Computational timbre and tonal system similarity analysis of the music of Northern Myanmar-based Kachin compared to Xinjiang-based Uyghur ethnic groups

Abstract

The music of Northern Myanmar Kachin ethnic group is compared to the music of western China, Xijiang based Uyghur music, using timbre and pitch feature extraction and machine learning. Although separated by Tibet, the muqam tradition of Xinjiang might be found in Kachin music due to myths of Kachin origin, as well as linguistic similarities, e.g., the Kachin term 'makan' for a musical piece. Extractions were performed using the apollon and COMSAR (Computational Music and Sound Archiving) frameworks, on which the Ethnographic Sound Recordings Archive (ESRA) is based, using ethnographic recordings from ESRA next to additional pieces. In terms of pitch, tonal systems were compared using Kohonen self-organizing map (SOM), which clearly clusters Kachin and Uyghur musical pieces. This is mainly caused by the Xinjiang muqam music showing just fifth and fourth, while Kachin pieces tend to have a higher fifth and fourth, next to other dissimilarities. Also, the timbre features of spectral centroid and spectral sharpness standard deviation clearly tells Uyghur from Kachin pieces, where Uyghur music shows much larger deviations. Although more features will be compared in the future, like rhythm or melody, these already strong findings might introduce an alternative comparison methodology of ethnic groups beyond traditional linguistic definitions.

Machine learning of Finite-Difference Time Domain (FDTD) physical modelling sound simulations of drumhead paste pattern distributions

“…The trained map is then used for detecting the best matches of each input vector again calculating the distances di,j as a scalar product and choosing the neuron with minimum distance as best match. The Apollon and Computational Phonogram Archiving (COMSAR) frameworks [8][9][10][11][12] were used to train and analyze the SOMs. For each simulated drum sound, the first 16 partials were detected by pick-peaking the FFT spectra and the ratio of partial frequencies to the frequency of the fundamental were calculated and used as the feature vector for training.…”