206626471
Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks
Background noise added to speech can decrease the performance of speech segmentation and enhancement. To solve this problem, new methods have been developed in this thesis. First, a new speech segmentation method (ATF-based SONFIN algorithm) is proposed in fixed noise-level environment. This method contains the multiband analysis and a neural fuzzy network, and it achieves higher recognition rate than the TF-based robust algorithm by 5%. In addition, a new speech segmentation method called RTF-based RSONFIN algorithm is proposed for variable noise-level environment. The RTF-based RSONFIN algorithm contains a recurrent neural fuzzy network. This method contains the multiband analysis and achieve higher recognition rate than the TF-based robust algorithm by 12%.
31744215
Multiband room effect simulator for 5.1-channel sound system
The paper investigates a new room effect simulator with different quality of audio sound sources for a 5.1-channel sound system. The audio effect relates its impulse response with different room dimensions. A multiband equalizer is designed to generate many kinds of music impressions. 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 44.1 kHz and 16 bits per sample. By the room effect simulator, we can get a full and a live listening experience.
15301928
New techniques on deformed image motion estimation and compensation
In this paper, new techniques for deformed image motion estimation and compensation using variable-size block-matching are proposed, which can be applied to an image sequence compression system or a moving object recognition system. The motion estimation and compensation techniques have been successfully applied in the area of image sequence coding. Many research papers on improving the performance of these techniques have been published; many directions are proposed, which can all lead to better performance than the conventional techniques. Among them, both generalized block-matching and variable-size block-matching are successfully applied in reducing the data rate of compensation error and motion information, respectively. These two algorithms have their merits, but suffer from their drawbacks. Moreover, reducing the data rate in compensation error is sometimes increasing the data rate in motion information, or vice versa. Based on these two algorithms, we propose and examine several algorithms which are effective in reducing the data rate. We then incorporate these algorithms into a system, in which they work together to overcome the disadvantages to individual and keep their merits at the same time. The proposed system can optimally balance the amount of data rate in two aspects (i.e., compensation error and motion information). Experimental results show that the proposed system outweighs the conventional techniques. Since we propose a recovery operation which tries to recover the incorrect motion vectors from the global motion, this proposed system can also be applied to the moving object recognition in image sequences.
5592923
Temperature control with a neural fuzzy inference network
Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.
44754516
Noisy Speech Segmentation/Enhancement with Multiband Analysis and Neural Fuzzy Networks
This paper addresses the problem of speech segmentation and enhancement in the presence of noise. We first propose a new word boundary detection algorithm by using a neural fuzzy network (called ATF-based SONFIN algorithm) for identifying islands of word signals in fixed noise-level environment. We further propose a new RTF-based RSONFIN algorithm where the background noise level varies during the procedure of recording. The adaptive time-frequency (ATF) and refined time-frequency (RTF) parameters extend the TF parameter from single band to multiband spectrum analysis, and help to make the distinction of speech and noise signals clear. The ATF and RTF parameters can extract useful frequency information by adaptively choosing proper bands of the mel-scale frequency bank. Due to the self-learning ability of SONFIN and RSONFIN, the proposed algorithms avoid the need of empirically determining thresholds and ambiguous rules. The RTF-based RSONFIN algorithm can also find the variation of the background noise level and detect correct word boundaries in the condition of variable background noise level by processing the temporal relations. Our experimental results show that both in the fixed and variable noise-level environment, the algorithms that we proposed achieved higher recognition rate than several commonly used word boundary detection algorithms and reduced the recognition error rate due to endpoint detection.
