Mel frequency cepstral coefficient temporal feature integration for classifying squeak and rattle noise

Abeysinghe, Asith; Fard, Mohammad; Jazar, Reza N.; Zambetta, Fabio; Davy, John L.

doi:10.1121/10.0005201

Cited by 14 publications

(8 citation statements)

References 30 publications

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“…The extracted original feature is the mel frequency cepstral coefficient (MFCC) ( Yang et al, 2020 ). Since the tones of musical instruments are used to distinguish different types of musical instruments, MFCC has proven to be a representation of musical tones ( Abeysinghe et al, 2021 ). Figure 4 shows the process of extracting characteristic parameters from traditional instrumental music MFCC.…”

Section: Methodsmentioning

confidence: 99%

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

2022

Front. Psychol.

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In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.

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Section: Methodsmentioning

confidence: 99%

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

2022

Front. Psychol.

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“…They are time domain, frequency domain and time-frequency domain [29]. Cepstral domain features are retrieved after taking their fast Fourier transform (FFT) of the amplitude's logarithm from spectrum data [30]. Since MFCCs closely resemble human auditory system, so their inherent power is normally harnessed for the speech recognition in the diverse problems [31].…”

Section: Related Workmentioning

confidence: 99%

A Novel Voice Feature AVA and its Application to the Pathological Voice Detection Through Machine Learning

Altaf,

Mahdin,

Maskat

et al. 2023

IJACSA

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Voice pathology is a universal problem which must be addressed. Traditionally, this malady is treated by using the surgical instruments in the varied healthcare settings. In the current era, machine learning experts have paid an increasing attention towards the solution of this problem by exploiting the signal processing of the voice. For this purpose, numerous voice features have been capitalized to classify the healthy and pathological voice signals. In particular, Mel-Frequency Cepstral Coefficients (MFCC) is a widely used feature in speech and audio signal processing. It denotes spectral characteristics of a voice signal, particularly of human speech. The modus operandi of MFCC is too time-consuming, which goes against the hasty and urgent nature of the modern times. This study has developed a yet another voice feature by utilizing the average value of the amplitudes (AVA) of the voice signals. Moreover, Gaussian Naive Bayes classifier has been employed to classify the given voice signals as healthy or pathological. Apart from that, the dataset has been acquired from the SVD (Saarbrucken Voice Database) to demonstrate the workability of the proposed voice feature and its usage in the classifier. The machine experimentation rendered very promising results. Particularly, Recall, F1 and accuracy scores obtained, are 100%, 83% and 80%, respectively. These results vividly imply that the proposed classifier can be installed in various healthcare settings.

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“…Specifically, one approach involves utilizing machine learning algorithms to extract representative sound features from the time domain, frequency domain, and Mel frequency cepstrum domain. These features are then fed into a classifier for classification [ 10 , 11 ]. Another approach leverages convolutional neural networks to extract deep features from sound spectrograms, enabling effective classification [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

Wang,

Qi,

et al. 2024

PLoS ONE

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Accurate identification of porcine cough plays a vital role in comprehensive respiratory health monitoring and diagnosis of pigs. It serves as a fundamental prerequisite for stress-free animal health management, reducing pig mortality rates, and improving the economic efficiency of the farming industry. Creating a representative multi-source signal signature for porcine cough is a crucial step toward automating its identification. To this end, a feature fusion method that combines the biological features extracted from the acoustic source segment with the deep physiological features derived from thermal source images is proposed in the paper. First, acoustic features from various domains are extracted from the sound source signals. To determine the most effective combination of sound source features, an SVM-based recursive feature elimination cross-validation algorithm (SVM-RFECV) is employed. Second, a shallow convolutional neural network (named ThermographicNet) is constructed to extract deep physiological features from the thermal source images. Finally, the two heterogeneous features are integrated at an early stage and input into a support vector machine (SVM) for porcine cough recognition. Through rigorous experimentation, the performance of the proposed fusion approach is evaluated, achieving an impressive accuracy of 98.79% in recognizing porcine cough. These results further underscore the effectiveness of combining acoustic source features with heterogeneous deep thermal source features, thereby establishing a robust feature representation for porcine cough recognition.

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Mel frequency cepstral coefficient temporal feature integration for classifying squeak and rattle noise

Cited by 14 publications

References 30 publications

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

A Novel Voice Feature AVA and its Application to the Pathological Voice Detection Through Machine Learning

Heterogeneous fusion of biometric and deep physiological features for accurate porcine cough recognition

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