Browsing by Author "Chung, JF"

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A low-cost and application-driven digital signal processor for speech/audio processing
(2004)
Chung, JF
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a new application-driven digital signal processor, called LASP24 (Low-cost Application-driven Speech Processor, 24-bit data width), for speech and audio signal processing is designed. Two applications, MELP and reverberation algorithms, are performed on LASP24. The developed emulator can quickly verify these two algorithms. The design has been integrated in the total area of 6.5 mm(2) by using UMC 0.18 mu m 1P6M technology and fabricated. The maximum clock frequency is about 100 MHz with a single 1.8V supply.
Cellular neural networks and PCA neural networks based rotation/scale invariant texture classification
(2004)
Lin, CT
;
Chen, SA
;
Huang, CH
;
Chung, JF
;
電控工程研究所
In this paper, we proposed a new index which can be used to classify the texture image. Because of the adjustment of image capture device or the distortion of image capture, the texture image may be transformed. Usually those transformations included rotation and scale. The proposed method provides an algorithm to avoid those effects respectively. This approach is the combination of Cellular Neural Networks and Principle Component Analysis Neural Networks. This fact implies it is a feed-forward neural networks, and it does not need any training set.
Multiband room effect simulator for 5.1-channel sound system
(2005)
Chung, JF
;
Liu, DJ
;
Lin, CT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper investigates a new room effect simulator with different quality of audio sound sources for 5.1-channel sound system. The audio effect relates its impulse response with different room-dimension. The multiband equalizer is designed to generate many kinds of music impression. We also modify the mono Dolby surround decoder into a simplified surround decoder so that the stereo room simulator can generate 5.1-channel surround outputs. All experiments are at a sampling rate of 44.1k Hz and 16 bits per sample. By the room effect simulator, we can get a full and a live listening experience.
Support-vector-based fuzzy neural network for pattern classification
(2006-02-01)
Lin, CT
;
Yeh, CM
;
Liang, SF
;
Chung, JF
;
Kumar, N
Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijenn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.