Enhanced Audio Source Separation and Musical Component Analysis

Audio source separation is a cornerstone problem for researchers engaged in Digital Signal Processing and Artificial Intelligence. Music unmixing is the task of decomposing music into its constitutive components, like yielding separated stems for the vocals, bass, drums, accompaniment, jazz, and others from a mastered song track. Due to recent progress in the field of Deep Learning, researchers have been able to devise Neural Networks that can perform this task with considerable precision. However, these models lack performance when dealing with generic musical audio despite having decent utility over specific music genres. The proposed system aims to develop a universal platform-independent software for accurate domain-specific implementation of music source separation for acute subsets of stereo audio using the Bidirectional Long Short Term Memory (BLSTM) architecture of Recurrent Neural Networks. The Deep Neural Network helps demix audio mixtures into the jazz solo and its accompaniment. In cohesion, these two models extract five independent audio stems from the original audio with reasonable accuracy. Further, the extracted accompaniment stem is processed using ConvNet Model to estimate the instrumental components. In synchronization, these three models can break down audio to its fundamental elements.