Abstract
Filter bank theory is used to identify the approximation and detail coefficients of the wavelet filter that are used to identify the scaling and wavelet function of the wavelet. If the filter bank coefficients of the sounds of musical instruments are calculated then it is possible to identify the signature wavelet of the sound signal, which can be used to reconstruct the original signal with negligible error. The filter bank coefficients can be identified with adaptive algorithms viz. LMS, NLMS and RLS. Among the three algorithms, RLS algorithm perform better in all regard and the algorithm converges very fast i.e. number of iterations are less. Hence an algorithm based on RLS algorithm is developed to find out scaling and wavelet functions of the sounds of musical instruments with better accuracy and speed of convergence.
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Sharma, R., Pyara, V.P. (2017). Signature Wavelet Identification of Sounds of Musical Instruments Using RLS Algorithm. In: Attele, K., Kumar, A., Sankar, V., Rao, N., Sarma, T. (eds) Emerging Trends in Electrical, Communications and Information Technologies. Lecture Notes in Electrical Engineering, vol 394. Springer, Singapore. https://doi.org/10.1007/978-981-10-1540-3_27
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DOI: https://doi.org/10.1007/978-981-10-1540-3_27
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