDM (MU-OFDM) and power domain (PD) NOMA for downlink transmission to overcome the limitations of both Orthogonal Multiple Access (OMA) and NOMA. Due to the randomness of a wireless channel, different user grouping strategies and dynamic power allocation (DPA) strategies are employed to satisfy users’ requirements. The proposed systems give high flexibility utilizing bitrate allocation and user fairness. System results show superior performance to traditional OMA and NOMA systems. The achieved variety of fairness is helpful for the diversity of applications which is a principal requirement for beyond-fifth-generation (B5G) networks. The next step in this analysis is to enhance the proposed systems’ spectral efficiency (SE) by introducing beamforming for massive Multiple-Input Multiple-Output (MIMO) systems. The bit error rate (BER) result of the proposed system achieves almost the same error floor with a benefit of approximately 10 dB signalto-noise ratio (SNR) when 90 resource blocks (RBs) are used.

How Does Deep Neural Network-Based Noise Reduction in Hearing Aids Impact Cochlear Implant Candidacy?

Adult hearing-impaired patients qualifying for cochlear implants typically exhibit less than 60% sentence recognition under the best hearing aid conditions, either in quiet or noisy environments, with speech and noise presented through a single speaker. This study examines the influence of deep neural network-based (DNN-based) noise reduction on cochlear implant evaluation. Speech perception was assessed using AzBio sentences in both quiet and noisy conditions (multi-talker babble) at 5 and 10 dB signal-to-noise ratios (SNRs) through one loudspeaker. Sentence recognition scores were measured for 10 hearing-impaired patients using three hearing aid programs: calm situation, speech in noise, and spheric speech in loud noise (DNN-based noise reduction). Speech perception results were compared to bench analyses comprising the phase inversion technique, employed to predict SNR improvement, and the Hearing-Aid Speech Perception Index (HASPI v2), utilized to predict speech intelligibility. The spheric speech in loud noise program improved speech perception by 20 to 32% points as compared to the calm situation program. Thus, DNN-based noise reduction can improve speech perception in noisy environments, potentially reducing the need for cochlear implants in some cases. The phase inversion method showed a 4-5 dB SNR improvement for the DNN-based noise reduction program compared to the other two programs. HASPI v2 predicted slightly better speech intelligibility than was measured in this study. DNN-based noise reduction might make it difficult for some patients with significant residual hearing to qualify for cochlear implantation, potentially delaying its adoption or eliminating the need for it entirely.

Performance Optimization of 5G–Satellite Integrated Networks for IoT Applications in Smart Cities: A Two-Ray Propagation Model Approach

The convergence of 5G terrestrial networks with satellite systems offers a revolutionary approach to achieving global, seamless connectivity, particularly for Internet of Things (IoT) applications in urban and rural settings. This paper investigates the implications of this 5G–satellite integrated network architecture, specifically through the application of the two-ray propagation model and the free-space path loss (FSPL) model. By simulating signal characteristics over varying distances, altitudes, and environmental parameters, we explore how factors such as transmitter height, satellite altitude, and frequency impact received power, path loss, channel capacity, and outage probability. The key findings indicate that received power decreases significantly with increasing distance, with notable oscillations in the two-ray model due to interference from ground reflections, particularly evident within the first 100 km. For example, at 50 km, a 300 km satellite altitude yields approximately −115 dBm in received power, while at 1000 km altitude, this power drops to around −136 dBm. Higher frequencies (e.g., 32 GHz) exhibit greater path loss than lower frequencies (e.g., 24 GHz), with a 5 dB difference observed at 1000 km, reinforcing the need for frequency considerations in long-range communication design. In terms of channel capacity, increasing bandwidth enhances achievable data rates but declines with distance due to diminishing received power. At 100 km, a 50 MHz bandwidth supports up to 4500 Mbps, while at 3000 km, capacity drops to around 300 Mbps. The outage probability analysis shows that higher signal-to-noise ratio (SNR) thresholds substantially increase the likelihood of communication failures, especially at distances exceeding 2000 km. For instance, at 3000 km, the outage probability for a 15 dB SNR threshold reaches approximately 25%, compared to less than 5% for a 5 dB threshold. These results underscore the critical trade-offs in designing 5G–satellite IoT networks, balancing bandwidth, frequency, SNR thresholds, and satellite altitudes for optimal performance across diverse IoT applications. The analysis provides valuable insights for enhancing connectivity and reliability in 5G–satellite integrated networks, especially in remote and underserved regions.

