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2 2020

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Segregation of songs and instrumentals : a precursor to voice/accompaniment separation from songs in noisy scenario

Mukherjee Himadri, Obaidullah Sk Md, Santosh K.C., Gonçalves Teresa, Phadikar Santanu, Roy Kaushik

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 2

81--90

The music industry has come a long way since its inception. Music producers have also adhered to modern technology to infuse life into their creations. Systems capable of separating sounds based on sources especially vocals from songs have always been a necessity which has gained attention from researchers as well. The challenge of vocal separation elevates even more in the case of the multi‐instrument environment. It is essential for a system to be first able to detect that whether a piece of music contains vocals or not prior to attempting source separation. It is also very much challenging to perform source separation from audio which is contaminated with noise. In this paper, such a system is proposed being tested on a database of more than 99 hours of instrumentals and songs. Experiments were performed with both noise free as well as noisy audio clips. Using line spectral frequency‐based features, we have obtained the highest accuracies of 99.78% and 99.34% (noise free and noisy scenario respectively) from among six different classifiers, viz. BayesNet, Support Vector Machine, Multi Layer Perceptron, LibLinear, Simple Logistic and Decision Table.

Radon‐wavelet based novel image descriptor for mammogram mass classification

Obaidullah Sk Md, Ahmed Sajib, Gonçalves Teresa, Rato Luís

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 2

74--80

Mammography based breast cancer screening is very popular because of its lower costing and readily availability. For automated classification of mammogram images as benign or malignant machine learning techniques are involved. In this paper, a novel image descriptor which is based on the idea of Radon and Wavelet transform is proposed. This method is quite efficient as it performs well without any clinical information. Performance of the method is evaluated using six different classifiers namely: Bayesian network (BN), Linear discriminant analysis (LDA), Logistic, Support vector machine (SVM), Multilayer perceptron (MLP) and Random Forest (RF) to choose the best performer. Considering the present experimental framework, we found, in terms of area under the ROC curve (AUC), the proposed image descriptor outperforms, upto some extent, previous reported experiments using histogram based hand‐crafted methods, namely Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (CNN). Our experimental results show the highest AUC value of 0.986, when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.