Abstract
LD-aCELP algorithm has 2.5ms delay and speech coding rate is 8Kbit/s. The adaptive codebook and backward pitch detection is used. LDaCELP depends on the Levinson-Durbin (L-D) algorithm to update gain filter coefficients. Because quantizer has not existed at optimizing gain filter, the quantization SNR can not be used to evaluate its performance. We use a new scheme to estimate SNR so that the gain predictor can be separately optimized with the quantizer. In this paper, using this scheme L-D method is replaced by three different methods which are the weighted L-S recursive filter, the finite memory recursive filter and the BP neural network, respectively. Experiments showing, they are all very effective to improve gain filter performance. The weighted L-S algorithm has the best effect, which is accordant with real speech coding. Its average segment SNR is higher than LD-aCELP about 0.720dB.
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© 2011 Springer-Verlag Berlin Heidelberg
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Wu, S., Zhang, G. (2011). Improving LD-aCELP’s Gain Filter. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_15
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DOI: https://doi.org/10.1007/978-3-642-23887-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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