We propose a novel method for estimating acoustic scenes such as user activities, e.g., “cooking,” “vacuuming,” “watching TV,” or situations, e.g., “being on the bus,” “being in a park,” “meeting,” utilizing the information of acoustic events. There are some methods for estimating acoustic scenes that associate a combination of acoustic events with an acoustic scene. However, the existing methods cannot adequately express acoustic scenes, e.g., “cooking,” that have more than one subordinate category, e.g., “frying ingredients” or “plating food,” because they directly associate acoustic events with acoustic scenes. In this paper, we propose an acoustic scene estimation method based on a hierarchical probabilistic generative model of an acoustic event sequence taking into account the relation among acoustic scenes, their subordinate categories, and acoustic event sequences. In the proposed model, each acoustic scene is represented as a probability distribution over their unsupervised subordinate categories, called “acoustic sub-topics,” and each acoustic sub-topic is represented as a probability distribution over acoustic events. Acoustic scene estimation experiments with real-life sounds showed that the proposed method could correctly extract subordinate categories of acoustic scenes.

In order that speech recognition system may have a high recognition rate in a noisy environment, a wide-band sharp directional microphone system is required at the input for securing a high S/N ratio. The authors have already reported the realization of a wide-band uni-directional microphone system by three-microphone integration method. In this paper, we intend to describe the derivation of a sharp directivity function and the realization of its microphone system. First, setting the shape of the characteristic function to bring a sharp directional pattern and then expanding it into the Fourier series, we derive a new directivity function. Next, on the basis of this directivity function, we will present a sharp directional microphone system with only three non-directional microphones and the subsequent analog signal processing. And also, the directional pattern acquired by the proposed method and the effect of the dispersion in the sensitivity of the constituent microphones on the directivity are discussed in detail.

An Acoustic echo canceller has problems adaptating under noisy or double-talk conditions. The adaptation process requires a precise identification of the temporarily changed room impulse response. To do this, both minimizing the step size parameter of the Least Mean Square (LMS) method to be as small as possible and giving up on updating the adaptive filter coefficients have been considered. This paper describes an adaptive cross-spectral technique that is robust to adaptive filtering under noisy or double-talk conditions and for colored signals such a speech signal. The cross-spectral technique was originally developed to measure the impulse response in a linear system. Here we apply in the adaptive cross-spectral technique to solve the acoustic echo cancelling problem. This cross-spectral technique takes the ensemble average of the cross spectrum between input and error signals and the averaged cross spectrum is divided by the averaged power spectrum of the input signal to update the filter coefficients. We have confirmed that the echo signal is suppressed by about 15 dB even under double-talk conditions. We also explain that this method has a systematic error due to using a short time block for estimating the room impulse response. Then we investigate overlapping every last half block by the following first half block in order to reduce the effect of the systematic error. Finally, we compare our method with the Frequency-domain Block LMS (FBLMS) method because both methods are implemented in the frequency domain using a short time block.

In order to ensure the reliability and safety of equipment installed in process lines, it is important that maintenance and management should make efficient use of machine diagnosis techniques. Machine diagnosis by means of acoustic signals has hitherto been beset with difficulty, but there is now a strong demand that new acoustic type diagnosis equipment (utilizing acoustic signals) be developed. In response to this demand, the authors recently conducted research on diagnosis of machine faults by means of the processing of acoustic signals. In this research they were able to develop new acoustic type machine diagnosis techniques, and, using these techniques, to develop acoustic diagnosis equipment for practical use.