Evaluation of a multi-channel algorithm for reducing transient sounds.

OBJECTIVE: The objective was to evaluate and select appropriate parameters for a multi-channel transient reduction (MCTR) algorithm for detecting and attenuating transient sounds in speech. DESIGN: In each trial, the same sentence was played twice. A transient sound was presented in both sentences, but its level varied across the two depending on whether or not it had been processed by the MCTR and on the "strength" of the processing. The participant indicated their preference for which one was better and by how much in terms of the balance between the annoyance produced by the transient and the audibility of the transient (they were told that the transient should still be audible). STUDY SAMPLE: Twenty English-speaking participants were tested, 10 with normal hearing and 10 with mild-to-moderate hearing-impairment. Frequency-dependent linear amplification was provided for the latter. RESULTS: The results for both participant groups indicated that sounds processed using the MCTR were preferred over the unprocessed sounds. For the hearing-impaired participants, the medium and strong settings of the MCTR were preferred over the weak setting. CONCLUSIONS: The medium and strong settings of the MCTR reduced the annoyance produced by the transients while maintaining their audibility.EPSRC RG7853

Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms: A deep recurrent neural network and spectral subtraction.

The effects on speech intelligibility and sound quality of two noise-reduction algorithms were compared: a deep recurrent neural network (RNN) and spectral subtraction (SS). The RNN was trained using sentences spoken by a large number of talkers with a variety of accents, presented in babble. Different talkers were used for testing. Participants with mild-to-moderate hearing loss were tested. Stimuli were given frequency-dependent linear amplification to compensate for the individual hearing losses. A paired-comparison procedure was used to compare all possible combinations of three conditions. The conditions were: speech in babble with no processing (NP) or processed using the RNN or SS. In each trial, the same sentence was played twice using two different conditions. The participants indicated which one was better and by how much in terms of speech intelligibility and (in separate blocks) sound quality. Processing using the RNN was significantly preferred over NP and over SS processing for both subjective intelligibility and sound quality, although the magnitude of the preferences was small. SS processing was not significantly preferred over NP for either subjective intelligibility or sound quality. Objective computational measures of speech intelligibility predicted better intelligibility for RNN than for SS or NP

Transient Noise Reduction Using a Deep Recurrent Neural Network: Effects on Subjective Speech Intelligibility and Listening Comfort.

A deep recurrent neural network (RNN) for reducing transient sounds was developed and its effects on subjective speech intelligibility and listening comfort were investigated. The RNN was trained using sentences spoken with different accents and corrupted by transient sounds, using the clean speech as the target. It was tested using sentences spoken by unseen talkers and corrupted by unseen transient sounds. A paired-comparison procedure was used to compare all possible combinations of three conditions for subjective speech intelligibility and listening comfort for two relative levels of the transients. The conditions were: no processing (NP); processing using the RNN; and processing using a multi-channel transient reduction method (MCTR). Ten participants with normal hearing and ten with mild-to-moderate hearing loss participated. For the latter, frequency-dependent linear amplification was applied to all stimuli to compensate for individual audibility losses. For the normal-hearing participants, processing using the RNN was significantly preferred over that for NP for subjective intelligibility and comfort, processing using the RNN was significantly preferred over that for MCTR for subjective intelligibility, and processing using the MCTR was significantly preferred over that for NP for comfort for the higher transient level only. For the hearing-impaired participants, processing using the RNN was significantly preferred over that for NP for both subjective intelligibility and comfort, processing using the RNN was significantly preferred over that for MCTR for comfort, and processing using the MCTR was significantly preferred over that for NP for comfort

Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants.

Speech-in-noise perception is a major problem for users of cochlear implants (CIs), especially with non-stationary background noise. Noise-reduction algorithms have produced benefits but relied on a priori information about the target speaker and/or background noise. A recurrent neural network (RNN) algorithm was developed for enhancing speech in non-stationary noise and its benefits were evaluated for speech perception, using both objective measures and experiments with CI simulations and CI users. The RNN was trained using speech from many talkers mixed with multi-talker or traffic noise recordings. Its performance was evaluated using speech from an unseen talker mixed with different noise recordings of the same class, either babble or traffic noise. Objective measures indicated benefits of using a recurrent over a feed-forward architecture, and predicted better speech intelligibility with than without the processing. The experimental results showed significantly improved intelligibility of speech in babble noise but not in traffic noise. CI subjects rated the processed stimuli as significantly better in terms of speech distortions, noise intrusiveness, and overall quality than unprocessed stimuli for both babble and traffic noise. These results extend previous findings for CI users to mostly unseen acoustic conditions with non-stationary noise

Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise

© 2020 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Auditory cortical activity entrains to speech rhythms and has been proposed as a mechanism for online speech processing. In particular, neural activity in the theta frequency band (4–8 ​Hz) tracks the onset of syllables which may aid the parsing of a speech stream. Similarly, cortical activity in the delta band (1–4 ​Hz) entrains to the onset of words in natural speech and has been found to encode both syntactic as well as semantic information. Such neural entrainment to speech rhythms is not merely an epiphenomenon of other neural processes, but plays a functional role in speech processing: modulating the neural entrainment through transcranial alternating current stimulation influences the speech-related neural activity and modulates the comprehension of degraded speech. However, the distinct functional contributions of the delta- and of the theta-band entrainment to the modulation of speech comprehension have not yet been investigated. Here we use transcranial alternating current stimulation with waveforms derived from the speech envelope and filtered in the delta and theta frequency bands to alter cortical entrainment in both bands separately. We find that transcranial alternating current stimulation in the theta band but not in the delta band impacts speech comprehension. Moreover, we find that transcranial alternating current stimulation with the theta-band portion of the speech envelope can improve speech-in-noise comprehension beyond sham stimulation. Our results show a distinct contribution of the theta- but not of the delta-band stimulation to the modulation of speech comprehension. In addition, our findings open up a potential avenue of enhancing the comprehension of speech in noise.Peer reviewe

