Auditory Model Research Articles

Neural rate difference model can account for lateralization of high-frequency stimuli.

Lateralization of complex high-frequency sounds is conveyed by interaural level differences (ILDs) and interaural time differences (ITDs) in the envelope. In this work, the authors constructed an auditory model and simulate data from three previous behavioral studies obtained with, in total, over 1000 different amplitude-modulated stimuli. The authors combine a well-established auditory periphery model with a functional count-comparison model for binaural excitatory-inhibitory (EI) interaction. After parameter optimization of the EI-model stage, the hemispheric rate-difference between pairs of EI-model neurons relates linearly with the extent of laterality in human listeners. If a certain ILD and a certain envelope ITD each cause a similar extent of laterality, they also produce a similar rate difference in the same model neurons. After parameter optimization, the model accounts for 95.7% of the variance in the largest dataset, in which amplitude modulation depth, rate of modulation, modulation exponent, ILD, and envelope ITD were varied. The model also accounts for 83% of the variances in each of the other two datasets using the same EI model parameters.

RETRACTED ARTICLE: Advancing an in-memory computing for a multi-accent real-time voice frequency recognition modeling: a comprehensive study of models & mechanism

In this age of pervasive computing, numerous scientific accomplishments, such as artificial intelligence and machine learning [ML], have conveyed exciting uprisings to human civilization, which highlights the prospect to shape superior tools and solutions to aid to discourse some of the world’s utmost persistent challenges. Coding of English-dialectal dialogue recognition that has numerous datasets turn into a worthy preparatory point. Due to the nature of established records, there is an enormous sum of auditory features that can instantaneously distress the communication signals. These aspects comprise orator transformations, channel spins, contextual and reverberant noise, etc. In the initial step of communication anticipation, input dialog frequencies are administered by a front-end to offer a torrent of audio feature trajectories or interpretations. In projected scheme, the mined reflection classification is served into a decoder to distinguish the furthermost probable term disarray. The aural model signifies the auditory understanding of function by which a reflection categorization can be plotted to a system of sub-word divisions. This study has engrossed on revision and adaptive preparation of auditory models.

Efforts to Improve The Ability of Self Efficacy Students with The AIR Learning Model

The process of observation and interview with one of the teachers of SMP Islamiyah Weru is the background of this research by producing information that found difficulties and self-efficacy of students in solving some of the problems given by the teacher. This study aims to improve the ability of self-efficacy in students. Quantitative method, the type of research uses a quasi-experiment and the design used is nonequivalent control group. Application of the Auditory learning model, Intellectually, Repetition (AIR) contributes to improving the ability of self-efficacy with the results obtained namely the difference in the increase in self-efficacy in students whose learning uses the AIR learning model with Expository learning models with the average N-Gain self-efficacy of classroom students Experiment is higher than the control class so it can be concluded that the increase in students' self-efficacy in the experimental class is better than in the control class

Assessing the perceived reverberation in different rooms for a set of musical instrument sounds.

Previous research has shown that the perceived reverberation in a room, or reverberance, depends on the sound source that is being listened to. In a study by Osses Vecchi, Kohlrausch, Lachenmayr, and Mommertz [(2017). J. Acoust. Soc. Am. 141(4), EL381-EL387], reverberance estimates obtained from an auditory model for 23 musical instrument sounds in 8 rooms predicted a sound-source dependency. As a follow-up to that study, a listening experiment with 24 participants was conducted using a subset of the original sounds with the purpose of mapping each test sound onto a reverberance scale. Consistent with the literature, the experimental reverberance estimates were significantly dependent on the instrument sound being listened to, but on the top of that, the estimates were significantly correlated with simulated reverberance estimates for the test stimuli as well as for the previously reported long-duration sounds.

