Abstract
This paper presents a novel adaptive recurrent neuro-fuzzy filter (ARNFF) for speech enhancement in noisy environment. The speech enhancement scheme consists of two microphones that receive a primary and a reference input source respectively, and the proposed ARNFF that attenuates the noise corrupting the original speech signal in the primary channel. The ARNFF is a connectionist network that can be translated effortlessly into a set of dynamic fuzzy rules and state-space equations as well. An effective learning algorithm, consisting of a clustering algorithm for the structure learning and a recurrent learning algorithm for the parameter learning, is adopted from our previous research for the ARNNF construction. From our computer simulations and comparisons with some existing filters, the advantages of the proposed ARNFF for noisy speech enhancement include: 1) a more compact filter structure, 2) no a priori knowledge needed for the exact lagged order of the input variables, 3) a better performance in long-delay environment.
| Original language | English |
|---|---|
| Pages (from-to) | 3083-3088 |
| Number of pages | 6 |
| Journal | IEEE International Conference on Neural Networks - Conference Proceedings |
| Volume | 4 |
| Publication status | Published - 2004 Dec 1 |
| Event | 2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 2004 Jul 25 → 2004 Jul 29 |
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An adaptive recurrent neuro-fuzzy filter for noisy speech enhancement. / Wu, Sheng Nan; Wang, Jeen-Shing.
In: IEEE International Conference on Neural Networks - Conference Proceedings, Vol. 4, 01.12.2004, p. 3083-3088.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - An adaptive recurrent neuro-fuzzy filter for noisy speech enhancement
AU - Wu, Sheng Nan
AU - Wang, Jeen-Shing
PY - 2004/12/1
Y1 - 2004/12/1
N2 - This paper presents a novel adaptive recurrent neuro-fuzzy filter (ARNFF) for speech enhancement in noisy environment. The speech enhancement scheme consists of two microphones that receive a primary and a reference input source respectively, and the proposed ARNFF that attenuates the noise corrupting the original speech signal in the primary channel. The ARNFF is a connectionist network that can be translated effortlessly into a set of dynamic fuzzy rules and state-space equations as well. An effective learning algorithm, consisting of a clustering algorithm for the structure learning and a recurrent learning algorithm for the parameter learning, is adopted from our previous research for the ARNNF construction. From our computer simulations and comparisons with some existing filters, the advantages of the proposed ARNFF for noisy speech enhancement include: 1) a more compact filter structure, 2) no a priori knowledge needed for the exact lagged order of the input variables, 3) a better performance in long-delay environment.
AB - This paper presents a novel adaptive recurrent neuro-fuzzy filter (ARNFF) for speech enhancement in noisy environment. The speech enhancement scheme consists of two microphones that receive a primary and a reference input source respectively, and the proposed ARNFF that attenuates the noise corrupting the original speech signal in the primary channel. The ARNFF is a connectionist network that can be translated effortlessly into a set of dynamic fuzzy rules and state-space equations as well. An effective learning algorithm, consisting of a clustering algorithm for the structure learning and a recurrent learning algorithm for the parameter learning, is adopted from our previous research for the ARNNF construction. From our computer simulations and comparisons with some existing filters, the advantages of the proposed ARNFF for noisy speech enhancement include: 1) a more compact filter structure, 2) no a priori knowledge needed for the exact lagged order of the input variables, 3) a better performance in long-delay environment.
UR - http://www.scopus.com/inward/record.url?scp=10944234990&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:10944234990
VL - 4
SP - 3083
EP - 3088
JO - IEEE International Conference on Neural Networks - Conference Proceedings
JF - IEEE International Conference on Neural Networks - Conference Proceedings
SN - 1098-7576
ER -