# Music Similarity Detection Through Comparative Imagery Data
## 1 Introduction  
It is common for artists to be influenced by the greats of their time, and imitate their work and style in their early years. After all, as [PERSON] said, \"imitation is the sincerest form of flattery\" [1]. However, when imitation is of a certain form or reaches a certain level, it may become copyright infringement and plagiarism. In recent years, music generated by generative artificial intelligence (AI) models gained much attention, with a swirl of excitement, suspicion, and fear. There are ongoing efforts to develop a copyright and AI framework that rewards human creativity (e.g., [2]). Existing technical methods for detecting similarity patterns in music can broadly be categorized into two groups: _context-based_ and _content-based_. Context-based methods use contextual metadata, an umbrella term for musical information such as user ratings, radio and user playlists that the songs are a part of, and the lyrics and collaborative tags made by users on music websites [3]. Meanwhile, content-based methods extract music features from audio files, such as melodic structure, among many others. Contextual methods perform well in music recommendation systems and music clustering based on similarity. For example, [PERSON] et al. [4] investigated the similaritybetween a wide array of artist's music and developed a recommendation strategy using text-based metadata. [PERSON] et al. (2018) compared user-assigned tags and features extracted from audio signal and showed that the former was outperformed by the latter in classifying the genre of musical pieces. [PERSON] (2018) proposed a weighting system for moderating the association between tags and songs in retrieval, which was utilized in the term frequency-inverse document frequency (TFIDF) metric. According to [PERSON] et al. (2018), 83% of text-based recommendation systems currently implement TFIDF. Content-based methods exploit stored music pieces (audio or MIDI) in various ways, using _text-based_ and _audio-based_ approaches. Text-based approaches focus on textual representations where each music piece is an array of symbols encoding the relative locations of musical notes chronologically. With textual representations, some researchers have applied commonly used similarity metrics, such as cosine similarity or Euclidean distance, in the literature (e.g., ([PERSON] et al., 2018; [PERSON] et al., 2018)). Others developed their own music similarity metrics, e.g., fuzzy vectorial-based similarity ([PERSON] et al., 2018). Text-based methods have been used to detect melodic plagiarism. However, an unsettled question is whether melodic similarity is the only factor of music plagiarism. Audio-based approaches rely on features extracted from music files and analyze music pieces in their feature vectors. Typically, music signals contained in audio files are processed via variations of Fourier and wavelet transforms. [PERSON] and [PERSON] (2018) used spectral features to find similar songs and detect emotion in music. [PERSON] et al. (2018) used melodies extracted from MIDI files to cluster similar songs, in conjunction with the K-nearest neighbors (kNN) and support vector machine (SVM) methods. In essence, this is similar to a text-based approach. [PERSON] (2018) evaluated the performance of several machine learning (ML) techniques for detecting music plagiarism, including logistic regression, naive Bayes, and random forest, among others. [PERSON] et al. (2018) applied neural networks to spectral features. As there are numerous ways for specifying a feature in music, many features, which may be known or unknown currently, may potentially be used to characterize music plagiarism. It will be a long-term endeavor for researchers to find the most effective feature specifications (in a vast feature space) for detecting music plagiarism.