A robust watermarking scheme based on the time-spread echo method is proposed in this letter. The embedding process is achieved by subband decomposition of a host signal and by controlling the amount of distortion, i.e., power of watermark, of each subband according to the Signal to Mask Ratio (SMR) calculated from MPEG psychoacoustic model. The decoding performance and robustness of the proposed method were evaluated.

A new method of designing a microphone array with two outputs preserving binaural information is proposed in this paper. This system employs adaptive beamforming using multiple constraints. The binaural cues may be preserved in the two outputs by use of these multiple constraints with simultaneous beamforming to enhance target signals is also available. A computer simulation was conducted to examine the performance of the beamforming. The results showed that the proposed array can perform both the generation of the binaural cues and the beamforming as intended. In particular, beamforming with double-constraints exhibits the best performance; DI is around 7 dB and good interchannel (interaural) time/phase and level differences are generated within a target region in front. With triple-constraints, however, the performance of the beamforming becomes poorer while the binaural information is better realized. Setting of the desired responses to give proper binaural information seems to become critical as the number of the constraints increases.

A new speech enhancement technique is proposed assuming that a speech signal is represented in terms of a linear probabilistic process and that a noise signal is represented in terms of a stationary random process. Since the target signal, i.e., speech, cannot be represented by a stationary random process, a Wiener filter does not yield an optimum solution to this problem regarding the minimum mean variance. Instead, a Kalman filter may provide a suitable solution in this case. In the Kalman filter, a signal is represented as a sequence of varying state vectors, and the transition is dominated by transition matrices. Our proposal is to construct the state vectors as well as the transition matrices based on time-frequency pattern of signals calculated by a wavelet transformation (WT). Computer simulations verify that the proposed technique has a high potential to suppress noise signals.

Surveillance with multiple cameras and microphones is promising to trace activities of suspicious persons for security purposes. When these sensors are connected to the Internet, they might also jeopardize innocent people's privacy because, as a result of human error, signals from sensors might allow eavesdropping by malicious persons. This paper presents a proposal for exploiting super-resolution to address this problem. Super-resolution is a signal processing technique by which a high-resolution version of a signal can be reproduced from a low-resolution version of the same signal source. Because of this property, an intelligible speech signal is reconstructed from multiple sensor signals, each of which is completely unintelligible because of its sufficiently low sampling rate. A method based on Bayesian linear regression is proposed in comparison with one based on maximum likelihood. Computer simulations using a simple sinusoidal input demonstrate that the methods restore the original signal from those which are actually measured. Moreover, results show that the method based on Bayesian linear regression is more robust than maximum likelihood under various microphone configurations in noisy environments and that this advantage is remarkable when the number of microphones enrolled in the process is as small as the minimum required. Finally, listening tests using speech signals confirmed that mean opinion score (MOS) of the reconstructed signal reach 3, while those of the original signal captured at each single microphone are almost 1.

The degree of reception of BS signals is affected by various factors. After routinely recording it at two observation points at two locations, we found that momentary upward and downward level shifts occurred multiple times, mainly during daytime. These level shifts were observed at one location. No such signal was sensed at the other location. After producing an algorithm to extract such momemtary level shifts, their statistical properties were investigated. Careful analyses, including assessment of the signal polarity, amplitude, duration, hours, and comparison with actual flight schedules and route information implied that these level shifts are attributable to the interference of direct and reflected waves from aircraft flying at approximately tropopause altitude. This assumption is further validated through computer simulations of BS signal interference.