# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 2 Related Work
### Data Preprocessing  
To obtain successful results, the data usually need to be preprocessed, extracting the features that provide useful information about the mode and key detection. Most of the previous works analyzed make use of Pitch Class Profile (PCP) extraction, based on the KK profiles by [PERSON] and [PERSON] [13; 14]. Other profiles have been researched, such as the Temperley profiles [15] and the Zweiklang profiles [16]. Our work will use the KK profiles. This selection is based on a comprehensive review of literature, revealing its prevalence in various key detection papers [8; 9; 17; 18].  
In this technique, each mode can be represented by a pitch profile composed of the number of occurrences of the 12 notes of the musical temperament. The study by [PERSON] and [PERSON] was conducted for the Ionian and Aeolian modes. The main discovery in their research was the observation that both modes exhibited analogous characteristics for different pitches, with a consistent shift of the necessary number of semitones--the smallest interval in Western equal-temperament music--to align with the tonic center. This finding implies a shared total essence between these modes, emphasizing the transpositional relationship and the perceptual stability of certain pitches across different keys. Consequently, a representative histogram of a mode, composed of the frequency of each note, can be extracted from a musical piece. From this pitch profile, mode detection can be applied.  
However, in recent years, embedding techniques have become central to various music information retrieval (MIR) tasks, including genre classification, mode detection, and recommendation systems [19; 20; 21; 22; 23]. Embeddings map musical features into continuous vector spaces, enabling more effective clustering and classification by capturing latent structure and relationships between musical elements.  
Unlike traditional handcrafted features--such as pitch histograms, n-grams, or key profiles--embedding-based approaches leverage deep learning models (e.g., autoencoders, transformer networks, or contrastive learning) to extract compact and expressive feature representations. These embeddings facilitate unsupervised and semi-supervised learning, making them particularly useful for tasks such as mode detection, where labeled data may be scarce.  
Several embedding techniques have been explored in MIR [24; 25; 26], including the following:  
* Spectrogram-based embeddings, which learn representations from time-frequency representations of audio signals;
* Symbolic music embeddings, which extract features from MIDI or ABC notation sequences, capturing melodic and harmonic patterns;
* Graph-based embeddings, which model relationships between musical elements, such as tonal connections or harmonic progressions.  
In the context of mode detection, from our point of view, embedding approaches may enhance clustering performance by providing better separation between modes in high-dimensional spaces. However, one challenge is the interpretability of embeddings, as they do not always align with traditional musical descriptors. Furthermore, the choice of embedding model and training data can significantly impact performance, requiring careful selection and tuning.