Abstract

Abstract

(TranslateEd)

Abstract

A vital aspect of Indian classical music (ICM) is raga, which server as a meta metapork for compositions and IMPROVISATIONS Alike. MPORTANT MUSIC Information RETRIEVAL TASK in ICM AS It can aid Numerous Downloads Ranging from Music Recomation HugMusic CollectionsHyderabad Wealth Management. In this work, We Propose A Deep Learning Based Approach to Raga Recognition. ORAL Sequences in Music Data USING LONG Short Term Memory Based Recurren Neural Networks (LSTM-RNN). We Train and TestThe Network On Smaller Sequences Sampled from the Original Audio While The Final Inference Is Performed on the Audio as a WHOLELucknow Investment. 8.1 % and 97 % During Inference on the Comp Music Carnatic DataSet and ITS 10 Raga Subset Respectively Making ItThe state-the Raga Recognition Task. Our Approach Also Enables Sequence Ranking Which Aids FROM A Given Music a base that are closly related to the presented query sequence.

Abstract (translateed)

A key aspect of Indian Classical Music (ICM) is Raga, as the melody framework of works and improvisation music.Lag recognizes a important music information retrieval task in ICM, because it can help many downstream applications, from music recommendation to tissue large -scale music collection.In this work, we proposed the Lag identification method based on deep learning.Our method uses the time sequence in music data based on circular neural networks (LSTM-RNN) based on long and short-term memory.We conduct training and testing on the smaller sequence of the original audio, and finally reasoning the entire audio.Our method has the accuracy rate of reasoning on the COMP MUSIC Carnatic dataset and its 10 -wrail sets of 88.1%and 97%, respectively, so that it is in the most advanced position in the Lag identification task.Our method also implements the sequence ranking, which helps us with closely related and closely related melody models that are closely related to the given query sequence from a given music data set.