The Application of TOPSIS Decision and Random Forests Method in Tone Recognition
The goal of tone recognition is to accurately identify the type and the name of the musical instruments through processing and analyzing the sound signals. In order to reduce the influence of feature confusion on classification process, a method of tone recognition based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Random Forests is proposed. In this process, Mel Frequency Cepstral Coefficients (MFCCs) are acquired, and the quadratic sum of distance between two MFCCs and the entropy of information are computed which are used as indices to analyze and select the MFCCs based the TOPSIS decision. The selected MFCCs are used to classify tone of trumpet-piano, trumpet-cello and piano-cello, and the recognition rates were 100%, 99.9% and 100% respectively. The results are satisfactory and verify feasibility of the developed method.
|Keywords||Mel Frequency Cepstral Coefficients, Random Forests, Tone Recognition, TOPSIS|
|Conference||2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018|
Guo, R. (Rui), Hanachi, H. (Houman), Zhang, C. (Chi), & Liu, J. (2019). The Application of TOPSIS Decision and Random Forests Method in Tone Recognition. In Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 (pp. 214–220). doi:10.1109/SDPC.2018.8664946