Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory（EA） model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.

Analysis with binaural auditory model and experiment on the timbre of Ambisonics recording and reproduction

A scheme for analyzing the timbre in spatial sound with binaural auditory model is proposed and the Ambisonics is taken as an example for analysis. Ambisonics is a spatial sound system based on physical sound field reconstruction. The errors and timbre colorations in the final reconstructed sound field depend on the spatial aliasing errors on both the recording and reproducing stages of Ambisonics. The binaural loudness level spectra in Ambisonics recon- struction is calculated by using Moore＇s revised loudness model and then compared with the result of real sound source, so as to evaluate the timbre coloration in Ambisonics quantitatively. The results indicate that, in the case of ideal ＇independent signals, the high-frequency limit and radius of region without perceived timbre coloration increase with the order of Ambisonics. On the other hand, in the case of recording by microphone array, once the high-frequency limit of microphone array exceeds that of sound field reconstruction, array recording influences little on the binaural loudness level spectra and thus timbre in final reconstruction up to the high- frequency limit of reproduction. Based on the binaural auditory model analysis, a scheme for optimizing design of Ambisonics recording and reproduction is also suggested. The subjective experiment yields consistent results with those of binaural model, thus verifies the effectiveness of the model analysis.

A Sound Quality Objective Evaluation Method Based on Auditory Peripheral Simulation Model

Based on auditory peripheral simulation model, a new Sound Quality Objective Evaluation (SQOE) method is presented,which can be used to model and analyze the impacts of head, shoulder and other parts of human body on sound wave trans-mission.This method employs the artificial head technique, in which the head related transfer function was taken into account tothe outer ear simulation phase.First, a bionic artificial head was designed as the outer ear model with considering the outersound field in view of theory and physical explanations.Then the auditory peripheral simulation model was built, which mimicsthe physiological functions of the human hearing, simulating the acoustic signal transfer process and conversion mechanismsfrom the free field to the peripheral auditory system.Finally, performance comparison was made between the proposed SQOEmethod and ArtemiS software, and the verifications of subjective and objective related analysis were made.Results show thatthe proposed method was economical, simple, and with good evaluation quality.

An auditory periphery model for improving narrow-band noise recognition rate of underwater targets

The recognition rate of the auditory periphery features decreases when the model is used to identify underwater targets in practice. To solve this problem, an improved method based on Gammatone filter bank is proposed. Firstly, after the reason of the decreasing of the recognition results is analyzed, the mechanism of multichannel data acquisition in acoustic engineering may narrow down signal frequency range, which leads to time-frequency features distortion. Secondly, the Gammatone filter bank is implemented to simulate frequency decom- position characteristics of human ear basilar membrane. Since the class information of the underwater noise signal is mostly contained in low frequency range, the auditory features of the conventional model are interpolated and the channel number of the filter bank and the central frequency of each frequency band are adjusted accordingly to obtain a 27-dimensional feature vector of the narrow-band target signal. The adjusted model may reflect the target＇s time- frequency feature more precisely. Finally, the performance of the auditory features is tested by a Neural Network classifier. The experiment results show that the modified auditory model is more effective than the conventional ones. The major information contained in broadband signals is reserved and the classification ability for real targets is further enhanced. The recog- nition results are increased from 82.59% to 88.80%. The modified auditory features effectively improve the recognition rate for underwater target radiated noise signals.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

A computational model for assessment of speech intelligibility in informational masking

The existing auditory computational mod- els for evaluating speech intelligibility can only account for energetic masking, and the effect of informational masking is rarely described in these models. This study was aimed to make a computational model considering the mechanism of informational masking. Several psy- choacoustic experiments were conducted to test the ef- fect of informational masking on speech intelligibility by manipulating the number of masking talker, speech rate, and the similarity of F0 contour between target and masker. The results showed that the speech recep- tion threshold for the target increased as the F0 contours of the masker became more similar to that of the tar- get, suggesting that the difficulty in segregating the tar- get harmonics from the masker harmonics may underlie the informational masking effect. Based on these stud- ies, a new auditory computational model was made by inducing the auditory function of harmonic extraction to the traditional model of speech intelligibility index （SII）, named as harmonic extraction （HF） model. The predictions of the HF model are highly consistent with the experimental results.