Identification of hearing disorder by multi-band entropy cepstrum extraction from infant's cry

Jam, Mahmoud Mansouri

doi:10.1109/icbpe.2009.5384066

Cited by 9 publications

(5 citation statements)

References 5 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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“…Mel-frequency Cepstral Coefficients (MFCC) are one of the most common features in the analysis of audio signals. They have been employed in the detection of many health conditions, such as cleft palate [ 18 ], asphyxia [ 19 , 20 ], respiratory distress syndrome [ 4 ] and hearing impairment [ 21 ], and have demonstrated efficient performance. Other feature sets, including fundamental and resonant frequencies [ 22 ], Linear Prediction Coding (LPC) [ 23 ] and prosodic features [ 24 ], have been explored in the feature extraction step of other NCDS designs.…”

Section: Introductionmentioning

confidence: 99%

“…Other feature sets, including fundamental and resonant frequencies [ 22 ], Linear Prediction Coding (LPC) [ 23 ] and prosodic features [ 24 ], have been explored in the feature extraction step of other NCDS designs. Various entropy feature sets were utilized in order to identify deaf neonates from the healthy group [ 21 ], for detection of asphyxia in newborns [ 25 ] and for automated detection of the cry [ 26 ]. It has been reported that approximate entropy has different levels across healthy and pathologic newborns [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

Khalilzad

Kheddache

Tadj

2022

Entropy

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The acoustic characteristics of cries are an exhibition of an infant’s health condition and these characteristics have been acknowledged as indicators for various pathologies. This study focused on the detection of infants suffering from sepsis by developing a simplified design using acoustic features and conventional classifiers. The features for the proposed framework were Mel-frequency Cepstral Coefficients (MFCC), Spectral Entropy Cepstral Coefficients (SENCC) and Spectral Centroid Cepstral Coefficients (SCCC), which were classified through K-nearest Neighborhood (KNN) and Support Vector Machine (SVM) classification methods. The performance of the different combinations of the feature sets was also evaluated based on several measures such as accuracy, F1-score and Matthews Correlation Coefficient (MCC). Bayesian Hyperparameter Optimization (BHPO) was employed to tailor the classifiers uniquely to fit each experiment. The proposed methodology was tested on two datasets of expiratory cries (EXP) and voiced inspiratory cries (INSV). The highest accuracy and F-score were 89.99% and 89.70%, respectively. This framework also implemented a novel feature selection method based on Fuzzy Entropy (FE) as a final experiment. By employing FE, the number of features was reduced by more than 40%, whereas the evaluation measures were not hindered for the EXP dataset and were even enhanced for the INSV dataset. Therefore, it was deduced through these experiments that an entropy-based framework is successful for identifying sepsis in neonates and has the advantage of achieving high performance with conventional machine learning (ML) approaches, which makes it a reliable means for the early diagnosis of sepsis in deprived areas of the world.

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Identification of hearing disorder by multi-band entropy cepstrum extraction from infant's cry

Cited by 9 publications

References 5 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

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

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