Signal Acquisition and Algorithm Design for Bioimpedance-Based Heart Rate Estimation from the Wrist

Background: Heart rate (HR) is a critical biomarker that provides insights into overall health, stress levels, and the autonomic nervous system. Pulse wave signals contain valuable information about the cardiovascular system and heart status. However, signal acquisition in wearables poses challenges, particularly when using electrical sensors, due to factors like the distance from the heart, body movement, and suboptimal electrode placement. Methods: Electrical bioimpedance (EBI) measurements using bipolar and tetrapolar electrode systems were employed for pulse wave signal acquisition from the wrist in both perpendicular and distal configurations. Signal preprocessing techniques, including baseline removal via Hankel matrix methods, normalization, cross-correlation, and peak detection, were applied to improve signal quality. This study describes the combination of sensor-level signal acquisition and processing for accurate wearable HR estimation. Results: The bipolar system was shown to produce larger ΔZ(t), while the tetrapolar system demonstrated higher sensitivity. Distal placement of the electrodes yielded greater ΔZ(t) (up to 0.231 Ω) when targeting both wrist arteries. Bandpass filtering resulted in a better signal-to-noise ratio (SNR), achieving 3.6 dB for the best bipolar setup and 4.8 dB for the tetrapolar setup, compared to 2.6 and 3.3 dB SNR, respectively, with the Savitzky–Golay filter. The custom HR estimation algorithm presented in this paper demonstrated improved accuracy over a reference method, achieving an average error of 1.8 beats per minute for the best bipolar setup, with a mean absolute percentage error (MAPE) of 8%. Conclusions: The analysis supports the feasibility of using bipolar electrode setups on the wrist and highlights the importance of electrode positioning relative to the arteries. The proposed signal processing method, featuring a preprocessing pipeline and HR estimation algorithm, provides a proof-of-concept demonstration for HR estimation from EBI signals acquired at the wrist.

Neural encoding for spatial release from informational masking and its correlation with behavioral metrics.

The central auditory system encompasses two primary functions: identification and localization. Spatial release from masking (SRM) highlights speech recognition in competing noise and improves the listening experience when a spatial cue is introduced between noise and target speech. This assessment focuses on the integrity of auditory function and holds clinical significance. However, infants or pre-lingual subjects sometimes provide less reliable results. This study investigates the value of cortical auditory evoked potentials (CAEPs) onset and acoustic change complex (ACC) as an objective measurement of SRM. Thirty normal-hearing young adults (11 males) were recruited. We found the spatial separation of signals and noise (±90° symmetrically) resulted in a signal-to-noise ratio (SNR) improvement of 9.00 ± 1.71 dB behaviorally. It significantly enhanced cortical processing at all SNR levels, shortened CAEP latencies, and increased amplitudes, resulting in a greater number of measurable peaks for ACC. SRM showed mild to moderate correlations with the differences between two conditions in CAEP measures. The regression model combining N1'-P2' amplitude at 5 dB SNR (R2 = 0.26), P1 amplitude at 0 dB SNR (R2 = 0.14), and P1 latency at -5 dB SNR (R2 = 0.15), explained 45.3% of the variance in SRM. Our study demonstrates that introducing spatial cues can improve speech perception and enhance central auditory processing in normal-hearing young adults. CAEPs may contribute to predictions about SRM and hold potential for practical application.NEW & NOTEWORTHY The neural encoding of spatial release from masking (SRM) can be observed in normal-hearing young adults. Spatial separation between target and masker improves speech perception in noise and enhances central auditory processing. The behavioral results showed mild-to-moderate correlations with electrophysiological measures, with acoustic change complex (ACC) amplitude being a better indicator than onset components. Cortical auditory evoked potentials (CAEPs) may contribute to predictions about spatial release from masking, especially when behavioral tests are less reliable.

