The proposed algorithm in this paper is capable of classifying not only unusual speech when people get anger, surprised, or excited but also unusual noise such as clashing, hitting, or clapping in real-time without depending on particular speaker voices or utterances. Also, it does not require a prior learning process to construct acoustic models. This algorithm, therefore, allows a surveillance camera system to effectively monitor quarrel or violent situations regardless of object shields and light conditions. To realize our approach, we analyze the variance and change of spectral densities and pitches when unusual speech and noise occur. We then propose new methods (SEBNI, USDF, and UNDF) to classify unusual sounds in real-time. Moreover, to improve performance, we apply a noise suppression system based on MMSE-STSA and a statistic model-based VAD to our algorithm in order to extract reliable voice features and segment only voice-related periods in noisy environments. We confirm that our proposed method achieves an 87% accuracy performance for classifying unusual speech.

The purpose of this research is to accurately classify the speech signals originating from the front even in noisy home environments. This ability can help robots to improve speech recognition and to spot keywords. We therefore developed a new voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method. It can classify the speech signals that are received at the front of two microphones by comparing the spectral energy of observed signals with that of target signals estimated by CSCC. Also, it can work in real time without training filter coefficients beforehand even in noisy environments (SNR ≫ 0 dB) and can cope with speech noises generated by audio-visual equipments such as televisions and audio devices. Since the CSCC method requires the directions of the noise signals, we also developed a sound source localization system integrated with cross-power spectrum phase (CSP) analysis and an expectation-maximization (EM) algorithm. This system was demonstrated to enable a robot to cope with multiple sound sources using two microphones.

In normal human communication, people face the speaker when listening and usually pay attention to the speaker’ face. Therefore, in robot audition, the recognition of the front talker is critical for smooth interactions. This paper presents an enhanced speech detection method for a humanoid robot that can separate and recognize speech signals originating from the front even in noisy home environments. The robot audition system consists of a new type of voice activity detection (VAD) based on the complex spectrum circle centroid (CSCC) method and a maximum signal-to-noise (Max-SNR) beamformer. This VAD based on CSCC can classify speech signals that are retrieved at the frontal region of two microphones embedded on the robot. The system works in real-time without needing training filter coefficients given in advance even in a noisy environment (SNR ≫ 0 dB). It can cope with speech noise generated from televisions and audio devices that does not originate from the center. Experiments using a humanoid robot, SIG2, with two microphones showed that our system enhanced extracted target speech signals more than 12 dB (SNR) and the success rate of automatic speech recognition for Japanese words was increased about 17 points.

We propose a way to evaluate various sound localization systems for moving sounds under the same conditions. To construct a database for moving sounds, we developed a moving sound creation tool using the API library developed by the ARINIS Company. We developed a two-channel-based sound source localization system integrated with a cross-power spectrum phase (CSP) analysis and EM algorithm. The CSP of sound signals obtained with only two microphones is used to localize the sound source without having to use prior information such as impulse response data. The EM algorithm helps the system cope with several moving sound sources and reduce localization error. We evaluated our sound localization method using artificial moving sounds and confirmed that it can well localize moving sounds slower than 1.125 rad/sec. Finally, we solve the problem of distinguishing whether sounds are coming from the front or back by rotating a robotpsilas head equipped with only two microphones. Our system was applied to a humanoid robot called SIG2, and we confirmed its ability to localize sounds over the entire azimuth range.

The purpose of this research is to develop techniques that enable robots to choose and track a desired person for interaction in daily-life environments. Therefore, localizing multiple moving sounds and human faces is necessary so that robots can locate a desired person. For sound source localization, we used a cross-power spectrum phase analysis (CSP) method and showed that CSP can localize sound sources only using two microphones and does not need impulse response data. An expectation-maximization (EM) algorithm was shown to enable a robot to cope with multiple moving sound sources. For face localization, we developed a method that can reliably detect several faces using the skin color classification obtained by using the EM algorithm. To deal with a change in color state according to illumination condition and various skin colors, the robot can obtain new skin color features of faces detected by OpenCV, an open vision library, for detecting human faces. Finally, we developed a probability based method to integrate auditory and visual information and to produce a reliable tracking path in real time. Furthermore, the developed system chose and tracked people while dealing with various background noises that are considered loud, even in the daily-life environments.

This paper presents techniques that enable talker tracking for effective human-robot interaction. To track moving people in daily-life environments, localizing multiple moving sounds is necessary so that robots can locate talkers. However, the conventional method requires an array of microphones and impulse response data. Therefore, we propose a way to integrate a cross-power spectrum phase analysis (CSP) method and an expectation-maximization (EM) algorithm. The CSP can localize sound sources using only two microphones and does not need impulse response data. Moreover, the EM algorithm increases the system's effectiveness and allows it to cope with multiple sound sources. We confirmed that the proposed method performs better than the conventional method. In addition, we added a particle filter to the tracking process to produce a reliable tracking path and the particle filter is able to integrate audio-visual information effectively. Furthermore, the applied particle filter is able to track people while dealing with various noises that are even loud sounds in the daily-life environments.

In this paper, we not only developed a reliable sound localization system including VAD (voice activity detection) component using three microphones but also a face tracking system using a vision camera. Moreover, we proposed a way to integrate these systems in the human-robot interaction to compensate the errors in the localization of a speaker and to reject unnecessary speech or noise signals entering from the undesired directions effectively. For the purpose of verifying our system's performances, we installed the proposed audition and vision system to the prototype robot, called IROBAA (Intelligent ROBot for Active Audition), and showed how to integrate an audio-visual system

In this paper, we have developed not only a probabilistic sound localization system including VAD (voice activity detection) component using three microphones but also a face tracking system using a vision camera. Moreover, we have proposed a way to integrate these systems to compensate the errors in the localization of a speaker and to reject unnecessary speech or noise signals entering from the undesired directions effectively. For the purpose of verifying our system's performances, we have installed the proposed audition and vision system to the prototype robot, called IROBAA (intelligent robot for active audition), and showed how to integrate an audio-visual system

In this paper, we propose reliable detection of sound's direction for human robot interaction. Compared with previous researches, this system comprises simpler algorithm and a proposed nonlinear amplifier which has advantages to increase a detectible distance of sound signal in spite of simple circuits. Moreover, we propose the new performance index using cross-correlation to make reliable detection of sound's direction. Also, since this system include a function to detect a period of voice signals, the robot can know when to start finding sound's direction and performing speech recognition automatically. In order to verify our system's performances, we install the proposed audition system to the prototype robot, called IROBAA (intelligent robot for active audition), and describe the experimental results.

Interconnect test for highly integrated environments becomes more important in terms of its test time and a complete diagnosis, as the complexity of the circuit increases. Since the board-level interconnect test is based on boundary scan technology, it takes a long test time to apply test vectors serially through a long scan chain. Complete diagnosis is another important issue. Since the board-level test is performed for repair, noticing the faulty position is an essential element of any interconnect test. Generally, the interconnect test algorithms that need a short test time cannot perform the complete diagnosis and the algorithms that perform the complete diagnosis need a lengthy test time. To overcome this problem, a new interconnect test algorithm is developed. The new algorithm can provide the complete diagnosis of all faults with a shorter test time compared to the previous algorithms.