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.

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

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

This work proposes a novel and robust adaptive speech enhancement system, which contains both time-domain and frequency-domain beamformers using Hinfinfiltering approach in vehicle environments. A corresponding microphone array data acquisition hardware is also designed and implemented. Traditionally, mutually matched microphones are needed, but this requirement is not practical. To conquer this issue, the proposed system adapts the mismatch dynamics to allow unmatched microphones to be used in an array. Furthermore, to achieve a satisfactory 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 channel effect usually causes the modeling error in a channel recovery process because of the long channel response. The proposed system using the Hinfinfiltering approach, which makes no assumptions about noise and disturbance, is robust to the modeling error. Consequently, the proposed frequency-domain beamformer provides a satisfactory performance without the need to retrain the speech.

Abstract-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.