@article{(Open Science Index):https://publications.waset.org/pdf/2060, title = {Musical Instrument Classification Using Embedded Hidden Markov Models}, author = {Ehsan Amid and Sina Rezaei Aghdam}, country = {}, institution = {}, abstract = {In this paper, a novel method for recognition of musical instruments in a polyphonic music is presented by using an embedded hidden Markov model (EHMM). EHMM is a doubly embedded HMM structure where each state of the external HMM is an independent HMM. The classification is accomplished for two different internal HMM structures where GMMs are used as likelihood estimators for the internal HMMs. The results are compared to those achieved by an artificial neural network with two hidden layers. Appropriate classification accuracies were achieved both for solo instrument performance and instrument combinations which demonstrates that the new approach outperforms the similar classification methods by means of the dynamic of the signal.}, journal = {International Journal of Electrical and Computer Engineering}, volume = {6}, number = {7}, year = {2012}, pages = {678 - 683}, ee = {https://publications.waset.org/pdf/2060}, url = {https://publications.waset.org/vol/67}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 67, 2012}, }