###### Abstract  
The digitalization of music has led to increased availability of music globally, and this spread has further raised the possibility of plagiarism. Numerous methods have been proposed to analyze the similarity between two pieces of music. However, these traditional methods are either focused on good processing speed at the expense of accuracy or they are not able to properly identify the correct features and the related feature weights needed for achieving accurate comparison results. Therefore, to overcome these issues, we introduce a novel model for detecting plagiarism between two given pieces of music. The model does this with a focus on the accuracy of the similarity comparison. In this paper, we make the following three contributions. First, we propose the use of provenance data along with musical data to improve the accuracy of the model's similarity comparison results. Second, we propose a deep learning-based method to classify the similarity level of a given pair of songs. Finally, using linear regression, we find the optimized weights of extracted features following the ground truth data provided by music experts. We used the main dataset, containing 3800 pieces of music, to evaluate the proposed method's accuracy; we also developed several additional datasets with their own established ground truths. The experimental results show that our method, which we call 'TruMuzic', improves the overall accuracy of music similarity comparison by 10% compared to the other state-of-the-art methods from recent literature. music plagiarism detection; data provenance; deep learning; data trustworthiness; music information retrieval +
Footnote †: journal: Medical  
refers to the degree of plagiarism between two songs, is a critical issue, since knowing how similar two songs are help in discerning intentional copies from unintentional copies. To resolve these issues, several organizations function as bridges by offering a platform for artists and other businesses to interact and secure their work concerning the relevant copyright laws. The ASCAP (American Society of Composers, Authors, and Publishers), which is a world leader in performing rights and advocacy for music creators, reported higher domestic revenues of 1.27 billion USD in 2019, a 47 million USD increase over 2018. Other organizations that are similar to ASCAP, such as SACEM (Society of Authors, Composers, Editors, and Musicians) and BMI (Broadcast Music Incorporation), work in their own countries and regions, performing similar roles. One of the main aims of these organizations is to help in the decision-making process once complaints about plagiarism surface. Music plagiarism cases sometimes end in voluntary, out-of-court settlements. However, these settlements are not usually guaranteed to be successful for the artist that has been copied due to the different dynamics involved in monopoly negotiations. In addition, the long delay between the original accusation judgment is also a critical issue, even in successful ASCAP, SACEM, or volunteer settlements [3]. Nevertheless, a substantial number of songs have been copied, and this malpractice continues at a fast pace. Detecting plagiarism is a challenging task. For instance, in 2004, SACEM was only able to verify 18,000 out of 250,000 registered complaints [4]. Unfortunately, with the increasing number of plagiarism cases, this gap is only getting wider every year. In the past two decades, the music industry has witnessed several technological advancements that directly or indirectly contribute to music plagiarism. First, the digitalization and compression techniques used for storing, processing, collecting, and querying music make listening to music vastly more affordable and accessible. _Discogs_ is a crowd-funded database that offers 1.5 billion tracks [5]. While other music databases or services, such as YouTube or SoundCloud also provide easy access to a vast amount of music.