Estimation of dominant sound source with three microphone array

Several real-life applications require a system that would reliably locate and track a single speaker. This can be achieved by using visual or audio data. Processing of an incoming signal to obtain the location of a source is known as Direction of Arrival (DOA) estimation. The basic setting in audio based DOA estimation is a set of microphones situated in known locations. The signal is captured by each of the microphones, and the signals are analyzed by one of the following methods: steered beamformer based method; subspace based method; or time delay estimation based method. The aim of this thesis is to review different classes of existing methods for DOA estimation and to create an application for visualizing the dominant sound source direction around a three-microphone array in real time. In practice, the objective is to enhance an algorithm for a DOA estimation proposed by Nokia Research Center. As visualization of dominant sound source creates a basis for many audio related applications, a practical example of such applications is developed. The proposed algorithm is based on time delay estimation method and utilizes cross correlation. Several enhancements are developed to the initial algorithm to improve its performance. The proposed algorithm is evaluated by comparing it with one of the most common methods, general cross correlation with phase transform (GCC PHAT). The evaluation includes testing all algorithms on three types of signals: speech signal arriving from a stationary location, speech signal arriving from a moving source, and a transient signal. Additionally, using the proposed algorithm, a computer application with a video tracker is developed. The results show that the initially proposed algorithm does not perform as well as GCC PHAT. The enhancements improve the algorithm performance notably, although they did not bring the efficiency of the algorithm to the level of GCC PHAT when processing speech signals. In case of transient signals, the enhanced algorithm was superior to GCC PHAT. The video tracker was able to successfully track the dominant sound source

Energy-Efficient Self-Organization of Wireless Acoustic Sensor Networks for Ground Target Tracking

With the developments in computing and communication technologies, wireless sensor networks have become popular in wide range of application areas such as health, military, environment and habitant monitoring. Moreover, wireless acoustic sensor networks have been widely used for target tracking applications due to their passive nature, reliability and low cost. Traditionally, acoustic sensor arrays built in linear, circular or other regular shapes are used for tracking acoustic sources. The maintaining of relative geometry of the acoustic sensors in the array is vital for accurate target tracking, which greatly reduces the flexibility of the sensor network. To overcome this limitation, we propose using only a single acoustic sensor at each sensor node. This design greatly improves the flexibility of the sensor network and makes it possible to deploy the sensor network in remote or hostile regions through air-drop or other stealth approaches. Acoustic arrays are capable of performing the target localization or generating the bearing estimations on their own. However, with only a single acoustic sensor, the sensor nodes will not be able to generate such measurements. Thus, self-organization of sensor nodes into virtual arrays to perform the target localization is essential. We developed an energy-efficient and distributed self-organization algorithm for target tracking using wireless acoustic sensor networks. The major error sources of the localization process were studied, and an energy-aware node selection criterion was developed to minimize the target localization errors. Using this node selection criterion, the self-organization algorithm selects a near-optimal localization sensor group to minimize the target tracking errors. In addition, a message passing protocol was developed to implement the self-organization algorithm in a distributed manner. In order to achieve extended sensor network lifetime, energy conservation was incorporated into the self-organization algorithm by incorporating a sleep-wakeup management mechanism with a novel cross layer adaptive wakeup probability adjustment scheme. The simulation results confirm that the developed self-organization algorithm provides satisfactory target tracking performance. Moreover, the energy saving analysis confirms the effectiveness of the cross layer power management scheme in achieving extended sensor network lifetime without degrading the target tracking performance

Handwriting Input Device Using Scratch Sound

13301甲第4244号博士（工学）金沢大学博士論文本文Full 以下に掲載：INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 7(2) pp.658-673 2014-01. 共著者：Leong Yeng Weng, Hiroaki Seki, Yoshitsugu Kamiya, Masatoshi Hikiz