0293 Sleep Deprivation Affects the Acoustic Properties of Human Speech

Abstract Introduction Lack of sleep drastically affects many aspects of human behavior. The early detection of sleepiness is thus a major challenge for health and security reasons. Here we investigated the effect of sleep deprivation on the acoustic properties of human speech. Methods Twenty-four participants were sleep deprived for two days (two successive nights with only 3 hours of sleep). They were recorded reading a short text aloud before and after sleep deprivation. An auditory model, based on spectro-temporal modulations, was used to analyse the acoustic properties of their speech and served as a front-end to machine-learning classifiers. Results Results showed that sleepiness could be accurately detected with individually-trained classifiers. However,we were not able to fit a generic classifier for all participants. As we relied on an auditory-inspired model,we could identify and interpret the acoustic features impacted by sleep deprivation. Again,no simple diagnostic feature could be easily identified in the group- level analyses of the speech signals. We therefore developed a novel probing method, combining signal detection theory and noise activation of the classifier, to understand what made the classifier successful for each participant. This led to a diagnostic map for each participant, specifying which frequency region and modulation rates were impacted by sleep deprivation for this particular individual Conclusion In addition to suggesting a practical machine learning algorithm to detect sleep deprivation, combining our probing method with considerations about voice production could help uncover the physiological impact of sleep deprivation at the level of each individual. Support

Activity-Dependent Neurodegeneration and Neuroplasticity of Auditory Neurons Following Conductive Hearing Loss in Adult Mice.

We examined the functional and structural changes of auditory neurons (ANs) in adult mice after conductive hearing loss (CHL). Earplugs (EPs) were bilaterally inserted in male 8-week-old mice for 4weeks [EP(+) group] and subsequently removed for 4weeks [EP(+/-) group]. We examined the control mice [EP(-) group] with no EPs inserted at 12weeks. The auditory brainstem response (ABR) was measured to determine the cochlear function before and after EP insertion, after EP removal, and at 4weeks following EP removal. We examined the cochleae for hair cell (HC) and spiral ganglion neuron survival, synaptic and neural properties, and AN myelination. There was a significant elevation of the ABR threshold across all tested frequencies after EP insertion. After removing the occlusion, these threshold shifts were fully recovered. Compared with the EP(-) mice, the EP(+) mice showed a significant decrease in the ABR peak 1 amplitude and a significantly prolonged latency at all tested frequencies. There was no significant effect of auditory deprivation on the survival of HCs and ANs. Conversely, auditory deprivation caused significant damage to the synapses and myelin and a significant decrease in the AN size. Although functional changes in the ABR amplitude and latency did not fully recover in the EP(+/-) mice, almost all anatomical changes were fully recovered in the EP(+/-) mice; however, cochlear synapses only showed partial recovery. These results suggest that auditory activities are required to maintain peripheral auditory synapses and myelination in adults. The auditory deprivation model allows for assessment of the mechanisms of synaptopathy and demyelination in the auditory periphery, and synaptic and myelin regeneration in sensorineural hearing loss.

Neuroestrogen synthesis modifies neural representations of learned song without altering vocal imitation in developing songbirds

Birdsong learning, like human speech, depends on the early memorization of auditory models, yet how initial auditory experiences are formed and consolidated is unclear. In songbirds, a putative cortical locus is the caudomedial nidopallium (NCM), and one mechanism to facilitate auditory consolidation is 17β-estradiol (E2), which is associated with human speech-language development, and is abundant in both NCM and human temporal cortex. Circulating and NCM E2 levels are dynamic during learning, suggesting E2’s involvement in encoding recent auditory experiences. Therefore, we tested this hypothesis in juvenile male songbirds using a comprehensive assessment of neuroanatomy, behavior, and neurophysiology. First, we found that brain aromatase expression, and thus the capacity to synthesize neuroestrogens, remains high in the auditory cortex throughout development. Further, while systemic estrogen synthesis blockade suppressed juvenile song production, neither systemic nor unilateral E2 synthesis inhibition in NCM disrupted eventual song imitation. Surprisingly, early life neuroestrogen synthesis blockade in NCM enhanced the neural representations of both the birds’ own song and the tutor song in NCM and a downstream sensorimotor region, HVC, respectively. Taken together, these findings indicate that E2 plays a multifaceted role during development, and that, contrary to prediction, tutor song memorization is unimpaired by unilateral estrogen synthesis blockade in the auditory cortex.

