Classification of Classical Indian Music Tabla Taals using Deep Learning

Abstract

In the research that we are bringing to light, we profoundly explore the categorization of Classical Indian Music Tabla Taals. This emphasizes widely recognized taals such as Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. To push the boundaries of our understanding, we implement a mixed-methods approach tethering both Feedforward Neural Networks (FNN) and Convolutional Neural Networks (CNN). These state-of-the-art technologies enable us to dissect and categorize tabla taals efficiently. In essence, the hallmark of Classical Indian music is its complex and multifaceted rhythms brought to life by the primal percussive instrument - the tabla. The conception and reproduction of these nuanced taals require technical finesse. Thus, accompanying the digital revolution and the eclectic musical preferences, it becomes essential for advanced methodologies to pinpoint and classify tabla taals. The hardcover of our research opens up to the magnificent crafting of an unmatched model employing both FNN and CNN. This blend enables us to recognize diverse features unique to tabla taals like Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. The model obtained its bosom knowledge during training from an assortment of Classical Indian music recordings showcasing these invigorating taals. This fosters a broader understanding regarding the array of minute differences brimming within each rhythmic inheritance. To bring user interaction to life, we have embedded a Graphical User Interface (GUI). This empowers users to introduce an audio file filled with table music from the taals listed and receive on-the-spot recognition. refining their connection and knowledge of the taal in question. Our research findings procure paramount importance in the scape of music analysis, especially framed within the heart of Classical Indian Music. We propose a system that would serve as a tool for amateur table players to learn the skill well and master their art. Instructors could also utilize it for training purposes. It opens a new window of possibilities providing an advanced model for intuitive, swift, and accurate automated identification of tabla taals.