Development of Abbreviated Versions of the Word Auditory Recognition and Recall Measure

Abstract Many data-driven coal gangue recognition (CGR) methods based on the vibration or sound of collapsed coal and gangue have been proposed to achieve automatic CGR, which is important for realizing intelligent top-coal caving. However, the strong background noise and complex environment in underground coal mines render this task challenging in practical applications. Inspired by the fact that workers distinguish coal and gangue from underground noise by listening to the hydraulic support sound, we propose an auditory model based CGR method that simulates human auditory recognition by combining an auditory spectrogram with a convolutional neural network (CNN). First, we adjust the characteristic frequency (CF) distribution of the auditory peripheral model (APM) based on the spectral characteristics of collapsed sound signals from coal and gangue and then process the sound signals using the adjusted APM to obtain inferior colliculus auditory signals with multiple CFs. Subsequently, the auditory signals of all CFs are converted into gray images separately and then concatenated into a multichannel auditory spectrum along the channel dimension. Finally, we input the multichannel auditory spectrum as a feature map to the two-dimensional CNN, whose convolutional layers are used to automatically extract features, and the fully connected layer and softmax layer are used to flatten features and predict the recognition result, respectively. The CNN is optimized for the CGR based on a comparison study of four typical types of CNN structures with different network training hyperparameters. The experimental results show that this method affords an accurate CGR with a recognition accuracy of 99.5%. Moreover, this method offers excellent noise immunity compared with typically used CGR methods under various noisy conditions.

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Auditory perception of Mexican children with profound bilateral hearing loss receiving auditory verbal therapy

Child Language Teaching and Therapy ◽

10.1177/0265659020969966 ◽

2020 ◽

pp. 026565902096996

Author(s):

Damaris F Estrella-Castillo ◽

Héctor Rubio-Zapata ◽

Lizzette Gómez-de-Regil

Keyword(s):

Hearing Loss ◽

Hearing Aids ◽

Oral Language ◽

Auditory Perception ◽

Extended Family ◽

Developed Countries ◽

Profound Hearing Loss ◽

Bilateral Hearing Loss ◽

Auditory Recognition ◽

Auditory Verbal Therapy

Profound hearing loss can have serious and irreversible consequences for oral language development in children, affecting spoken and written language acquisition. Auditory-verbal therapy has been widely applied to children with hearing loss with promising results, mainly in developed countries where cochlear implants are available. An evaluation was done of auditory perception in 25 children 5 to 8 years of age, with profound hearing loss, users of 4- or 5-channel hearing aids, and enrolled in a personalized auditory-verbal therapy program. Regarding initial auditory perception skills, children performed better on the Noises and Sounds block than on the Language block. By subscales, top performance was observed for auditory analysis (Noises and Sounds) and auditory recognition (Language). A series of t-tests showed that significant improvement after Auditory-verbal therapy occurred in global scores for Noises and Sounds and for Language blocks, regardless of sex, urban or rural community origin, nuclear or extended family. The study provides evidence of deficiencies in auditory in children with profound bilateral hearing loss and how this might improve after receiving Auditory-verbal therapy. Nevertheless, the descriptive study design prevents conclusions regarding the effectiveness of the therapy. Subsequent research must take into account intrinsic and environmental factors that might play a mediating role in the benefits of Auditory-verbal therapy for auditory perception.

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