Detection, thresholds of human echolocation in static situations for distance, pitch, loudness and sharpness

Human echolocation describes how people, often blind, use reflected sounds to obtain information about their ambient world. Using auditory models for three perceptual variables, loudness, pitch and one aspect of timbre, namely sharpness, we determined how these variables can make people detect objects by echolocation. We used acoustic recordings and the resulting perceptual data from a previous study with stationary situations, as input to our analysis. One part of the analysis was on the physical room acoustics of the sounds, i.e. sound pressure level, autocorrelation and spectral centroid. In a second part we used auditory models to analyze echolocation resulting from the perceptual variables loudness, pitch and sharpness. Based on these results, a third part was the calculation of psychophysical thresholds with a non-parametric method for detecting a reflecting object with constant physical size for distance, loudness, pitch and sharpness. Difference thresholds were calculated for the psychophysical variables, since a 2-Alternative-Forced-Choice Paradigm had originally been used. We determined (1) detection thresholds based on repetition pitch, loudness and sharpness varied and their dependency on room acoustics and type of sound stimuli. We found (2) that repetition pitch was useful for detection at shorter distances and was determined from the peaks in the temporal profile of the autocorrelation function, (3) loudness at shorter distances provides echolocation information, (4) at longer distances, timbre aspects, such as sharpness, might be used to detect objects. (5) It is suggested that blind persons may detect objects at lower values for loudness, pitch strength and sharpness and at further distances than sighted persons. We also discuss the auditory model approach. Autocorrelation was assumed as a proper measure for pitch, but we ask whether a mechanism based on strobe integration is a viable possibility.

Abstract TP259: Mission: Lifeline Stroke Education Campaign Drives Systems of Care Improvement in North Dakota

Introduction: The AHA Mission: Lifeline Stroke program is a 3-year initiative in North Dakota which aims to improve statewide stroke systems of care. Due to complexities in recognizing and treating stroke patients, effectively educating prehospital and hospital health care providers on guideline-based assessments and treatment methods was identified as an interventional opportunity. Purpose: The purpose of the education campaign was to increase early recognition of stroke symptoms and expand application of guideline-based triage and treatment protocols thereby improving early recognition, treatment times and outcomes. Methods: The planning group considered individual learning style preferences by using the Visual, Auditory, Read/Write, and Kinesthetic (VARK) model which divides learners into these four categories. Beginning in January 2017, the following education offerings were deployed to address each style: in person lectures, conferences, workshops, stroke simulation trainings, online courses, webinars and a stroke certification course. Topics included: stroke symptom screening, severity scoring, LVO criteria, transport protocols, statewide stroke treatment guidelines and IV alteplase administration. Systems improvement data was correlated from the GWTG-Stroke registry in which all 6 ND tertiary hospitals and 32 of 36 critical access hospitals participate. Results: In the ND critical access hospital cohort the percentage of stroke patients who received brain imaging within 25 minutes of arrival increased from 57.1% in 2016 to 72% in 2018, and those who arrived at the hospital within 2 hours of LKW and received IV alteplase within 3 hours increased from 56.0% in 2016 to 64.3% in 2018. While in the all ND hospital cohort the percentage of acute ischemic stroke patients receiving IV alteplase within 60 minutes of arrival increased from 66.2% in 2016 to 78.2% in 2018. Conclusions: A multifaceted stroke education campaign can be an effective way to improve statewide stroke triage and treatment times. Continuing educational efforts are needed to maintain these gains and elicit further advancements. Additional studies are needed to determine if an improvement in triage and treatment times corresponds with reduced mortality and morbidity.

