Shanit Ghose | Oberoi International School JVLR |
This research paper delves into the intriguing and often underexplored domain of online stem-splitting tools and their efficiency in handling diverse musical genres. Stem separation, a crucial process for music producers, is the key to isolating and reusing specific elements of a track. However, a significant limitation in the current landscape of stem-splitting tools is their ineffectiveness in the context of regional music genres, such as Indian classical music. This paper investigates this issue by examining the frequency behavior of instruments as represented in EQ graphs and analyzing how it impacts the performance of stem-splitting algorithms. Our research reveals that a majority of these tools are optimized for the characteristics of Western pop music, which typically features instruments with well-defined and distinct frequency profiles. This design limits their applicability in genres like Indian classical music, where a wide array of unique instruments produces complex, harmonically rich sounds at various frequencies. These findings highlight a dearth of efficient stem-splitting tools for regional music genres, depriving music producers of valuable resources to resample and reuse stems effectively. To bridge this gap, this study emphasizes the need for customized models and tools that can adapt to the unique frequency behavior of instruments in these regional genres. This paper proposes that specialized datasets and finely-tuned parameters should be employed to create tools that are genre-specific, enabling music producers to harness the full potential of stem separation in a broader musical context. This research contributes to the ongoing discourse on music technology and offers a roadmap for the development of more inclusive and versatile stem-splitting tools.