A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.

Molecular interactions of plumbagin inclusion complexes with β-cyclodextrin (BCD), dimethyl--cyclodextrin (MBCD), and hydroxypropyl-β-cyclodextrin (HPBCD) were investigated by semi-empirical, Parameterization Method 6 and 7 (PM6, and PM7) in the aqueous phase using polarizable continuum calculations. The results revealed two different binding modes of the plumbagin molecule inside the BCD cavity with a negative value of the complexation energy. In conformation-I, the hydroxyl phenolic group of plumbagin was placed in the BCD cavity near the narrow-side of the host molecule. In the other model, conformation-II, the methyl quinone group of plumbagin was placed in the cavity of BCD near the narrow-side of the host molecule. The higher the negative value of the complexation energy, the more favorable is the pathway of inclusion-complex formation.

There is a great concern regarding the misuse of deepfake speech technology to synthesize a real person’s voice. Therefore, developing speech-security systems capable of detecting deepfake speech remains paramount in safeguarding against such misuse. Although various speech features and methods have been proposed, their potential for distinguishing between genuine and deepfake speech remains unclear. Since speech-pathological features with deep learning are widely used to assess unnaturalness in disordered voices associated with voice-production mechanisms, we investigated the potential of eleven speech-pathological features for distinguishing between genuine and deepfake speech, i.e., jitter (three types), shimmer (four types), harmonics-to-noise ratio, cepstral-harmonics-to-noise ratio, normalized noise energy, and glottal-to-noise excitation ratio. This paper proposes a method of combining two models on the basis of two different dimensions of speech-pathological features to greatly improve the effectiveness of deepfake speech detection, along with mel-spectrogram features, to enhance detection efficiency. We evaluated the proposed method on the datasets of the Automatic Speaker Verification Spoofing and Countermeasures Challenges ASVspoof 2019 and 2021. The results indicate that the proposed method outperforms the baselines in terms of accuracy, recall, F1-score, and F2-score, achieving 95.06, 99.46, 97.30, and 98.59%, respectively, on the ASVspoof 2019 dataset. It also surpasses the baselines on the ASVspoof 2021 dataset in terms of recall, F1-score, F2-score, and equal error rate, achieving 99.96, 96.65, 98.18, and 15.97%, respectively.

In similarity-based constrained clustering, there have been various approaches on how to define the similarity between documents to guide the grouping of similar documents together. This paper presents an approach to use term-distribution statistics extracted from a small number of cue instances with their known classes, for term weightings as indirect distance constraint. As for distribution-based term weighting, three types of term-oriented standard deviations are exploited: distribution of a term in a collection (SD), average distribution of a term in a class (ACSD), and average distribution of a term among classes (CSD). These term weightings are explored with the consideration of symmetry concepts by varying the magnitude to positive and negative for promoting and demoting effects of three standard deviations. In k-means, followed the symmetry concept, both seeded and unseeded centroid initializations are investigated and compared to the centroid-based classification. Our experiment is conducted using five English text collections and one Thai text collection, i.e., Amazon, DI, WebKB1, WebKB2, and 20Newsgroup, as well as TR, a collection of Thai reform-related opinions. Compared to the conventional TFIDF, the distribution-based term weighting improves the centroid-based method, seeded k-means, and k-means with the error reduction rate of 22.45%, 31.13%, and 58.96%.