Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes' vocalisations

Ogundile, Olayinka O.; Usman, Ayinde M.; Babalola, Oluwaseyi P.; Versfeld, Jaco

doi:10.1016/j.ecoinf.2021.101306

Cited by 9 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Features in the time-domain and frequency-domain can be generated based on signal waveforms, e.g., Short-Time Fourier Transforms (STFT) 6 , Mel-Scale (Mel) Spectrogram 7 , Mel-Scale Frequency Cepstral Coefficients (MFCC) 8 , Constant-Q Transform (CQT) 9 and various one-dimensional (1D) spectral properties. Traditionally, statistical models are used in classification tasks, such as Hidden Markov Model (HMMs) 10 , Gaussian Mixture Model (GMM) 11 , 12 , and Support Vector Machines (SVM) 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

Qin

et al. 2022

Sci Rep

View full text Add to dashboard Cite

As an effective approach to perceive environments, acoustic scene classification (ASC) has received considerable attention in the past few years. Generally, ASC is deemed a challenging task due to subtle differences between various classes of environmental sounds. In this paper, we propose a novel approach to perform accurate classification based on the aggregation of spatial–temporal features extracted from a multi-branch three-dimensional (3D) convolution neural network (CNN) model. The novelties of this paper are as follows. First, we form multiple frequency-domain representations of signals by fully utilizing expert knowledge on acoustics and discrete wavelet transformations (DWT). Secondly, we propose a novel 3D CNN architecture featuring residual connections and squeeze-and-excitation attentions (3D-SE-ResNet) to effectively capture both long-term and short-term correlations inherent in environmental sounds. Thirdly, an auxiliary supervised branch based on the chromatogram of the original signal is incorporated in the proposed architecture to alleviate overfitting risks by providing supplementary information to the model. The performance of the proposed multi-input multi-feature 3D-CNN architecture is numerically evaluated on a typical large-scale dataset in the 2019 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2019) and is shown to obtain noticeable performance gains over the state-of-the-art methods in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

Qin

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Whereas, feature extraction does a functional mapping of input features to generate new features [3]. Principal Component Analysis [12], Dynamic Mode Decomposition [13], Mel-scale Frequency Cepstral Coefficient [14], Non Negative Matrix Factorization [15], Empirical Mode Decomposition [16] etc. are examples of feature extraction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Neurochaos Feature Transformation and Classification for Imbalanced Learning

Sethi¹,

Nagaraj²,

Harikrishnan³

2022

Preprint

View full text Add to dashboard Cite

Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Real-time scenarios demand decision-making from rare events wherein the data are typically imbalanced. These situations commonly arise in medical applications, cybersecurity, catastrophic predictions etc. This motivates development of learning algorithms capable of learning from imbalanced data. Human brain effortlessly learns from imbalanced data. Inspired by the chaotic neuronal firing in the human brain, a novel learning algorithm namely Neurochaos Learning (NL) was recently proposed. NL is categorized in three blocks: Feature Transformation, Neurochaos Feature Extraction (CFX), and Classification. In this work, the efficacy of neurochaos feature transformation and extraction for classification in imbalanced learning is studied. We propose a unique combination of neurochaos based feature transformation and extraction with traditional ML algorithms. The explored datasets in this study revolve around medical diagnosis, banknote fraud detection, environmental applications and spoken-digit classification. In this study, experiments are performed in both high and low training sample regime. In the former, five out of nine datasets have shown a performance boost in terms of macro F1-score after using CFX features. The highest performance boost obtained is 25.97% for Statlog (Heart) dataset using CFX+Decision Tree. In the low training sample regime (from just one to nine training samples per class), the highest performance boost of 144.38% is obtained for Haberman's Survival dataset using CFX+Random Forest. NL offers enormous flexibility of combining CFX with any ML classifier to boost its performance, especially for learning tasks with limited and imbalanced data.

show abstract

“…Compared with the time-domain features, there are more optional features in the frequency domain, such as spectral envelope [ 19 ], spectral flux [ 20 ], and Mel spectrogram [ 21 ]. By decomposing these features, we can get cepstral features, Mel frequency cepstral coefficients (MFCCs) [ 22 ], linear-frequency cepstral coefficients (LFCCs) [ 23 ], gammatone-feature cepstral coefficients [ 24 ], and linear predictive coding (LPC) [ 25 ], as well as some feature histograms of oriented gradients [ 26 ], sub-band power distribution [ 27 ], and local binary patterns [ 28 ] developed from the field of image recognition.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation

Cai

Zhu

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

Due to the poor visibility of the deep-sea environment, acoustic signals are often collected and analyzed to explore the behavior of marine species. With the progress of underwater signal-acquisition technology, the amount of acoustic data obtained from the ocean has exceeded the limit that human can process manually, so designing efficient marine-mammal classification algorithms has become a research hotspot. In this paper, we design a classification model based on a multi-channel parallel structure, which can process multi-dimensional acoustic features extracted from audio samples, and fuse the prediction results of different channels through a trainable full connection layer. It uses transfer learning to obtain faster convergence speed, and introduces data augmentation to improve the classification accuracy. The k-fold cross-validation method was used to segment the data set to comprehensively evaluate the prediction accuracy and robustness of the model. The evaluation results showed that the model can achieve a mean accuracy of 95.21% while maintaining a standard deviation of 0.65%. There was excellent consistency in performance over multiple tests.

show abstract

Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes' vocalisations

Cited by 9 publications

References 22 publications

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

Neurochaos Feature Transformation and Classification for Imbalanced Learning

A Parallel Classification Model for Marine Mammal Sounds Based on Multi-Dimensional Feature Extraction and Data Augmentation

Contact Info

Product

Resources

About