Optimizing Speech Recognition Using a Computational Model of Human Hearing: Effect of Noise Type and Efferent Time Constants

Physiological and psychophysical methods allow for an extended investigation of ascending (afferent) neural pathways from the ear to the brain in mammals, and their role in enhancing signals in noise. However, there is increased interest in descending (efferent) neural fibers in the mammalian auditory pathway. This efferent pathway operates via the olivocochlear system, modifying auditory processing by cochlear innervation and enhancing human ability to detect sounds in noisy backgrounds. Effective speech intelligibility may depend on a complex interaction between efferent time-constants and types of background noise. In this study, an auditory model with efferent-inspired processing provided the front-end to an automatic-speech-recognition system (ASR), used as a tool to evaluate speech recognition with changes in time-constants (50 to 2000 ms) and background noise type (unmodulated and modulated noise). With efferent activation, maximal speech recognition improvement (for both noise types) occurred for signal-to-noise ratios around 10 dB, characteristic of real-world speech-listening situations. Net speech improvement due to efferent activation (NSIEA) was smaller in modulated noise than in unmodulated noise. For unmodulated noise, NSIEA increased with increasing time-constant. For modulated noise, NSIEA increased for time-constants up to 200 ms but remained similar for longer time-constants, consistent with speech-envelope modulation times important to speech recognition in modulated noise. The model improves our understanding of the complex interactions involved in speech recognition in noise, and could be used to simulate the difficulties of speech perception in noise as a consequence of different types of hearing loss.

HRTF Selection by Anthropometric Regression for Improving Horizontal Localization Accuracy

This work focuses on objective Head-Related Transfer Function (HRTF) selection from anthropometric measurements for minimizing localization error in the frontal half of the horizontal plane. Localization predictions for every pair of 90 subjects in the HUTUBS database are first computed through an interaural time difference-based auditory model, and an error metric based on the predicted lateral error is derived. A multiple stepwise linear regression model for predicting error from inter-subject anthropometric differences is then built on a subset of subjects and evaluated on a complementary test set. Results show that by using just three anthropometric parameters of the head and torso (head width, head depth, and shoulder circumference) the model is able to identify non-individual HRTFs whose predicted horizontal localization error generally lies below the localization blur. When using a lower number of anthropometric parameters, this result is not guaranteed.

Assessing the effects of hearing-aid compression on auditory spectral and temporal resolution using an auditory modeling framework

Assessing the effects of hearing-aid compression on auditory spectral and temporal resolution using an auditory modeling framework

Limitations of the Envelope Difference Index as a Metric for Nonlinear Distortion in Hearing Aids.

The envelope difference index (EDI) compares the envelopes of two signals. It has been used to measure nonlinear distortion in hearing aids, but it also responds to linear processing. This article compares linear and nonlinear processing effects on the EDI. The EDI for spectral tilt and peak clipping distortion is computed to illustrate the effects of linear and nonlinear signal modifications. The EDI for wide dynamic-range compression is then compared with that obtained for linear amplification for a set of standard audiograms to show the expected range of EDI values for linear and nonlinear hearing aid processing. The EDI for hearing aid amplification and compression is also compared with a measure of time-frequency envelope modulation distortion for the same conditions. The EDI is shown to be as sensitive to linear amplification as it is to nonlinear processing. The EDI values for spectral tilt can exceed those for peak clipping, and the EDI values for linear amplification exceed those for wide dynamic-range compression for four of the nine audiograms considered. The agreement of the EDI with a nonlinear envelope distortion measure is shown to depend on the long-term spectra of the signals being compared when computing the EDI. The accuracy of the EDI as an indicator of nonlinear distortion for sentence materials can be improved by equalizing the long-term spectrum of the processed signal to match that of the unprocessed input. However, the EDI does not have a clear interpretation because of the confound between linear and nonlinear processing effects and the lack of an auditory model in calculating the signal differences.

Robust speaker recognition based on biologically inspired features

This paper proposes two speech parameterisation techniques for noise-robust speaker recognition: the normalised gammachirp cepstral coefficients (NGCC) and the perceptual linear predictive normalised gammachirp (PLPnGc). These techniques employ a biologically inspired auditory model that simulates the cochlea spectral behaviour. In an automatic speaker recognition (ASR) system, we consider the Gaussian mixture model-universal background model (GMM-UBM) for speaker modelling. The performances are evaluated in clean and noisy environments using Timit, Aurora, and Demand databases. The experimental results in noisy environments showed that the biologically inspired feature extraction techniques give a better recognition rate than state-of-the-art methods.

