An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Khan, Isra; Emaduddin, Shah Muhammad; Ullah, Ashhad

doi:10.2478/acss-2022-0019

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Rooted in the tenets of human auditory cognition, the Mel spectrum aptly lends itself to noise analysis, demonstrating resilience against extraneous noise and data gaps during transformation. Within the realms of audio processing [21,22] and vocal recognition [23][24][25][26][27], the Mel spectrogram has been pervasively and efficaciously employed. Furthermore, select inquiries have progressively integrated Mel features into the realm of sound event detection and classification, encompassing machinery noise [28,29] and environmental acoustics [30].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

Zhao,

Ding,

Huang

et al. 2023

Sound&Vibration

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The deployment of vehicle micro-motors has witnessed an expansion owing to the progression in electrification and intelligent technologies. However, some micro-motors may exhibit design deficiencies, component wear, assembly errors, and other imperfections that may arise during the design or manufacturing phases. Consequently, these micro-motors might generate anomalous noises during their operation, consequently exerting a substantial adverse influence on the overall comfort of drivers and passengers. Automobile micro-motors exhibit a diverse array of structural variations, consequently leading to the manifestation of a multitude of distinctive auditory irregularities. To address the identification of diverse forms of abnormal noise, this research presents a novel approach rooted in the utilization of vibro-acoustic fusion-convolutional neural network (VAF-CNN). This method entails the deployment of distinct network branches, each serving to capture disparate features from the multi-sensor data, all the while considering the auditory perception traits inherent in the human auditory system. The intermediary layer integrates the concept of adaptive weighting of multi-sensor features, thus affording a calibration mechanism for the features hailing from multiple sensors, thereby enabling a further refinement of features within the branch network. For optimal model efficacy, a feature fusion mechanism is implemented in the concluding layer. To substantiate the efficacy of the proposed approach, this paper initially employs an augmented data methodology inspired by modified SpecAugment, applied to the dataset of abnormal noise samples, encompassing scenarios both with and without in-vehicle interior noise. This serves to mitigate the issue of limited sample availability. Subsequent comparative evaluations are executed, contrasting the performance of the model founded upon single-sensor data against other feature fusion models reliant on multi-sensor data. The experimental results substantiate that the suggested methodology yields heightened recognition accuracy and greater resilience against interference. Moreover, it holds notable practical significance in the engineering domain, as it furnishes valuable support for the targeted management of noise emanating from vehicle micro-motors.

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Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

Zhao,

Ding,

Huang

et al. 2023

Sound&Vibration

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Research on Musical Tone Recognition Method Based on Improved RNN for Vocal Music Teaching Network Courses

Long

2023

International Journal of Web-Based Learning and Teaching Technologies

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The test results show that the fast Fourier process with multiple time superposition and a dimension length of 40 is most beneficial to the accuracy of the model. The loss curve value of the convolutional recurrent network model (CRN) is much lower than the other three models. The music tone recognition model learns better. The accuracy rate value and recall rate value of the CRN are the highest, and the accuracy rates of the four music tone indicators are 94.6%, 92.4%, 93.5%, 92.5%, and the recall rates were 93.2%, 94.9%, 95.2%, and 88.6% respectively; the improved algorithm was the most accurate in terms of F1 values and is suitable for use in vocal music teaching courses. The results show that the algorithm can be broadly performed in the zone of music tone recognition and has a certain contribution to the development of the field of music tone recognition.

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An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Cited by 2 publications

References 26 publications

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception

Research on Musical Tone Recognition Method Based on Improved RNN for Vocal Music Teaching Network Courses

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