Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions

Eigen-frequency, compared with mode shape and damping, is a more practical and reliable dynamic feature to portray structural damage. The frequency contour-line method relying on this feature is a representative method to identify damage in beam-type structures. Although this method has been increasingly applied in the area of damage identification, it has two significant deficiencies: inefficiency in establishing the eigen-frequency panorama; and incompetence to identify cracks in noisy conditions, considerably impairing the effectiveness in identifying structural damage. To overcome these deficiencies, a novel method, termed the frequency contour-strip method, is developed for the first time. This method is derived by extending the frequency contour line of 1D to frequency contour strip of 2D. The advantages of the frequency contour-strip method are twofold: (i) it uses the isosurface function to instantly produce the eigen-frequency panorama with a computational efficiency several orders of magnitude higher than that of the frequency contour-line method; and (ii) it can accommodate the effect of random noise on damage identification, thereby thoroughly overcoming the deficiencies of the frequency contour-line method. With these merits, the frequency contour-strip method can characterize damage in beam-type structures with more efficiency, greater accuracy, and stronger robustness against noise. The proof of concept of the proposed method is performed on an analytical model of a Timoshenko beam bearing a crack and the effectiveness of the method is experimentally validated via crack identification in a steel beam.

Phase-shifting digital holography with an unknown reference beam

In digital holography, errors of the reference field degrade the quality of the reconstructed object field. In this paper, we propose an effective method in phase-shifting digital holography in which the reference field does not need to be known and perfect. The unknown complex amplitudes of both reference and object fields are derived simultaneously. The method employs only five digital holograms and a single execution of a phase retrieval algorithm. So, the required measurements and algorithm executions in this method are fewer than those in other methods; it suggests a simpler and faster method. The effectiveness of the suggested method is indicated by simulation, under noise-free and noisy conditions. Moreover, the capability of the method to extract full information about the phase singularities in both fields is demonstrated.

Responses in left inferior frontal gyrus are altered for speech-in-noise processing, but not for clear speech in autism

People with an autism spectrum disorder (ASD) often have difficulties with recognising what another person is saying in noisy conditions such as in a crowded classroom or a restaurant. The underlying neural mechanisms of this speech perception difficulty are unclear. In typically developed individuals, three cerebral cortex regions are particularly related to speech-in-noise perception: The left inferior frontal gyrus (IFG), the right insula and the left inferior parietal lobule (IPL) (Alain et al., HBM, 2018). Here we tested whether responses in these cerebral cortex regions are altered in speech-in-noise perception in ASD. 17 adults with ASD and 17 typically developing controls (matched pairwise on age, sex and IQ) performed an auditory-only speech recognition task during functional magnetic resonance imaging (fMRI). Speech was presented either with noise (noise condition) or without noise (no noise condition, i.e., clear speech). In the left IFG, blood-oxygenation-level-dependent (BOLD) responses were higher in the control compared to the ASD group for recognising speech-in-noise in comparison to clear speech. In the right insula and left IPL both groups had similar response magnitudes for the contrast between speech-in-noise and clear speech recognition. Additionally, we replicated previous findings that BOLD responses in speech-related and auditory brain regions (including bilateral superior temporal sulcus and Heschl’s gyrus) for clear speech were similar in both groups. Our findings show that in ASD, the processing of speech is particularly reduced under noisy conditions in the left IFG. Dysfunction of the IFG might be important in explaining restricted speech comprehension in noisy environments in ASD.

Comprehension under noise in L1 and L2: Acoustic entrainment of speech envelope

Listeners regularly comprehend continuous speech despite noisy conditions. Previous studies show that cortical entrainment to speech degrades under noise, predicts comprehension, and increases for non-native listeners. We test the hypothesis that listeners similarly increase cortical entrainment for both L2 and noisy L1 speech, after controlling for comprehension. Twenty-four Chinese-English bilinguals underwent EEG while listening to one hour of an audiobook, mixed with three levels of noise, in Mandarin and English and answered comprehension questions. We estimated cortical entrainment for one-minute tracks using the multivariate temporal response function (mTRF). Contrary to our prediction, entrainment of the L2 was significantly lower than L1, while L1 entrainment significantly increased when speech was masked by noise without reducing comprehension. However, greater L2 proficiency was positively associated with greater entrainment. We discuss how studies of entrainment relating to noise and bilingualism might be reconciled with an approach focused on exerted rather than demanded effort.

Robust speaker verification by combining MFCC and entrocy in noisy conditions

Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robust speaker recognition, which we mainly employ to support MFCC coefficients in noisy environments. Entrocy is the fourier transform of the entropy, a measure of the fluctuation of the information in sound segments over time. Entrocy features are combined with MFCCs to generate a composite feature set which is tested using the gaussian mixture model (GMM) speaker recognition method. The proposed method shows improved recognition accuracy over a range of signal-to-noise ratios.

A Robust Document Identification Framework through f-BP Fingerprint

The identification of printed materials is a critical and challenging issue for security purposes, especially when it comes to documents such as banknotes, tickets, or rare collectable cards: eligible targets for ad hoc forgery. State-of-the-art methods require expensive and specific industrial equipment, while a low-cost, fast, and reliable solution for document identification is increasingly needed in many contexts. This paper presents a method to generate a robust fingerprint, by the extraction of translucent patterns from paper sheets, and exploiting the peculiarities of binary pattern descriptors. A final descriptor is generated by employing a block-based solution followed by principal component analysis (PCA), to reduce the overall data to be processed. To validate the robustness of the proposed method, a novel dataset was created and recognition tests were performed under both ideal and noisy conditions.

How Reliable are 11- to 13-Year-Olds’ Self-Ratings of Effort in Noisy Conditions?

Performing a task in noisy conditions is effortful. This is especially relevant for children in classrooms as the effort involved could impair their learning and academic achievements. Numerous studies have investigated how to use behavioral and physiological methods to measure effort, but limited data are available on how well school-aged children rate effort in their classrooms. This study examines whether and how self-ratings can be used to describe the effort children perceive while working in a noisy classroom. This is done by assessing the effect of listening condition on self-rated effort in a group of 182 children 11–13 years old. The children performed three tasks typical of daily classroom activities (speech perception, sentence comprehension, and mental calculation) in three listening conditions (quiet, traffic noise, and classroom noise). After completing each task, they rated their perceived task-related effort on a five-point scale. Their task accuracy and response times (RTs) were recorded (the latter as a behavioral measure of task-related effort). Participants scored higher (more effort) on their self-ratings in the noisy conditions than in quiet. Their self-ratings were also sensitive to the type of background noise, but only for the speech perception task, suggesting that children might not be fully aware of the disruptive effect of background noise. A repeated-measures correlation analysis was run to explore the possible relationship between the three study outcomes (accuracy, self-ratings, and RTs). Self-ratings correlated with accuracy (in all tasks) and with RTs (only in the speech perception task), suggesting that the relationship between different measures of listening effort might depend on the task. Overall, the present findings indicate that self-reports could be useful for measuring changes in school-aged children’s perceived listening effort. More research is needed to better understand, and consequently manage, the individual factors that might affect children’s self-ratings (e.g., motivation) and to devise an appropriate response format.