달팽이관 필터뱅크 및 ZCPA 청각모델에 기반한 두 귀의 시간차 추정기법

This study presents a method for estimating interaural time difference (ITD) by modeling the human auditory system. The proposed model simulated the response of the human inner ear by signal processing. The response of the cochlea consists of the mechanical vibration of the basilar membrane and the neural transduction of the inner hair cells. The traveling waves on the basilar membrane were represented by a cascade of digital filter sections that function as a series of bandpass filters. We mimicked the neural firing pattern from the inner hair cells by the zero-crossings with peak amplitudes (ZCPA) auditory model in which frequency information of the signal is obtained by zero-crossing intervals. Zero-crossings have been used to find noise-robust speech features. The ITD between two channels was estimated by obtaining the ZCPA pattern of each channel and then calculating an interaural cross-correlogram by center frequencies between the two patterns. We examined the feasibility of the proposed method by simulation, in which binaural speech signals were mixed with normally distributed noise. As a result, the proposed method was able to provide an accurate estimate of ITDs and was robust to Gaussian noise.

Exploring the Usage of Vak Model Based Tasks to Teach Speaking Skills to The Engineering Professionals

Learner differences in learning language skills differ from one individual to another. Different learning styles (LS) of the learners give the facilitator a tough challenge to meet the expectations of the learners. Coming to Engineering professionals it is known that the learners give priority to the core subjects and a little effort for the language skill. Therefore, it is a challenging task for the language teachers to grab learners’ attention and make learning effective. This paper attempts to explore strategies to overcome the challenges of the language teachers. It specifically looks at the Visual, Auditory and Kinesthetic (VAK) model based tasks as an effective strategy in motivating learners to develop their speaking skills regardless of the learners inattentive behaviors. VAK provides a varied range of tasks in the language class which enable the learners to focus in the tasks and increase their participation in the classroom.

Single-ended Speech Quality Evaluation Using Linear Combination of the Quality Score Estimates of Multi-instances Features

Background: In a single-ended speech quality evaluation, the measurement of Mean Opinion Score (MOS) is done objectively without the use of clean speech as a reference. In this work, multiple time-instances or multi-instances features using Multi-Resolution Auditory Model (MRAM) along with other relevant features such as Mel-Frequency Cepstral Coefficients (MFCC), and Line Spectral Frequencies (LSF) features are used for single-ended speech quality measurement. The Voice Activity Detection (VAD) algorithm separates the presence of speech-regions from silence in a speech signal. Methods: The multi-instances features are computed using MRAM, MFCC and LSF for different combinations of speech-regions to capture degradations due to multiple time-localized effects or the attacks of short-time transient distortions such as impulsive noise and their distinctions from plosive sounds in speech. These multi-instances features are used for Gaussian Mixture Model (GMM) MAPPING to compute the objective MOS values, corresponding to all multi-instances features. Results: The overall objective MOS estimation of the speech signal is calculated by averaging all the values of objective MOS corresponding to the different multi-instances features of a speech signal. The results in terms of Pearson’s Correlation Coefficients (PCC) and root mean square error (RMSE) between the subjective MOS and the estimated overall objective MOS of speech signals are computed and compared with International Telecommunication Union-telephony (ITU-T) Recommendation P.563 and recently published works on similar types of databases. Conclusion: The improved values of PCC and RMSE between the subjective and the estimated overall objective MOS show the efficacy of the approach.

