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Classification of Carnatic Thumbnails using CNN-RNN ModelsAuthor: Amulya Sri Pulijala Date: 2021-05-22 Report no: IIIT/TH/2021/75 Advisor:Suryakanth V Gangashetty AbstractMusic signal processing is a sub branch in signal processing which is a promising area these days. Music analysis based on signal processing techniques paved a new way of generation and analysis of music. Music analysis and recognition of various swaras and ragas is inherent to human understanding. Music signal processing include various areas of research such as Synthesis of Music, Transcription, Classification, Music Information Retrieval, Raga Classification, Tala Classification, Instrument/ Voice Identification, Audio Matching, Source Separation, Tonic Identification, Intonation/melodic/rhythmic analysis, Music emotion recognition etc. The concept of Raga and Tala is integral part of Indian Classical music. Raga is the melodic component while Tala is the rhythmic component in the music. Hence, classification and identification of Raga and tala is a paramount problem in the area of Music Information Retrieval (MIR) systems. Although there are seven basic Talas in Carnatic Music, a further subdivision of them gives a total of 175 ragas. There are 72 melakartha ragas and more than thousand janya ragas. Statistical and machine learning approaches are proposed in Literature Survey to classify Ragas and Talas. However, they use complete musical recording for training and testing. As part of this thesis, a novel approach is proposed for the first time in Carnatic music to classify Carnatic music recordings using repetitive structure called Thumbnails. We proposed a parallel CNN-RNN models to classify Ragas and Talas in Carnatic music using ’Thumbnails’. Full thesis: pdf Centre for Language Technologies Research Centre |
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