Browsing by Author "Charles, J."

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    A Data Mining Approach for Taal and Laya Recognition of North Indian Classical Music
    (Uva Wellassa University of Sri Lanka, 2020) Hettiarachchi, B.; Charles, J.; Lekamge, L.S.
    Music plays a vital role in our day-to-day life, especially in today’s digital age. Computational musicology is an interdisciplinary area in which computational methods are used to analyse musical structures: notes, chords, rhythms, and patterns thereof. While western classical music is extensively explored, North Indian classical music remains to be explored computationally. Meanwhile, our recent review of the literature revealed that Raag identification is among the frequent data mining tasks applied to North Indian music. However, recognition of their rhythmic structures is also important as it serves in a multitude of applications e.g., intelligent music archival, enhanced navigation and retrieval of music, and informed music listening. Rhythm in North Indian classical music revolves around the theme of Taal - the cycle of beats of specific syllables and beats. It is the most basic information for listeners to follow the rhythmic structure of music. Laya is the speed of Taal and may vary between Vilambit (slow), Madhya (medium), and Drut (fast). Taken together, the main aim of the proposed study is to apply data mining for the recognition of Taal and Laya in North Indian classical music. A dataset of 151 excerpts (2mins; 44.1 kHz; stereo; .wav) from CompMusic Hindustani test corpus, belonging to four popular Taals is used in the study. For each Taal, there are excerpts in three Layas. Acoustic features pertaining to fluctuation, beat spectrum, onsets, event density, tempo, metre, metroid, and pulse clarity will be extracted using MATLAB MIRToolbox. The performance of frequently adopted algorithms e.g., k-Nearest Neighbor and Support Vector Machine is to be compared in the study with the aim of developing a classifier with higher accuracy. Even though the findings of the study would be limited by the consideration of a smaller dataset, the study would make a promising contribution through computationally exploring rhythmic patterns of a great musical tradition Keywords: Music data mining, Taal and Laya recognition, North Indian classical music, Rhythmic analysis, Computational musicology
  • No Thumbnail Available
    Item
    ICT Integration in Agriculture: A Case of Sri Lanka
    (Uva Wellassa University of Sri Lanka, 2020) Pannala, P.A.M.L.; Charles, J.; Lekamge, L.S.
    Information and Communication Technology (ICT) has become an indispensable tool in several sectors including the agricultural sector. It embodies all the digital technologies used to capture, store, process, and exchange information. Traditionally, agriculture has been the backbone of the Sri Lankan economy and in the transition towards food self-sufficiency in Sri Lanka, ICT integration in the agricultural sector would be imperative. Accordingly, the main aim of the proposed study is to develop a framework for ICT integration in agriculture taking into account the Sri Lankan agricultural sector. The study would be supplemented by a systematic review of literature that identifies the different domains of research related to ICT in agriculture, existing frameworks for ICT adoption in agriculture around the world, and the global initiatives for ICT integration in the agricultural sector. The study will also survey major barriers and challenges in ICT integration in agriculture and it will provide insights on the future of ICT integration in agriculture in Sri Lanka. As a case study, the study considers the Permanent Crop Clinic Programme (PCCP) which is a plant pest and disease diagnostic and recommendation service implemented through farmer group structure called the Crop Clinics (CCs). CCs serve as an extension tool contributing to promote sustainable agriculture and also provide a unique educational experience for farmers through making recommendations based on the diagnosis of live samples. Based on a questionnaire survey employing different stakeholder groups including officers from relevant government authorities, instructors, and farmers, the study aims to provide recommendations on where and how ICT can be better integrated into the above program thereby supporting the realization of the PCCP objectives. Keywords: ICT integration, Agriculture, Sri Lanka, Permanent crop clinic programme
  • Thumbnail Image
    Item
    A Machine Learning Approach for Emotion Classification of Sri Lankan Folk Melodies
    (Uva Wellassa University of Sri Lanka, 2019-02) Charles, J.; Lekamge, S.
    Music plays a vital role in our day-to-day life and considerably more in the current digital age. It can convey and evoke powerful emotions, owing to various musical characteristics such as rhythm, melody, and orchestration. This amazing ability has motivated the researchers worldwide to discover relationships between music and emotion. As a result, various data mining tasks have been carried out where state-of-the-art machine learning techniques are utilized in music emotion classification. However, the literature reveals that these studies frequently utilize western or western classical music. Since the emotional expression in music is carried out through various ensembles of musical characteristics which are cultural-specific, generalizability of classification models trained using different ground-truth data in a new context is problematic. This demands the development of emotion classifiers for cultural-specific music which are been less explored. As an example, no considerable effort is reported in computational modeling of Sri Lankan folk melodies, despite being an abundant source of emotion expression. Therefore, we propose a machine learning approach for their emotion classification, supported by a comparison among different standard classification algorithms, further identifying a set of acoustic features contributing for improved classification accuracy. A systematic literature review has been carried out which revealed the use of classical machine learning algorithms e.g., Artificial Neural Networks, Support Vector Machines and Bayesian networks, frequently employing timbral, rhythmic, and pitch features. In the proposed study, an emotion-annotated dataset comprising of 76 music stimuli (30s; 44100Hz; stereo; 32bit; .wav) is to be utilized with MATLAB MIRToolbox for acoustic feature extraction. It is believed that the findings of the study would mark a promising start, introducing machine learning for emotion analysis in Sri Lankan folk melodies.
  • No Thumbnail Available
    Item
    Sharing Economy Business Models: A Case of Accommodation Sector in Sri Lanka
    (Uva Wellassa University of Sri Lanka, 2020) Jayaweera, S. N.; Charles, J.; Kulathunga, K.M.S.; Lekamge, L.S.
    The tourism industry has long been one of the largest export earners in the Sri Lankan economy. Accommodation is a major sector in the tourism industry that needs to be thoroughly investigated for the potential benefits to be reaped through the successful integration of ICT. The sharing economy is defined as an economic system in which assets or services are shared among individuals and/or organizations either free or for a fee, usually through the use of the Internet. Airbnb is among the best examples for such businesses that connect hosts and travelers, facilitating the process of renting without owning any such facility. However, the opportunities brought by ICT integration in the tourism industry are yet to be grasped by the Sri Lankan rural community which is nearly four times the urban population. Taken together, the main aim of the proposed study is to investigate the sharing economy business models to adopt them in the accommodation sharing business in the rural areas of Sri Lanka. The study would be supported by a comprehensive review of sharing economy business models adopted worldwide. Further, a SWOT analysis is to be carried out analysing the respective competitive environment. Based on the findings the study aims to provide a set of guidelines and recommendations to be adopted in creating and revising relevant government policies and regulations and to develop a business model for successfully implementing a sharing-based accommodation business by the Sri Lankan rural community. As a pilot study, a questionnaire survey will be carried out centering Dickwella city, Matara district employing different stakeholder groups including hosts, travelers, and relevant institutions. As also investigating the associated key concerns including sustainability, security, and privacy, the study is believed to help enhance the participation of the rural population in the sharing-based accommodation business and thereby towards improving the national economy. Keywords: Sharing economy, Business model, Tourism industry, Accommodation sector, Sri Lanka