Cochlear Mechanical Models Used in Automatic Speech Recognition Tasks

In this paper we show that its possible unify two theories that we can find in the state of the art related with human hearing, one of them related with human perceptual phenomenon and the another one related with cochlear mechanic’s models linear. The first of them has been used since decade 1980’s into Automatic Speech Recognition Systems (ASRs) with satisfactory results. Whereas the second has been used since decade 1950’s but never used for ASRs. Since the second is the inner functionality with respect to the first, we propose that is very important to have a study about the behavior of the cochlea models into ASR tasks and compare the results that we can obtain. Then we present an auditory signal processing model that has been proposed as an alternative to the traditional filter banks and LPC models for speech spectral analysis. The argument for such a model is that, because it is based on known properties of the human auditory model (i.e. a model of the cochlea mechanics), it is inherently a better representation of the relevant spectral information that either a traditional bank-filter or an LPC model. In this work we use two different models of the cochlea that they are based in the classic mechanical to analyze their behavior when they are employed for ASR tasks with two variants and two more equations related with the place theory proposed by Von Bekesy. Also, we propose an alternative solution for another model based in the fluid mechanical. One time that we analyzed the response of the cochlea with different linear mechanical models we extracted features for ASR tasks that follow the cochlea behavior described by these models. The results obtained demonstrate that our propose represents a real alternative to be considered for this kind of computational applications. We obtained 2% of higher performance that when we used MFCC parameters in major cases.In this paper we show that its possible unify two theories that we can find in the state of the art related with human hearing, one of them related with human perceptual phenomenon and the another one related with cochlear mechanic’s models linear. The first of them has been used since decade 1980’s into Automatic Speech Recognition Systems (ASRs) with satisfactory results. Whereas the second has been used since decade 1950’s but never used for ASRs. Since the second is the inner functionality with respect to the first, we propose that is very important to have a study about the behavior of the cochlea models into ASR tasks and compare the results that we can obtain. Then we present an auditory signal processing model that has been proposed as an alternative to the traditional filter banks and LPC models for speech spectral analysis. The argument for such a model is that, because it is based on known properties of the human auditory model (i.e. a model of the cochlea mechanics), it is inherently a better representation of the relevant spectral information that either a traditional bank-filter or an LPC model. In this work we use two different models of the cochlea that they are based in the classic mechanical to analyze their behavior when they are employed for ASR tasks with two variants and two more equations related with the place theory proposed by Von Bekesy. Also, we propose an alternative solution for another model based in the fluid mechanical. One time that we analyzed the response of the cochlea with different linear mechanical models we extracted features for ASR tasks that follow the cochlea behavior described by these models. The results obtained demonstrate that our propose represents a real alternative to be considered for this kind of computational applications. We obtained 2% of higher performance that when we used MFCC parameters in major cases.

An emergent deep developmental model for auditory learning

ABSTRACTSpeech recognition performance of the machine has been greatly improved using artificial intelligence. However, compared with the superior recognition ability of human auditory system, the machine still has some problems to deal with. Based on the existing physiological principle of human auditory system, this paper proposes a novel emergent auditory model. This model simulates each key part of the human auditory pathway with a deep developmental network (DDN). Furthermore, this model simulates the function of the superior colliculus in the thalamus, i.e., context integration, as an additional layer in the DDN. Mel-frequency cepstral coefficients (MFCC) are used to extract the speech signal features to be inputs of the DDN. This work is different from other previous models as we emphasise the mechanism that makes a system to develop its emergent representations from its operational experience, i.e., the internal unsupervised neurons of the DDN are utilised to depict the short contexts, and competitions among them afford an interpretation of how such internal neurons denote the different speech contexts when they are not supervised by the external world. Experimental results show the advantage of the proposed DNN compared to the state-of-the-art methods for the recognition accuracies of English words and phrases.

A neural ensemble correlation code for sound category identification.

Humans and other animals effortlessly identify natural sounds and categorize them into behaviorally relevant categories. Yet, the acoustic features and neural transformations that enable sound recognition and the formation of perceptual categories are largely unknown. Here, using multichannel neural recordings in the auditory midbrain of unanesthetized female rabbits, we first demonstrate that neural ensemble activity in the auditory midbrain displays highly structured correlations that vary with distinct natural sound stimuli. These stimulus-driven correlations can be used to accurately identify individual sounds using single-response trials, even when the sounds do not differ in their spectral content. Combining neural recordings and an auditory model, we then show how correlations between frequency-organized auditory channels can contribute to discrimination of not just individual sounds but sound categories. For both the model and neural data, spectral and temporal correlations achieved similar categorization performance and appear to contribute equally. Moreover, both the neural and model classifiers achieve their best task performance when they accumulate evidence over a time frame of approximately 1–2 seconds, mirroring human perceptual trends. These results together suggest that time-frequency correlations in sounds may be reflected in the correlations between auditory midbrain ensembles and that these correlations may play an important role in the identification and categorization of natural sounds.