Musical Genre Classification of Recorded Songs based on Music Structure Similarity (Published)
Automatic music genre classification is a research area that is increasing in popularity. Most researchers on this research area have been focusing on combining information from different sources than the musical signal itself. This paper presents a novel approach for the automatic music genre classification problem using audio signal for the context of Sri Lankan Music. The proposed approach uses two feature vectors and Support Vector Machine (SVM) classifier with radial-basis kernel function. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed through this work. Music genre classification accuracy of 74.5% was recorded as the highest overall classification accuracy on our dataset containing over 100 songs over the five musical genres. This approach shows that it is possible to implement a genre classification model with a reasonably good accuracy by using different types of domain based audio features.
Keywords: Audio Signal Analysis, Feature Extraction, Music Information Retrieval, Musical Genre Classification, SVM
A Novel Method Of Average Filtering For Removing Noise And Face Recognition (Published)
Face recognition is new and difficult which requires great effort and determination due to the Wide variety of faces, complexity of noises and image backgrounds. In this paper, we propose an Average Filtering based novel method for face recognition in cluttered and noisy images. It is imperative that computational researchers know of the key findings from experimental studies of face recognition by human. These findings provide insights into the nature of starting symbol to begin that the human visual system relies upon for achieving its great deal of performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we are presenting what we believe are various basic results, with implications for the computational design systems. The aim of our proposed work of average filtering based method for face recognition is to improve the recognition accuracy. We use AT&T face database and experiments on it are performed to demonstrate the effectiveness of the proposed method.
Keywords: Average Filter, Eigenfaces, Face Recognition, Feature Extraction, Fisherfaces., Laplacianfaces, Linear Discriminant Analysis, Principal Component Analysis, Smooth Mean Filter