Long-term follow-up of Bonebridge BCI 601 implantation in microtia patients with aural atresia: Acoustic and subjective benefits.

This study evaluated the long-term acoustic and subjective outcomes of Bonebridge bone conduction implant (BCI) 601 implantation in Taiwanese patients with microtia and aural atresia (AA). A total of 41 microtia patients (28 males and 13 females; 26 with bilateral AA and 15 with unilateral AA) who received Bonebridge BCI 601 implantation between December 2014 and March 2021 at Chang Gung Memorial Hospital, Linkou, Taiwan, were included in this retrospective study. Acoustic outcomes assessed included functional hearing gain (FHG), speech reception threshold (SRT), and word recognition score (WRS), were assessed. Subjective outcomes were assessed using the Chinese versions of four questionnaires: the Abbreviated Profile of Hearing Aid Benefit (APHAB); the Speech, Spatial and Qualities of Hearing Scale; the International Outcome Inventory for Hearing Aids; and the Satisfaction with Amplification in Daily Living. The mean age at the time of implantation was 18.9 years (range, 6.3-54.9), and the mean follow-up duration was 6.3 years (range, 2.8-9.1). The mean unaided air conduction pure tone average (PTA4) was 65.3 ± 8.8 decibels (dB) hearing level (HL) and the mean aided sound field PTA4 was 31.1 ± 9.1 dB HL, resulting in a FHG of 34.2 ± 11.7 dB HL ( p < 0.05). After Bonebridge implantation, improvements ( p < 0.05) in the mean SRT in quiet (from 58.3 ± 7.4 dB HL to 29.4 ± 7.0 dB HL), SRT in noise (from -1.4 ± 7.3 dB signal-to-noise ratio (SNR) to -9.6 ± 5.4 dB SNR), WRS in quiet (from 46.4 ± 26.9% to 93.8 ± 3.1%), and WRS in noise (from 46.7 ± 21.8% to 72.7 ± 19.3%) were found. Additionally, the bilateral AA group exhibited greater SRT and WRS improvements compared to the unilateral AA group ( p < 0.05). All mean subscale scores in the four questionnaires showed improvement after Bonebridge implantation, except for the mean aversiveness to sounds subscale score in the APHAB questionnaire. Bonebridge BCI 601 implantation provided long-term acoustic and subjective benefits for patients with microtia and AA, particularly those with bilateral AA.

Improvement Underwater Acoustic Signal De-Noising Based on Dual-Tree Complex Wavelet Transform

Underwater Acoustic signal denoising is encountering high demand due to the extensive use of acoustic in a lot of underwater applications. Underwater acoustic noise (UWAN) has a high effect on the quality of the acoustic signal therefore, it is always preferred to use a de-noising filter to remove it. In this paper, we propose a filter that utilizes a Complex wavelet transform (CWT) to remove UWAN and help improve the signal-to-noise ratio (SNR) of the detected acoustic signal. CWT is nearly shift-invariant and offers a good directionality in contrast to normal wavelet transform (DWT). The proposed method was tested using a real recorded UWAN for three depths from the Tigris River. The proposed method was compared with a more conveniently used discrete wavelet transform. The test included using Two signals: fixed frequency and linear modulation signal. De-noising was performed using a soft thresholding technique based on level-dependent threshold estimation. The proposed method showed supreme performance in terms of SNR and root mean square error (RMSE). When the input signal was 5.9 dB and -13.2 dB for SNR and RMSE respectively, the results were 10.9 dB for SNR and -15.7 dB for RMSE in the case of fixed frequency.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Development of 20-Talker Korean Sentence In Noise Test Lists