Luteolin Reduced the Traumatic Brain Injury-Induced Memory Impairments in Rats: Attenuating Oxidative Stress and Dark Neurons of Hippocampus

Traumatic Brain Injury (TBI) is generally recognized as a major risk factor for memory impairments and Alzheimer’s disease (AD). In this experimental study, our aim was to investigate the ameliorating effects of luteolin (LUT) on the memory impairments, oxidative stress, and histopathological changes induced by TBI in rats. The adult male Wistar rats were randomly divided into six groups including: Control (Co), sham, TBI, TBI+LUT (10 mg/kg), TBI +LUT (25 mg/kg), TBI +LUT (50 mg/kg). To evaluate the protective effects of LUT on the memory of the rats, passive avoidance test using shuttle box was performed. Finally, the animals were anesthetized, and the brain tissues were removed and analyzed for oxidative stress parameters. Using histological methods, dark neuron production was also evaluated. There was a significant decrease in the latency time to enter the dark compartment in passive avoidance test in TBI animals. This latency time was significantly increased in TBI+LUT (25 mg/kg) and TBI+LUT (50 mg/kg) groups along with significant increases in superoxide dismutase and catalase activity in the hippocampal zone and a decrease in malondialdehyde (MDA). The number of dark neurons in the hippocampus decreased with all three doses of LUT. In the present study, LUT showed neuroprotective effects, improvement in learning and reduction in memory impairment induced by TBI in rats. Protection against oxidative stress might be a possible mechanism behind these effects. Further works are necessary to work out if LUT is potentially a suitable therapeutic candidate for neural disorders

Decoding speech information from EEG data with 4-, 7- and 11-month-old infants: Using convolutional neural network, mutual information-based and backward linear models.

BackgroundComputational models that successfully decode neural activity into speech are increasing in the adult literature, with convolutional neural networks (CNNs), backward linear models, and mutual information (MI) models all being applied to neural data in relation to speech input. This is not the case in the infant literature.New methodThree different computational models, two novel for infants, were applied to decode low-frequency speech envelope information. Previously-employed backward linear models were compared to novel CNN and MI-based models. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung or chanted nursery rhymes) presented by video with a female singer.ResultsEach model computed speech information for these nursery rhymes in two different low-frequency bands, delta and theta, thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band neural activity from 4 months of age, with two of three models also showing significant performance for theta-band activity. All models also demonstrated higher accuracy for the delta-band neural responses. None of the models showed developmental (age-related) effects.Comparisons with existing methodsThe data demonstrate that the choice of algorithm used to decode speech envelope information from neural activity in the infant brain determines the developmental conclusions that can be drawn.ConclusionsThe modelling shows that better understanding of the strengths and weaknesses of each modelling approach is fundamental to improving our understanding of how the human brain builds a language system

Effect of rigid vegetation on velocity distribution and bed topography in a meandering river with a sloping bank

In the present study, a physical model of a meandering river with the sloping bank was used to assess the effects of rigid vegetation on flow velocity distribution and bed scouring at the toe and bank slope of a meandering river. The experimental tests were carried out under non-vegetated condition as a reference and vegetated conditions with six different patterns. The results revealed that the flow velocity distribution and bed topography at each cross-section were considerably affected by vegetation density and planting patterns. For the vegetated condition, the longitudinal flow velocity component increased in the main channel, while it decreased in the bank zone when compared to the non-vegetated conditions. Additionally, in the presence of vegetation, the core of maximum velocity diverts toward the centerline of the flume, which reduces erosion risk. Furthermore, a comparison of the longitudinal velocity profiles under vegetated and non-vegetated conditions showed that the area occupied by the core of maximum velocity at the bend apex decreases up to 26%, notably for the conditions with double rows and hybrid patterns. The results of this study showed that the use of vegetation can be recommended to stabilize rivers’ bed and banks a green and cost-effective alternative to hard-engineering methods

Comparison of Behavioral Disorders, Compromise Behaviors and Academic Achievement of Exceptional Students in Especial Educational System and Integrated Educational System

Objective:The purpose of this study is to compare the educational progress and behavioral compromise of exceptional students in special educational system and integrated system in Hamedan province. Materials & Methods:This research is based on comparison between all the exceptional students who divided in two groups of blind and deaf in three levels in clouding primary, guidance and high school students. 40 students from integrated and 40 students from especial educational system with cluster sampling method were selected the rate of P is considered as q .The rate of p was considered equal 0.5 and the rate of q equal 0.5 . Then for gathering data a questionnaire based on Akhnbakh experience was used . The data were analysed in &alpha= 0.05 level with independent T test and multivariate analysis of MANOVA variance . Results: The result showed that there is a significant difference between the amount of educational progress , compromise behaviors , amount and kind of abnormal behaviors of exceptional students in integrated educational system with special educational system . Also the rate of educational progress and compromise behaviors is better in integrated system than especial educational system . Moreover disordered behaviors of these students are less than their counterparts in special educational system . Conclusion: in general integrated educational system in comparison with especial educational system is most successful in field of educational progress ,compromise behavior and abnormal behavior in exceptional students (blind and deaf)