# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 2 Related Work
### Machine Learning Techniques in Mode Detection  
Within the domain of music computing, there has been little research on mode detection. Most existing approaches focus on identifying the major and minor modes, which dominate Western classical and commercial music. In contrast, modes prevalent in modal folk music traditions--such as the Dorian, Phrygian, Lydian, Mixolydian, and Locrian modes--remain under-researched and need deeper investigation.  
In contrast, key detection has been extensively studied in classical music. The concept of key or tonality plays a crucial role in this genre, characterized by a tonic and a scale that, together, create a framework of pitch relationships, both vertically and horizontally, within a musical composition [27].  
However, key detection holds less significance in folk music, as vocal melodies are often transposed to accommodate a singer's vocal range, and instrumental tunes are frequently adjusted to suit the capabilities of specific instruments.  
The following paragraphs will review existing literature on key detection, highlighting its relevance as a baseline for exploring mode detection in the analysis of folk music traditions.  
Computational efforts in automatic key detection have supported various content-driven applications within musicology and music theory, notably facilitating large-scale empirical analysis [28; 29]. This computational approach has found applications in music information retrieval tasks such as classification and retrieval [30; 31; 32], content analysis, and psychology, where it contributes to hierarchical representations of musical pitch [13; 14; 33]. The pursuit of automatic key detection extends to symbolic music encodings (e.g., MusicXML and MIDI) and audio recordings (Please refer to [PERSON] et al. [34] for a comprehensive review of audio key detection methods, which fall outside the scope of our work concerned with symbolic music encodings).  
Supervised machine learning algorithms have also been extensively investigated and applied to key detection [17; 18]. The utilization of labeled datasets and explicit guidance during the training phase allowed us to obtain accurate predictions in various musical contexts. [PERSON]. [17] presents an approach using different supervised algorithms, such as KNN, SVM, and CNN. Furthermore, [PERSON]. [18] tries to detect major and minor modes using an SVM algorithm. Finally, [PERSON]. [9] tries to compare different Artificial Neural Networks (ANNs) to detect the minor and major modes.  
However, the exploration of unsupervised learning approaches in key detection remains relatively limited in comparison. We can mention [35], which proposes an unsupervised approach to detect the major and minor modes based on Manhattan distance among the features of the data. Unsupervised methods have the potential to unveil underlying structures and relationships within musical data without explicit labeling, providing a more autonomous and adaptable approach. The scarcity of research in this area underscores the need for a more comprehensive exploration of unsupervised learning techniques in key detection.