Performance Measurement Of<i>Mel Frequency Ceptral Coefficient</i>(MFCC) Method In Learning System Of Al- Qur’an Based In<i>Nagham</i>Pattern Recognition

Afrillia, Yesy; Mawengkang, Herman; Ramli, Marwan; Fadlisyah,; Fhonna, Rizky Putra

doi:10.1088/1742-6596/930/1/012036

Cited by 17 publications

(8 citation statements)

References 3 publications

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“…These are similar to MFCCs, yet they do not include the DCT operation. This feature provided good results in detecting different audio sounds and classification of sounds in previous studies [2,13,33].…”

Section: Feature Extractionmentioning

confidence: 72%

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Coelho

Pereira

Matos

et al. 2021

IFIP Advances in Information and Communication Technology

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Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.

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Section: Feature Extractionmentioning

confidence: 72%

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Coelho

Pereira

Matos

et al. 2021

IFIP Advances in Information and Communication Technology

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“…In particular, a feature for audio signal processing named Mel Frequency Energy Coefficients (MFECs) is addressed, which are log-energies derived directly from the filterbanks energies. This feature provided good results in detecting different audio sounds and classification of sounds in previous studies [22,28,29]. For demonstration purposes, fig.…”

Section: Audio Feature Extractionmentioning

confidence: 73%

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

Coelho

Matos

Pereira

et al. 2022

Neural Comput & Applic

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The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the main target of this research project). This paper introduces three deep AutoEncoders (AE) for unsupervised AAD tasks, namely a Dense AE, a Convolutional Neural Network (CNN) AE and Long Short-Term Memory Autoencoder (LSTM) AE. To tune the deep learning architectures, development data was adopted from public domain audio datasets related with working machines. A large set of computational experiments was held, showing that the three proposed deep autoencoders, when combined with a melspectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Next, on a second experimental stage, aiming to address the final in-vehicle passenger safety goal, the three AEs were adapted to learn from in-vehicle normal audio, assuming three realistic scenarios that were generated by a Deep Autoencoders for Acoustic Anomaly Detection synthetic audio mixture tool. In general, a high quality AAD discrimination was obtained: working machine data -72% to 91%; and in-vehicle audio -78% to 81%. In conjunction with an automotive company, an in-vehicle AAD intelligent system prototype was further developed, aiming to test a selected model (LSTM AE) during a pilot demonstration event that targeted the cough anomaly. Interesting results were obtained, with the AAD system presenting a high cough classification accuracy (e.g., 100% for front seat locations).

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“…The wrapping of Mel Frequency is often used in conjunction with a FB. A FB is a kind of filter used to determine the amount of energy contained within a certain frequency range, Afrillia et al (2017) . Finally, the logarithmic (LOG) value is obtained by converting the DFT result to a single value.…”

Section: Dataset and Preprocessingmentioning

confidence: 99%

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

Amjad

Khan

Chang

2022

PeerJ Computer Science

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Speech emotion recognition (SER) systems have evolved into an important method for recognizing a person in several applications, including e-commerce, everyday interactions, law enforcement, and forensics. The SER system’s efficiency depends on the length of the audio samples used for testing and training. However, the different suggested models successfully obtained relatively high accuracy in this study. Moreover, the degree of SER efficiency is not yet optimum due to the limited database, resulting in overfitting and skewing samples. Therefore, the proposed approach presents a data augmentation method that shifts the pitch, uses multiple window sizes, stretches the time, and adds white noise to the original audio. In addition, a deep model is further evaluated to generate a new paradigm for SER. The data augmentation approach increased the limited amount of data from the Pakistani racial speaker speech dataset in the proposed system. The seven-layer framework was employed to provide the most optimal performance in terms of accuracy compared to other multilayer approaches. The seven-layer method is used in existing works to achieve a very high level of accuracy. The suggested system achieved 97.32% accuracy with a 0.032% loss in the 75%:25% splitting ratio. In addition, more than 500 augmentation data samples were added. Therefore, the proposed approach results show that deep neural networks with data augmentation can enhance the SER performance on the Pakistani racial speech dataset.

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Performance Measurement OfMel Frequency Ceptral Coefficient(MFCC) Method In Learning System Of Al- Qur’an Based InNaghamPattern Recognition

Cited by 17 publications

References 3 publications

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

Data augmentation and deep neural networks for the classification of Pakistani racial speakers recognition

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