Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge

The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various text-to-speech(TTS) algorithms as well as unknown TTS algorithms. Weexperiment with both the classical machine learning methodssuch as support vector machine, Gaussian mixture model, anddeep learning based methods such as ResNet, VGG16, and twoshallow end-to-end networks. We observe that deep learningbased methods with raw data demonstrate the best performance.

Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production

In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasize the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.

Bayesian Receiver Operating Characteristic Metric for Linear Classifiers

We propose a novel classifier accuracy metric: the Bayesian Area Under the Receiver Operating Characteristic Curve (CBAUC). The method estimates the area under the ROC curve and is related to the recently proposed Bayesian Error Estimator. The metric can assess the quality of a classifier using only the training dataset without the need for computationally expensive cross-validation. We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data. These experiments confirm that the closed-form CBAUC is both faster and more accurate than conventional AUC estimators.