Browsing by Author "Liu, Wei-Han"

Now showing 1 - 11 of 11

A robust adaptive speech enhancement system for vehicular applications
(2006-08-01)
Hu, Jwu-Sheng
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Cheng, Chieh-Cheng
;
Liu, Wei-Han
;
Yang, Chia-Hsing
;
電控工程研究所
This work proposes and implements a novel and robust adaptive speech enhancement system, which contains both time domain and frequency domain beamformers using H-infinity filtering approach to provide a clean and undisturbed speech waveform and improve the speech recognition rate in vehicle environments. A microphone array data acquisition hardware is also designed and implemented for the proposed speech enhancement system. Mutually matched microphones are needed for traditional multidimensional noise reduction methods, but this requirement is not practical for consumer applications from the cost standpoint. To overcome this issue, the proposed system adapts the mismatch dynamics to maintain the theoretical performance allowing unmatched microphones to be used in an array. Furthermore, to achieve a high speech recognition performance, the speech recognizer is usually required to be retrained for different vehicle environments due to different noise characteristics and channel effects. The proposed system using the H-infinity filtering approach, which makes no assumptions about noise and disturbance, is robust to the modeling error in a channel recovery process. Consequently, the real vehicular experimental results show that the proposed frequency domain beamformer provides a satisfactory speech recognition performance without the need to retrain the speech recognizer.(1)
A robust statistical-based speaker's location detection algorithm in a vehicular environment
(2007)
Hu, Jwu-Sheng
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Cheng, Chieh-Cheng
;
Liu, Wei-Han
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This work presents a robust speaker's location detection algorithm using a single linear microphone array that is capable of detecting multiple speech sources under the assumption that there exist nonoverlapped speech segments among sources. Namely, the overlapped speech segments are treated as uncertainty and are not used for detection. The location detection algorithm is derived from a previous work ( 2006), where Gaussian mixture models (GMMs) are used to model location-dependent and content and speaker-independent phase difference distributions. The proposed algorithm is proven to be robust against the complex vehicular acoustics including noise, reverberation, near-filed, far-field, line-of-sight, and non-line-of-sight conditions, and microphones' mismatch. An adaptive system architecture is developed to adjust the Gaussian mixture ( GM) location model to environmental noises. To deal with unmodeled speech sources as well as overlapped speech signals, a threshold adaptation scheme is proposed in this work. Experimental results demonstrate high detection accuracy in a noisy vehicular environment.
An investigation of time delay estimation in room acoustic environment using magnitude ratio
(2010-01-01)
Hu, Jwu-Sheng
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Yang, Chia-Hsin
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Liu, Wei-Han
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper investigates the relation between the nonstationary sound source and the frequency domain magnitude ratio of two microphones based on short-term frequency analysis. The fluctuation level of nonstationary sound sources is modeled by the exponent of polynomials from the concept of moving pole model. According to this model, the sufficient condition for utilizing the fluctuation level and magnitude ratio to estimate the time delay between two microphones is suggested. Simulation results are presented to show the performance of the suggested method. (C) 2009 Elsevier B.V. All rights reserved.
Gaussian mixture-sound field landmark model for robot localization
(2005)
Wu, Li-Wei
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Cheng, Chieh- Cheng
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Liu, Wei-Han
;
Hu, Jwu-Sheng
;
電控工程研究所
This investigation proposes a robust robot localization system. The system contains a novel Gaussian Mixture-Sound Field Landmark Model (GM-SFLM) and can localize the robot accurately in noisy environments. Moreover, the proposed method depends nothing on the geometry relation between source locations and two microphones; it is able to cover both near-field and far-field problems. With this proposed GM-SFLM, we can localize robot in 2-dimentional indoor environments. Furthermore, we realize the GM-SFLM into a quadruped robot system composed of an eRobot and a robot agent by using embedded Ethernet technology. The experiment demonstrates that when the robot is completely non-line-of-sight, this system still provides high detection accuracy. Additionally, the proposed method has advantages of high accuracy, low-cost, easy to implement and environmental adaptation.
Gaussian mixture-sound field landmark model for robot localization applications
(2007)
Wu, Li-Wei
;
Liu, Wei-Han
;
Cheng, Chieh-Cheng
;
Hu, Jwu-Sheng
;
電控工程研究所
This work proposes a novel Gaussian mixture-sound field landmark model for localization applications, based on the principle that sound fields produced by sources at different locations can be distinguished in terms of their statistical patterns. The experimental results indicate that two microphones are sufficient to differentiate among the patterns. The proposed method is robust against environmental noise and performs accurately in a complex environment. Moreover, it cannot only detect the non-line-of-sight locations when the direct path between the microphones and the location is blocked, but also can distinguish the locations aligned with respect to the line connecting the microphones. However, using only two microphones, these scenarios are difficult to handle by traditional direction-of-arrival or beamforming methods in microphone array research. The experiments were conducted on a quadruped robot platform with an eRobot agent using embedded Ethernet technology. Because of its high accuracy and low-cost, this method is suitable for robot localization in real environments. The experimental results also show that the proposed method with only two microphones outperforms the conventional multiple signal classification method (MUSIC) technique with six microphones at various signal-to-noise ratios.
Indoor sound field feature matching for robot's location and orientation detection
(2008-01-15)
Hu, Jwu-Sheng
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Liu, Wei-Han
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Cheng, Chieh-Cheng
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電控工程研究所
;
Institute of Electrical and Control Engineering
In this work, an indoor sound field feature matching method is proposed and is applied to detect a mobile robot's location and orientation. The sound field feature, captured from a sound source to a pair of microphones, contains the dynamic of the propagation path. Because of the complexity of indoor environment, the features from different path can be distinguished using appropriate models. Gaussian mixture models are utilized in this paper to characterize the phase difference and magnitude ratio distributions between the microphone pair in consecutive data frames. The application provides an alternative thinking compared with traditional methods such as direction of arrival (DOA) using propagation delay. They usually suffer from reverberation, non-line-of-sight and microphone mismatch problems. The experimental results show the method not only has a high recognition rate for robot's location and orientation, but also is robust against environmental noise. (c) 2007 Elsevier B.V. All rights reserved.
