{"title":"Songs Recommendation using Context-Based Semantic Similarity between Lyrics","authors":"Vidit Gupta, J. S., Somesh Kumar","doi":"10.1109/INDISCON53343.2021.9582158","DOIUrl":"https://doi.org/10.1109/INDISCON53343.2021.9582158","url":null,"abstract":"With the rapid growth of the internet, many songs and music are readily available for users on various platforms. The number, however, gets so huge that the user might get overwhelmed when it comes to selecting a follow-up song. A recommender system comes in handy in such situations, where users can choose a recommended piece based on their likes and dislikes. There can be various metrics in developing a song recommender system, lyrics being one of them. In this paper, a song recommendation system is proposed on English songs, which uses the contextual embeddings to extract the context out of the song lyrics, identifies semantic similarity between these lyrics, and gives the most similar songs to the user based upon his choice. The dataset is taken from musixmatch.com, containing around 3300 songs. Following the pre-processing of the data, the context is extracted from the lyrics using Google's Universal Sentence Encoder algorithm. The proposed methodology achieves an F1 score of 0.7700, which shows that the accuracy of the proposed model is better than the available models in the literature for the song recommendation system using the lyrics of the songs.","PeriodicalId":167849,"journal":{"name":"2021 IEEE India Council International Subsections Conference (INDISCON)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133835687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Smog Particulate Matter Detector Using ZigBee","authors":"Rujuta Amar Kelkar","doi":"10.1109/INDISCON53343.2021.9582251","DOIUrl":"https://doi.org/10.1109/INDISCON53343.2021.9582251","url":null,"abstract":"Wired equipment was traditionally used to gather required information related to Smog. Smog is a kind of haze intensified by atmospheric pollutants. A wireless sensor module is used for data transmission and collection in a more efficient way using IEEE protocol ZigBee. The Aid of data visualization with effective use of network algorithms is made to detect particulate matter which is an important constituent of smog. Smog detection has become important as it helps in determining the air quality index of a particular area. For this, a smog detection module is introduced on a small scale which uses particulate matter analyzers. The analysis of the concentration of smog at different times of the day is made for records. These records may be worked upon in the future to reduce smog-associated problems faced by the community. Better plans and actions can be implemented by environmentalists and governments for improved general health and administration.","PeriodicalId":167849,"journal":{"name":"2021 IEEE India Council International Subsections Conference (INDISCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121790085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hindi Text to Speech Conversion","authors":"Eesha Poonja A, Geeta Shet","doi":"10.1109/INDISCON53343.2021.9582198","DOIUrl":"https://doi.org/10.1109/INDISCON53343.2021.9582198","url":null,"abstract":"Recognition of handwritten text and its conversion to speech is one of the most challenging areas of research because the most crucial step involved in this process is to identify the text written by different individuals which vary greatly in its appearance. In this paper, Hindi text to speech conversion has been proposed. Here an image of the handwritten Hindi text is preprocessed and segmented. Followed by that the segmented character is recognized and converted to speech. Support Vector Machine and projection method are the two main techniques used in this process.","PeriodicalId":167849,"journal":{"name":"2021 IEEE India Council International Subsections Conference (INDISCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121889189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}