Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN

Jin, Shuaipu; Wang, Xiufeng; Du, Leilei; He, Dan

doi:10.1016/j.apacoust.2020.107562

Cited by 32 publications

(9 citation statements)

References 25 publications

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“…MFCC is reported to be a powerful feature hence it has mostly been adopted alone, much less been combined with other features. The reason of combination of MFCC with other features is mostly to overcome the sensitivity of MFCC in noise environment [109].…”

Section: Discussionmentioning

confidence: 99%

Mel Frequency Cepstral Coefficient and its Applications: A Review

2022

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Feature extraction and representation has significant impact on the performance of any machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to model features of audio signal and is widely used in various fields. This paper aims to review the applications that the MFCC is used for in addition to some issues that facing the MFCC computation and its impact on the model performance. These issues include the use of MFCC for non-acoustic signals, adopting the MFCC alone or combining it with other features, the use of time series versus global representation of the MFCC, following the standard form of the MFCC computation versus modifying its parameters, and supplying the traditional machine learning methods versus the deep learning methods..

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Section: Discussionmentioning

confidence: 99%

Mel Frequency Cepstral Coefficient and its Applications: A Review

2022

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“…Qureshi et al [14] used general neural network (ANN) and convolutional neural network (CNN) methods to effectively solve the problem of female urinary incontinence diagnosis and management. Jin et al [15] combined MFCC with a convolutional neural network (CNN) and developed a psychoacoustic model to measure automotive transmission noise perceived by humans. Cuocolo et al [16] used machine learning (ML) to read prostate magnetic resonance imaging (MRI).…”

Section: Related Workmentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

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“…One of the most popular machine learning techniques for pattern recognition and classification is the convolution neural network (CNN) (1)(2)(3)(4)(5)(6). One of the most commonly used feature extraction techniques for voice recognition is the mel-frequency cepstral coefficients (MFCC) (7)(8)(9)(10). Extracted features are fed into the CNN to produce the voice recognition model.…”

Section: Introductionmentioning

confidence: 99%

“…transmission noise(8). The architecture of the general CNN has been modified where the softmax classification layer has been substitute for the linear transform prediction layer.…”

mentioning

confidence: 99%

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

et al. 2021

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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

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Evaluation and modeling of automotive transmission whine noise quality based on MFCC and CNN

Cited by 32 publications

References 25 publications

Mel Frequency Cepstral Coefficient and its Applications: A Review

Mel Frequency Cepstral Coefficient and its Applications: A Review

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network

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