# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 1 Introduction  
Traditional music is a pillar of cultural identity, reflecting the heritage and uniqueness of every community. However, the preservation of this type of music faces significant challenges due to its oral transmission tradition and the growing influence of commercial music and mass media in contemporary society. The potential loss of traditional music threatens the authenticity and cultural diversity of local communities. Digitalization is crucial for its preservation and global dissemination, contributing to a greater appreciation and understanding of musical traditions among international audiences. One of the distinctive properties of traditional music is its mode, which has been studied by musicologists without digital support to gain a deeper understanding of cultural contexts [1, 2, 3]. However, when dealing with large datasets, this analysis requires the assistance of computational tools. Specifically, the application of Artificial Intelligence (AI) techniques to traditional music offers new opportunities to preserve and study its unique characteristics, with mode standing out as a distinctive and essential feature of this musical genre [4, 5]. In music theory [2, 3], a mode is a specific arrangement of whole and half steps within an octave, forming the basis of a melody or harmonic structure, i.e., it is the scale on which that melody is based. Modes are particularly relevant in folk music traditions, where melodies often do not conform to the harmonic expectations of major or minor keys. In Western folk music, modes such as Dorian, Mixolydian, and Aeolian are commonly used, each carrying a distinct tonal color and emotional quality. A very similar concept is the _key_ of a musical piece. A key in tonal music is defined by a tonic (central pitch) and a specific major or minor scale that establishes a hierarchicalrelationship between notes and chords. In a given key, harmonic progressions follow a structured pattern, with certain chords (such as the dominant-to-tonic motion) reinforcing a strong sense of resolution. The key determines how tension and release function in the music, providing a framework for both melody and harmony. While the concepts of mode and key share similarities (both describing pitch organization within a scale), they differ significantly in their musical functions and structural implications. A mode does not necessarily imply tonal harmony or a functional chord system. Instead, it primarily governs melodic characteristics, defining which notes are used and how they interact, without requiring harmonic resolution. This distinction is crucial in folk music, where modal melodies often lack strong harmonic direction and may drift between tonal centers or avoid traditional cadences that reinforce a key. As a result, standard key detection algorithms, which rely on harmonic context and chord functions, may not be directly applicable to mode detection. Instead, mode classification requires analysis of melodic features, scale-degree distributions, and intervallic patterns rather than harmonic relationships. In previous work, we developed a novel approach to mode detection that aims to identify the mode of a symbolically encoded modal music piece within the Irish folk music tradition [6]. This work demonstrates that detecting modes through machine learning algorithms is possible but also highlights the lack of knowledge regarding how to improve detection or determine the best approach. The field of AI algorithms, preprocessing techniques, classifiers, and paradigms is vast, requiring more specific research. Building on this foundation, the current paper presents a comparative study of various preprocessing techniques for unsupervised classification and their influence on the resulting performance. Specifically, we focus on three key aspects: exploring the impact of different feature representations, evaluating the effectiveness of alternative representation of data paradigms, and assessing the performance of a variety of unsupervised learning algorithms.