###### Abstract
Second, the influx of software-based music editing tools over recent years has allowed musicians (even novices) to customize original pieces of music that many then go on to claim as their own unique work. Third, high-speed internet and progress with music-related middleware tools have improved the accessibility of music around the world while also speeding up composers' workflows. The current global accessibility of music, which has been facilitated by these advances, is among the fundamental reasons that have contributed to the growth of the music business. However, these advances also raise significant challenges for the music industry regarding music plagiarism. Novel methods and tools are needed to efficiently and transparently measure indications of plagiarism. Typically, when music plagiarism cases are brought to court, they often ask music experts and musicologists to analyze the similarities between two songs, the final judgments then rely entirely on whose subjective opinion of the court fact-finders choose to believe. Plagiarism claims are very challenging to verify because music experts are not reliable, especially as they are being paid by each side to support their case, and with the thousands of cases being registered, it is not practically feasible to recruit the enormous numbers of unbiased music experts needed. Hence, a technical tool for music similarity analysis between two songs can play the vital role of a scientific reference for decision-making, speeding up the analysis process, and assisting in faster judgments being made. Therefore, a computer-based approach to musical similarity comparisons is needed to detect plagiarism efficiently. Thus far, various similarity approaches have been applied to music retrieval techniques. However, few pieces of research have addressed detecting when music has been copied, including [6; 7; 8], respectively. Due to the limitations of the current state-of-the-art methods, the accuracy of the comparison results is often limited. Existing similarity models also find it challenging to address another issue: computing the degree of plagiarism present. In real-world music comparison, it is not enough to just determine if a song is similar to another one, we must also know how similar it is (using a score/measure), to help determine if the similarity is intentional or not. In addition to straight plagiarism, there is another issue of stealing from coworkers or not giving them proper credit. In their paper,Cameron et al. [3] raised the question of how to determine the various contributions from each member of a music group. The research objectives of this work are as follows:  
* To develop a solution that allows users to accurately compare two pieces of music, with accuracy being the main goal of the model above other considerations. * Use a provenance-based approach, which can further help to identify the source's trustworthiness and determine the contribution of a musician or a group of musicians to a piece of music. * To find a deep learning-based solution that learns from expert knowledge regarding similarity comparison and then classifies the similarity level of any two given songs. * To compare the similarity among the various musical features, our model identifies the given pieces of music and also discovers the right weights for those features. Our provenance-based method focuses on the origin of the music as well as the authenticity of the sources. Provenance, when it comes to music data, provides authentic information about the history of the musical piece and its composer. This information improves the overall accuracy of our process and helps the model identify plagiarism without hesitation or long deliberation. In this paper, we present a novel music plagiarism detection approach called TruMuzic that exploits provenance information in music as well as the music content itself. Music theory states that there are weak and strong beat positions in musical composition [9].