r the singers do not express it, the song would be considered a gnewh or gstrangeh Taiwanese song. This feature is similar to Japanesefs Enka and to out-of-the-ordinary music analysis units, such as timbre, melody, harmony, and rhythm. In addition to the features in a songfs genre, language is an important category in music taxonomy. However, separating songs into language levels may hinder the study of cross-language commonalities in songs. Nicolas Scaringella et al. (2006) have mentioned the dilemma of classification:
...[T]his semantic confusion within a single taxonomy can lead to redundancies that may not be confusing for human users but that may hardly be solved by automatic systems. Furthermore, genre taxonomies may be dependent on cultural references. For example, a song by the French singer Charles Aznavour would be considered variety in France but would be filed as world music in the United Kingdom (2006:134).
In addition to vocals, most songs are accompanied by instruments which may play a more important role beyond making sound, especially for ethnomusicologists and anthropologists. For example, the sound of a nose flute in Taiwanfs aboriginal Paiwan can invoke emotions and memories for both the audience and the player, even if the player does not finish the song. This situation is similar to the poem by Bai Juyi in the Tang Dynasty, which features gemotion before melody ’¬Θ²ζLξ.h This relationship between timbre and instruments is strong and it should be classified carefully to prevent losing this information.
Taxonomy, and structured and document-oriented data
Traditional music classification is hierarchical, such as pop music in Mandarin or Taiwanese categories. For romantic pop music, an emotion tag is used to label the song (Figure 1). Figure 2 shows the simple and clear classification of music genres in Applefs iTunes store. However, this taxonomy provides only a simplified music world, so more inter-relational data is needed to help explore and retrieve music information. For example, Zhijung Zhao et al. (2010) have explored how to detect emotions in Chinese and Western music based on timbre, rhythm, and pitch. They used four mood categories (anxiousness, depression, contentment, and exuberance) to classify the music in their auto-detection system. Although their mood classification system is simplified, traditional structured classification cannot be used to integrate their research into the existing system.
Data from ethnomusicology has exposed shortcomings in existing music taxonomy. Linton C. Freeman and Alan P. Merriam (1956) considered gthe individuality of esthetic expression as it is shaped by the customs of a particular group is more sharply established.h They computed frequencies of use of major seconds, minor thirds, and total intervals in 20 Trinidad Rada and Brazilian Ketu songs. After weighting various measurements via a lambda score, the statics displayed the differences between songs in these two areas. They then attempted to solve the classification challenge through statistics rather than distinguishing songs via classification terms. Their approach not only reflected the debater of classification in cultural anthropology but also served as a reminder that creating a hierarchical classification system may be useful only in a specific field, such as the commercial music market.
In contrast, document-oriented data contains more variations. For example, Samingadfs (IϊN) Wandering (¬QL) is a Mandarin song with a Taiwan aboriginal rhythm and melody. In a document-oriented database, this song could be stored in the following categories:
Entity 1. Samingad-> Singer, Wandering, Mandarin, Puyuma (Ϊμ), Taiwan Aboriginal, Nostalgia.
Entity 2. Wandering-> Song, Mandarin, Samingad, Puyuma, Nostalgia.
Entity 3. Nostalgia-> Samingad, Song, Puyuma, Mandarin, Alone.
Entity 4. Lonesome-> Wandering, Mandarin, Nostalgia.
From the examples above, it can be seen that document-oriented taxonomy is flexible and it is similar to Gilles Deleuze and Félix Guattarifs rhizome concept. They adopted this term to de