The single hidden layer neural network based classifiers for Han Chinese folk songs

This thesis investigates the application of a few powerful machine learning techniques in music classification using a symbolic database of folk songs: The Essen Folksong Collection. Firstly, a meaningful and representative set of theory-based method of encoding Chinese folk songs, called the musical feature density map (MFDMap) is developed to enable efficient classification by machines. This encoding method effectively encapsulates useful musical information that is readable by the machines and at the same time can be easily interpreted by humans. This encoding will aid ethnomusicologists in future folk song research. The extreme learning machine (ELM), an extremely fast machine learning algorithm that utilizes the structure of the single-hidden layer feedforward neural networks (SLFNs) is employed as the machine classifier. This algorithm is capable of performing at a very fast speed and has good generalization performance. The application of the ELM classifier and its enhanced variant called the regularized extreme learning machine (R-ELM), in real-world multi-class folk song classification is examined in this thesis. The effectiveness of the MFDMap encoding technique combining with the ELM classifiers for multi-class folk song classification is verified. The finite impulse response extreme learning machine (FIR-ELM) is a relatively new learning algorithm. It is a powerful algorithm in the sense that its robustness is reflected in the design of the input weights and the output weights. This algorithm can effectively remove input disturbances and undesired frequency components in the input data. The capability of the FIR-ELM in solving complex real-world multi-class classification is examined in this thesis. The MFDMap performed more effectively with the FIR-ELM. The classification accuracy using the FIR-ELM is significantly better than both the ELM and the R-ELM. The techniques of folk song classification proposed in this thesis are further investigated on a different data samples. These techniques are also applied to the European folk songs, a culture that is very different from the Chinese culture, to investigate the flexibility of the learning machines. In addition, the roles and relationships of four music elements: solfege, interval, duration and duration ratio are investigated.