Raga recognition is only possible by trained musician to understand the notes based on the lead voice but a beginner is unable to decode the notes. This is significant for current scenarios in developing an automated note transcription system in Indian Classical Music (ICM). In the present research, various properties of raga and the machine learning techniques that are used for identifying the raga by a machine rather than a human or music expert are surveyed. The previously developed automatic raga recognition techniques using Carnatic and Hindustani Music, the main drawbacks and the improvements required are discussed. The present research work discusses about the future proposed models for automatic raga recognition using pitch detection algorithm, finding Tuning Offset, and Note Segmentation process. The proposed model will obtain better accuracy more than 96 % when compared to the existing CNN, GMM that obtained accuracy of 94 % and 95 %.
A raga is a unique set of notes with certain rules that carefully followed, retain and protect its purity and produce amazing musical effects. An automated raga transcription and identification is important for computational musicology, which is an important step for musicology for indexing, classifying, and recommending tunes. In the present research, the audio features such as mel frequency cepstrum coefficients (MFCCs), spectral flux, short time energy, audio feature extractor, and spectral centroid features are used for the prediction of a raga. The model showed more complexity which means it required lots of training data. The proposed enhanced spatial bound whale optimization algorithm (ESBWOA) is used that overcome the feature selection problem of high dimensional features. In addition to this, a weighted salp swarm algorithm (SSA) is used for selecting the tone-based features from the ragas based on amplitude or each raga sample. The features were fed for bidirectional long short-term memory (Bi-LSTM) network, which enhanced the success rate for raga identification and classification. The present research uses CompMusic dataset in the research work where 9 classes for Carnatic music and 7 classes in Hindustani music are considered for the classification of ragas.
Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.
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