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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Liang, SF

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    Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks
    (2000-01-01)
    Liang, SF
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    Su, AWY
    ;
    Lin, CT
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A physical modeling method for electronic music synthesis of plucked-string tones by using recurrent networks is proposed. A scattering recurrent network (SRN) which is used to analyze string dynamics is built based on the physics of acoustic strings, The measured vibration of a plucked string is employed as the training data for the supervised learning of the SRN, After the network is well trained, it can be regarded as the virtual model for the measured string and used to generate tones which can be very close to those generated by its acoustic counterpart. The "virtual string" corresponding to the SRN can respond to different "plucks" just like a real string, which is impossible using traditional synthesis techniques such as frequency modulation and wavetable. The simulation of modeling a cello "A"-string demonstrates some encouraging results of the new music synthesis technique. Some aspects of modeling and synthesis procedures are also discussed.
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    Recurrent neural-network-based physical model for the chin and other plucked-string instruments
    (2000-11-01)
    Liang, SF
    ;
    Su, AWY
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A new physical model with neural networks is presented. The structure of the network is designed for the analysis of plucked-string instruments, and this network is also used as the corresponding synthesis engine. The proposed approach also provides a general and automatic way of determining suitable synthesis parameters by using a supervised neural network training algorithm with recorded sounds of a specific played instrument as the training vector. This is a general method and can be used for any plucked-string instrument. A traditional Chinese plucked-string instrument, called the Chin, is used as the target instrument to demonstrate this new synthesis method.
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    A class of physical modeling recurrent networks for analysis/synthesis of plucked string instruments
    (2002-09-01)
    Su, AWY
    ;
    Liang, SF
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A new approach is proposed that closely synthesizes tones of plucked string instruments by using a class of physical modeling recurrent networks. The strategies employed in this paper consist of a fast training algorithm and a multistage training procedure that are able to obtain the synthesis parameters for a specific instrument automatically. The training vector can be recorded tones of most target plucked instruments with ordinary microphones. The proposed approach delivers encouraging results when it is applied to different types of plucked string instruments such as steel-string guitar, nylon-string guitar, harp, Chin, Yueh-chin, and Pipa. The synthesized tones sound very close to the originals produced by their acoustic counterparts. In addition, this paper presents an embedded technique that can produce special effects such as vibrato and portamento that are vital to the playing of plucked-string instruments. The computation required in the resynthesis processing is also reasonable.
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    A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system
    (2004-02-01)
    Lin, CT
    ;
    Wu, RC
    ;
    Chang, JY
    ;
    Liang, SF
    ;
    電控工程研究所
    In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-constructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two,parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are on-line available for demonstration.
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    Modeling and analysis of acoustic musical strings using Kelly-Lochbaum lattice networks
    (2004-11-01)
    Liang, SF
    ;
    Su, AWY
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    Musical strings play important roles in the timbre of a stringed instrument. An instrument installed with different strings can produce very different sounds. To model a plucked-string instrument physically, analyzing its strings may be a good starting point. In this paper, a novel strategy for the modeling and analysis of acoustic musical strings is proposed. In order to obtain the vibration of an acoustic string, a measurement device with multiple sets of electromagnetic pickups is built. A recurrent network based on the Kelly-Lochbaum structure with multiple sets of system parameters is proposed to simulate the time-varying vibrating behavior of a target string. A fast training algorithm combining BPTT and SARPROP is also proposed to train the network automatically. The well-trained recurrent network based model can be regarded as a close simulation model of a real string, and using the model, it becomes easier to understand the characteristics of a string under different conditions when it vibrates.
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    Estimating driving performance based on EEG spectrum analysis
    (2005)
    Lin, CT
    ;
    Wu, RC
    ;
    Jung, TP
    ;
    Liang, SF
    ;
    Huang, TY
    The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.
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    An on-line ICA-mixture-model-based self-constructing fuzzy neural network
    (2005-01-01)
    Lin, CT
    ;
    Cheng, WC
    ;
    Liang, SF
    ;
    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    This paper proposes a new fuzzy neural network (FNN) capable of parameter self-adapting and structure self-constructing to acquire a small number of fuzzy rules for interpreting the embedded knowledge of a system from the given training data set. The proposed FNN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy-rule-based model with neural network's learning ability. There are no rules initiated at the beginning and they are created and adapted through an on-line learning processing that performs simultaneous structure and parameter identification. In the structure identification of the precondition part, the input space is partitioned in a flexible way according to newly proposed on-line independent component analysis (ICA) mixture model. The input space is thus represented by linear combinations of independent, non-Gaussian densities. The first input training pattern is assigned to the first rule initially by the on-line ICA mixture model. Afterwards, some additional significant terms (input variables) selected by the on-line ICA mixture model will be added to the consequent part (forming a liner equation of input variables) incrementally or create a new rule in the learning processing. The combined precondition and consequent structure identification scheme can make the network grow dynamically and efficiently. In the parameter identification, the consequent parameters are tuned by the backpropagation rule and the precondition parameters are turned by the on-line ICA mixture model. Both the structure and parameter identifications are done simultaneously to form a fast learning scheme. The derived on-line ICA mixture model also provide a natural linear transformation for each input variable to enhance the knowledge representation ability of the proposed FNN and reduce the required rules and achieve higher accuracy efficiently. in order to demonstrate the performance of the proposed FNN, several experiments covering the areas of system identification, classification, and image segmentation are carried out. Our experiments show that the proposed FNN can achieve significant improvements in the convergence speed and prediction accuracy with simpler network structure.
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    A novel neural-network-based image resolution enhancement
    (2003)
    Pu, HC
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    Lin, CT
    ;
    Liang, SF
    ;
    Kumar, N
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    電控工程研究所
    In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.
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    EEG-based fuzzy neural network estimator for driving performance
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Jung, TP
    Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level. of alertness of drivers. This paper proposes a brain-machine interface that combines electroencephalographic power spectrum estimation, principal. component analysis, and fuzzy neural networks to estimate/predict drivers' drowsiness level in a virtual-reality-based driving simulator. The driving performance is defined as deviation between the center of the vehicle and the center of the cruising lane. Our results demonstrated that the proposed method is feasible to accurately estimate quantitatively driving performance in a realistic driving simulator.
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    Estimating driving performance based on EEG spectrum and fuzzy neural network
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Chen, YC
    The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition! and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.