Mel Frequency Cepstral Coefficient and its Applications: A Review

Abdul, Zrar Kh.; Al-Talabani, Abdulbasit K.

doi:10.1109/access.2022.3223444

Cited by 101 publications

(28 citation statements)

References 143 publications

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“…Therefore, the MFCC represents the coefficients of the Mel-frequency spectrum. The MFCC is used to model features of the audio signal (Abdul and Al-Talabani, 2022 ). It extracts harmonics and side bands of the signal's spectrum.…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based infant crying interpretation

Hammoud,

Getahun,

Baldycheva

et al. 2024

Front. Artif. Intell.

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Crying is an inevitable character trait that occurs throughout the growth of infants, under conditions where the caregiver may have difficulty interpreting the underlying cause of the cry. Crying can be treated as an audio signal that carries a message about the infant's state, such as discomfort, hunger, and sickness. The primary infant caregiver requires traditional ways of understanding these feelings. Failing to understand them correctly can cause severe problems. Several methods attempt to solve this problem; however, proper audio feature representation and classifiers are necessary for better results. This study uses time-, frequency-, and time-frequency-domain feature representations to gain in-depth information from the data. The time-domain features include zero-crossing rate (ZCR) and root mean square (RMS), the frequency-domain feature includes the Mel-spectrogram, and the time-frequency-domain feature includes Mel-frequency cepstral coefficients (MFCCs). Moreover, time-series imaging algorithms are applied to transform 20 MFCC features into images using different algorithms: Gramian angular difference fields, Gramian angular summation fields, Markov transition fields, recurrence plots, and RGB GAF. Then, these features are provided to different machine learning classifiers, such as decision tree, random forest, K nearest neighbors, and bagging. The use of MFCCs, ZCR, and RMS as features achieved high performance, outperforming state of the art (SOTA). Optimal parameters are found via the grid search method using 10-fold cross-validation. Our MFCC-based random forest (RF) classifier approach achieved an accuracy of 96.39%, outperforming SOTA, the scalogram-based shuffleNet classifier, which had an accuracy of 95.17%.

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Section: Methodsmentioning

confidence: 99%

Machine learning-based infant crying interpretation

Hammoud,

Getahun,

Baldycheva

et al. 2024

Front. Artif. Intell.

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“…To train and test our models, we use the RAVDESS and the SAVEE database. These databases contain '.wav' audio files, which we process to export Mel Frequencies Cepstral Coefficients (MFCCs) [31]. These factors use the Mel scale, which is based on how humans perceive different signal frequencies (the spectral content of the voice signal, as recognized by human hearing).…”

Section: Methodsmentioning

confidence: 99%

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Mountzouris,

Perikos,

Hatzilygeroudis

2023

Electronics

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Speech emotion recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a deep belief network (DBN), a simple deep neural network (SDNN), an LSTM network (LSTM), an LSTM network with the addition of an attention mechanism (LSTM-ATN), a convolutional neural network (CNN), and a convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of the attention mechanism on the results. Dropout and batch normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio–Visual Expressed Emotion (SAVEE) database and the Ryerson Audio–Visual Database (RAVDESS) were used for the training and evaluation of our models. The results showed that the networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that the CNN-ATN was the best among the tested networks, achieving an accuracy of 74% for the SAVEE database and 77% for the RAVDESS, and exceeding existing state-of-the-art systems for the same datasets.

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“…This is due to variations in the known human ear frequency bandwidth. This coefficient is created by embellishing the filter bank's output log energy, which comprises triangle filters positioned linearly on the Mel frequency scale [20], [21]. The MFCC coefficient is obtained by decorating the output log energy of a filter bank consisting of triangular filters, which are linearly spaced on the Mel frequency scale [22].…”

Section: Mel Frequency Cepstral Coefficientsmentioning

confidence: 99%

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

Wirdiani,

Machetho,

Putra

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Various biometric security systems, such as face recognition, fingerprint, voice, hand geometry, and iris, have been developed. Apart from being a communication medium, the human voice is also a form of biometrics that can be used for identification. Voice has unique characteristics that can be used as a differentiator between one person and another. A sound speaker recognition system must be able to pick up the features that characterize a person's voice. This study aims to develop a human speaker recognition system using the Convolutional Neural Network (CNN) method. This research proposes improvements in the fine-tuning layer in CNN architecture to improve the Accuracy. The recognition system combines the CNN method with Mel Frequency Cepstral Coefficients (MFCC) to perform feature extraction on raw audio and K Nearest Neighbor (KNN) to classify the embedding output. In general, this system extracts voice data features using MFCC. The process is continued with feature extraction using CNN with triplet loss to obtain the 128-dimensional embedding output. The classification of the CNN embedding output uses the KNN method. This research was conducted on 50 speakers from the TIMIT dataset, which contained eight utterances for each speaker and 60 speakers from live recording using a smartphone. The accuracy of this speaker recognition system achieves high-performance accuracy. Further research can be developed by combining different biometrics objects, commonly known as multimodal, to improve recognition accuracy further.

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Mel Frequency Cepstral Coefficient and its Applications: A Review

Cited by 101 publications

References 143 publications

Machine learning-based infant crying interpretation

Machine learning-based infant crying interpretation

Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism

Improvement Model for Speaker Recognition using MFCC-CNN and Online Triplet Mining

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