The field of music has been a subject of great fascination since the inception of human civilization, characterized by continuous advancements and developments. The availability of music teachers has been a challenge for anyone seeking to learn to play music, particularly due to the diverse range of percussion and string instruments that have been invented over time. Consequently, a significant challenge faced by many artists pertains to acquiring the necessary materials for learning, such as understanding the background or comprehending the specific role of a single instrument within an audio clip. Therefore, locating a suitable music track for practice is a considerable challenge, leading many musicians to encounter difficulties in this regard. Numerous attempts have been made by researchers and practitioners to extract the signal of a musical instrument from an audio track as a means of resolving this issue. However, the identification of a single instrument, specifically the violin, is being considered. This study considers various sets of instrument data. There exist numerous possibilities within this expansive domain, and a multitude of methodologies have been developed and explored by various academics. Certain individuals have utilized statistical models for computation, while others have experimented with other filter models. The features to be extracted for the identification of the musical instrument, specifically the violin, are determined by analyzing the spectrogram. Nevertheless, there exist several prospects in this domain that can be explored through various methodologies for future implementation.