Location and orientation detection of mobile robots using sound field features under complex environments
(2006)
Wu, Jwu-Sheng
;
Liu, Wei-Han
;
Cheng, Chieh-Cheng
;
Yang, Chia-Hsing
;
電控工程研究所
In this paper, the feasibility of utilizing sound for robot's pose detection is investigated, and a novel and robust robot location and orientation detection method based on sound field features for noisy environment is proposed. Unlike traditional methods, the proposed method does not explicitly consider the characteristic of direct path from sound source to microphones, nor attempt to suppress the effect of reverberations and noise signals. Instead, it utilizes the sound field features of a robot at different location and orientation in a normal environment. The sound field feature is captured by using a probability distribution estimation method called Gaussian Mixture Model (GMM). The experimental results show that this method can detect robot's location and orientation under both line-of-sight and non-line-of-sight conditions using only two microphones and is robust to environmental noise. Moreover, it can also solve the microphones' mismatch problem and can be applied to both near-field and far-field conditions. Since this method can provide global location and orientation detection, it is suitable to fuse with other localization methods to provide initial conditions for reduction of the search effort, or provide the compensation for localizing certain locations that cannot be detected using other localization methods.
Location Classification of Nonstationary Sound Sources Using Binaural Room Distribution Patterns
(2009-05-01)
Hu, Jwu-Sheng
;
Liu, Wei-Han
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper discusses the relationships between the nonstationarity of sound sources and the distribution patterns of interaural phase differences (IPDs) and interaural level differences (ILDs) based on short-term frequency analysis. The amplitude variation of nonstationary sound sources is modeled by the exponent of polynomials from the concept of moving pole model. According to the model, the sufficient condition for utilizing the distribution patterns of IPDs and ILDs to localize a nonstationary sound source is suggested and the phenomena of multiple peaks in the distribution pattern can be explained. Simulation is performed to interpret the relation between the distribution patterns of IPD and ILD and the nonstationary sound source. Furthermore, a Gaussian-mixture binaural room distribution model (GMBRDM) is proposed to model distribution patterns of IPDs and ILDs for nonstationary sound source location classification. The effectiveness and performance of the proposed GMBRDM are demonstrated by experimental results.
Robust speaker's location detection in a vehicle environment using GMM models
(2006-04-01)
Hu, Jwu-Sheng
;
Cheng, Chieh-Cheng
;
Liu, Wei-Han
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Human-computer interaction (HCI) using speech communication is becoming increasingly important, especially in driving where safety is the primary concern. Knowing the speaker's location (i.e., speaker localization) not only improves the enhancement results of a corrupted signal, but also provides assistance to speaker identification. Since conventional speech localization algorithms suffer from the uncertainties of environmental complexity and noise, as well as from the microphone mismatch problem, they are frequently not robust in practice. Without a high reliability, the acceptance of speech-based HCI would never be realized. This work presents a novel speaker's location detection method and demonstrates high accuracy within a vehicle cabinet using a single linear microphone array. The proposed approach utilize Gaussian mixture models (GMM) to model the distributions of the phase differences among the microphones caused by the complex characteristic of room acoustic and microphone mismatch. The model can be applied both in near-field and far-field situations in a noisy environment. The individual Gaussian component of a GMM represents some general location-dependent but content and speaker-independent phase difference distributions. Moreover, the scheme performs well not only in nonline-of-sight cases, but also when the speakers are aligned toward the microphone array but at difference distances from it. This strong performance can be achieved by exploiting the fact that the phase difference distributions at different locations are distinguishable in the environment of a. car. The experimental results also show that the proposed method outperforms the conventional multiple signal classification method (MUSIC) technique at various SNRs.
Robust statistical-based speaker's location detection algorithm in a vehicular environment
(2006)
Hu, Jwu-Sheng
;
Cheng, Chieh-Cheng
;
Liu, Wei-Han
;
Yang, Chia-Hsing
;
電控工程研究所
This work proposes a threshold adaptation method to detect speaker's location in a vehicular environment. The method is robust to unmodeled sound source locations in a noisy environment and uses a single linear microphone array. The proposed approach is an improvement over previous work [12] which adopts Gaussian mixture models (GMMs) to model location-dependent but content and speaker independent acoustic characteristics of sound sources. Experimental results show that this scheme can overcome the far-filed and near-riled problem with the same architecture and perform well in both line-of-sight and non-line-of-sight cases.
Speaker attention system for mobile robots using microphone array and face tracking
(2006)
Song, Kai-Tai
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Hu, Jwu-Sheng
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Tsai, Chi-Yi
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Chou, Chung-Min
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Cheng, Chieh- Cheng
This paper presents a real-time human-robot interface system (HRIS), which processes both speech and vision information to improve the quality of communication between human and an autonomous mobile robot. The HRIS contains a real-time speech attention system and a real-time face tracking system. In the speech attention system, a microphone-array voice acquisition system has been developed to estimate the direction of speaker and purify the speaker's speech signal in a noisy environment. The developed face tracking system aims to track the speaker's face under illumination variation and react to the face motion. The proposed HRIS can provide a robot with the abilities of finding a speaker's direction, tracking the speaker's face, moving its body to the speaker, focusing its attention to the speaker who is talking to it, and purifying the speaker's speech. The experimental results show that the HRIS not only purifies speech signal with a significant performance, but also tracks a face under illumination variation in real-time.