Purpose: Clinical speech recognition measures often present the sentences recorded with a single speaker and one rate of speech. Performances with single-talker recordings may not accurately represent the speech recognition abilities of the listeners in a multitalker communication situation. The present study aimed to construct and optimize the sentences recorded by 20 different talkers for sentence-in-noise recognition tests (20-talker Korean sentence-in-noise test, 20-talker K-SIN).Methods: Phases I and II were conducted in this study. In phase I (developmental phase), preliminary 720 sentences composed of 3 to 6 words were selected and recorded by twenty different talkers (10 male and 10 female). The recorded sentences were superimposed to generate a speech-shaped noise similar to the long-term average speech spectrum of the sentences. In phase II (optimization and formation of equally intelligible sentence lists), the psychometric functions of 30 normal-hearing listeners were obtained from the sentence-in-noise recognition scores at three different signal-to-noise ratios (SNRs) (-2, -4, and -7 dB). Based on these scores, the SNR required for 50% sentence intelligibility (SNR-50) and the slope at that point were derived from the psychometric function curves.Results: Before level adjustment, the median SNR-50 and slope were -4.12 dB SNR and 24.33%/dB over 720 sentences. Level adjustments were applied to homogenize the intelligibility of the sentences, resulting in 508 sentences remained. Out of 508 sentences, 320 sentences were used to construct 1 practice list and 15 test lists, wherein 20 sentences in each list were spoken by each of the 20 different talkers.Conclusion: The 20-talker K-SIN sentences can be used for high-variability speech-in-noise recognition test to better reflect the multitalker communication abilities of listeners.

Distributed fiber optic sensing based on multipath data compression fusion

In conventional distributed sensing systems, the sampling rate exceeds the requisite sampling rate for system spatial resolution due to direct sampling of electrical signals. Furthermore, unrelated sensing paths exhibit distinct coherent fading points. Through the application of sampling point compression and multipath fusion, this study effectively resolves issues such as coherent fading and low signal-to-noise ratio (SNR) commonly encountered in single-path distributed sensing systems. In the initial stage, within two independent single-mode optical fiber structures, a consistent rotation of data along different fiber paths is performed. Subsequently, a segmentation compression and fusion method is applied. Experimental results demonstrate effective suppression of coherent fading along the sensing paths, an improvement of 9 dB in SNR, unaffected spatial resolution, and a significant reduction in data processing load for subsequent processing modules. This method can be widely applied in distributed sensing systems that allow for multipath division.

Noise modelling and mitigation for broadband in‐door power line communication systems

AbstractCommunication systems are greatly hampered by many disruptive noises in powerline communication systems (PLC), which come with strong interference, resulting in the malfunction of PLC systems. Hence, there is a need to model noise and its effect on communication systems. This paper presents noise modelling and mitigation techniques for indoor broadband powerline communication systems. To model the PLC noise, frequency domain measurements employing the GSP‐930 spectrum analyser were carried out to determine the noise frequency response in the frequency range of 1–30 MHz. The results obtained were plotted. While the analytical model for the noise model is presented, furthermore, noise mitigation techniques for multiple input multiple output PLC (MIMO‐PLC) systems in the form of spatial modulation PLC systems have been proposed. The SM‐PLC system employs the indices of the individual transmit lines to increase the data rate, as opposed to the traditional MIMO‐PLC systems, where the symbol to be transmitted is transmitted by duplicating the symbol across all lines. The proposed system uses the maximum likelihood (ML) detector at the receiver to obtain estimates of the transmitted symbols. The simulation results of the SM‐PLC system are compared with the already existing MIMO‐PLC system and show a significant improvement of ≈6 dB and 5.2 dB in signal‐to‐noise ratio (SNR) at a bit error rate of 10(−5) for spectral efficiencies of 4 bits per channel use (bpcu) and 6 bpcu, respectively. On comparison of the SM‐PLC system having a combination of additive white Gaussian noise and impulse noise at the receiver, the SM‐PLC system outperformed the traditional MIMO‐PLC by 3.5 and 3.8 dB in SNR for 4 and 6 bpcu, respectively.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals

Background Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients’ active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. Methods This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. Results The results indicate that the Modified Hodges detector – a simplified version of the threshold-based Hodges detector introduced in the current study – was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Conclusions Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.

Development and validation of words in babble test in the modern Greek language

Abstract Purpose: The aim of this paper is to present and describe the development and validation of new Words in the Babble test in modern Greek. Materials and Methods: Four 50-word phonemically balanced lists in babble noise were administered to 44 normal hearing subjects and 53 patients with hearing loss (HL) ears with mild and moderate sensorineural HL (SNHL). The word lists were monoaurally presented (i.e., separately for the right and left ears) at 11 signal-to-noise (babble) intensity levels, from −12 to +12 dB. The signal remained constant at 65 dB for all participants with normal hearing and at 20 dB SL for participants with SNHL, whereas the multitalker babble stimulus varied according to the desired signal-to-noise ratio (SNR). Results: The results showed that word recognition scores were negatively affected by HL and were negatively related to the degree of HL. There was a statistically significant difference for all scores in all SNRs, both between participants with normal hearing and those with mild HL as well as between participants with mild and moderate HL, except for −3 dB SNR and −6 dB SNR between mild and moderate HL. In addition, there were no statistically significant differences between the right and left ears in any of the SNRs. Conclusions: The existing literature suggests that the use of monosyllabic words produces recognition performance in multitalker babble tests, as observed in languages such as Spanish, Estonian, and Arabic, whereas the present study provides evidence that a similar effect is produced when disyllabic words are used.

Effect of Digital Noise Reduction in Hearing Aids on Speech Intelligibility in Both Quiet and Noisy Environments.

Digital noise reduction (DNR) minimizes the effect of noise on speech signals by continuously monitoring frequency bands in the presence of noise. In the present study, we explored the effect of DNR technology on speech intelligibility in individuals using hearing aids (HAs) and investigated implications for daily use. Eighteen participants with bilateral moderate sensorineural hearing loss (aged 16-45 years) were included. Bilateral receiver-in-the-ear HAs were fitted in the participants. The adaptive and nonadaptive (with a signal-to-noise ratio (SNR) of +5 and -5 dB, respectively) Turkish matrix sentence test (TURMatrix) in noise and free-field hearing assessments, including hearing thresholds with hearing aids, speech recognition thresholds (SRT), and speech discrimination scores, were conducted in two different conditions: HA in the DNR-on and DNR-off conditions. No significant difference was observed between free-field hearing assessments with the HA in the DNR-off and DNR-on conditions (P > 0.05). Furthermore, the adaptive and nonadaptive TURMatrix revealed significant differences between the scores under the DNR-on and DNR-off conditions (P < 0.05). Nevertheless, under the DNR-on condition, there was no correlation between free-field hearing assessments with HA and TURMatrix results (P > 0.05). However, a significant correlation was observed between SRT scores with HA and TURMatrix scores (adaptive and nonadaptive, +5 and -5 dB SNR, respectively) under the DNR-off condition (P < 0.05). Our study findings suggest that DNR can improve speech intelligibility in noisy environments. Therefore, DNR can enhance an individual's auditory comfort by improving their capacity to grasp speech in background noise.

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation.

Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods-LMS, RLS and PNLMS-DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.

A systematic investigation of detectors for low signal-to-noise ratio EMG signals.

Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown. This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy. The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges. Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.