4468971
Tracking a maneuvering target using neural fuzzy network
A fast target maneuver detection and highly accurate tracking technique using a neural fuzzy network based on a Kalman filter is proposed in this paper. In the automatic target tracking system, there exists an important and difficult problem: how to detect the target maneuvers and fast response to avoid miss-tracking? The traditional maneuver detection algorithms, such as variable dimension filter (VDF) and input estimation (IE) etc., are computation intensive and difficult to implement in real time. To solve this problem, neural network algorithms have been issued recently. However, normal neural networks such as backpropagation networks usually produce the extra problems of low convergence speed and/or large network size. Furthermore, the way to decide the network structure is heuristic. To overcome these defects and to make use of neural learning ability, a developed standard Kalman filter with a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is presented in this paper. By generating possible target trajectories including maneuver information to train the SONFIN, the trained SONFIN can detect when the maneuver occurred, the magnitude of maneuver values and when the maneuver disappeared. Without having to change the structure of the Kalman filter or modeling the maneuvering target, this new algorithm, SONFIN, can always find an economic network size with a fast learning process. Simulation results show that KF-SONFIN is superior to traditional IE and VDF methods in estimation accuracy.
41121683
Decomposition Methods for Linear Support Vector Machines
In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.
20106221
Radius Margin Bounds for Support Vector Machines with the RBF Kernel
An important approach for efficient support vector machine (SVM) model selection is to use differentiable bounds of the leave-one-out (loo) error. Past efforts focused on finding tight bounds of loo (e.g., radius margin bounds, span bounds). However, their practical viability is still not very satisfactory. Duan, Keerthi, and Poo (2003) showed that radius margin bound gives good prediction for L2-SVM, one of the cases we look at. In this letter, through analyses about why this bound performs well for L2-SVM, we show that finding a bound whose minima are in a region with small loo values may be more important than its tightness. Based on this principle, we propose modified radius margin bounds for L1-SVM (the other case) where the original bound is applicable only to the hard-margin case. Our modification for L1-SVM achieves comparable performance to L2-SVM. To study whether L1-or L2-SVM should be used, we analyze other properties, such as their differentiability, number of support vectors, and number of free support vectors. In this aspect, L1-SVM possesses the advantage of having fewer support vectors. Their implementations are also different, so we discuss related issues in detail.
11669518
Error checking with client-driven pointer analysis
This paper presents a new client-driven pointer analysis algorithm that automatically adjusts its precision in response to the needs of client analyses. Using five significant error detection problems as clients, we evaluate our algorithm on 18 real C programs. We compare the accuracy and performance of our algorithm against several commonly used fixed-precision algorithms. We find that the client-driven approach effectively balances cost and precision, often producing results as accurate as fixed-precision algorithms that are many times more costly. Our algorithm works because many client problems only need a small amount of extra precision applied to selected portions of each input program.
7870525
Adaptive History-Based Memory Schedulers
As memory performance becomes increasingly important to overall system performance, the need to carefully schedule memory operations also increases. This paper presents a new approach to memory scheduling that considers the history of recently scheduled operations. This history-based approach provides two conceptual advantages: (1) it allows the scheduler to better reason about the delays associated with its scheduling decisions, and (2) it allows the scheduler to select operations so that they match the program's mixture of Reads and Writes, thereby avoiding certain bottlenecks within the memory controller. We evaluate our solution using a cycle-accurate simulator for the recently announced IBM Power5. When compared with an in-order scheduler, our solution achieves IPC improvements of 10.9% on the NAS benchmarks and 63% on the data-intensive Stream benchmarks. Using microbenchmarks, we illustrate the growing importance of memory scheduling in the context of CMP's, hardware controlled prefetching, and faster CPU speeds.
17509188
Client-Driven Pointer Analysis
This paper presents a new client-driven pointer analysis algorithm that automatically adjusts its precision in response to the needs of client analyses. We evaluate our algorithm on 18 real C programs, using five significant error detection problems as clients. We compare the accuracy and performance of our algorithm against several commonly-used fixed-precision algorithms. We find that the client-driven approach effectively balances cost and precision, often producing results as accurate as fixed-precision algorithms that are many times more costly. Our algorithm works because many client problems only need a small amount of extra precision applied to the right places in each input program.