This study investigates changes in the rhythmic characteristics of Japanese speech induced by delayed auditory feedback (DAF). Ten native speakers read sentences under four DAF conditions (0, 100, 200, and 300 ms), and interval durations (consonant, vowel, and mora) were measured. Speech rhythm indices were evaluated using Mean (average duration), Delta (absolute variability), and Varco (relative variability). DAF increased Mean and Delta in consonant and vowel durations, indicating greater temporal variability at the phoneme level. However, the relative isochrony of morae, measured by VarcoM, remained stable, suggesting mora timing tends to be preserved under feedback perturbations.

Both amplitude modulation and frequency modulation occur in a sound field radiated from moving sound sources. However, the amplitude modulation for different source types has not been clarified experimentally. In this study, the directivities of sound radiated from an ultrasonic transducer attached to a moving scaled train model and used as a simplified vibrational sound source were measured. The surface of the vibrational source was parallel to the movement direction of the source. The directivities of an aeolian tone sound generated using a cylinder attached to the train model were also measured; the cylinder was used as an aerodynamic sound source for a simplified dipole component of aerodynamic sound, which is dominant at low Mach numbers, generated from pressure fluctuation. The experimental results show that the acoustic intensity of sound radiated from a vibrational source increases roughly in proportion to the fourth power of the Doppler factor, whereas that of the dipole component of aerodynamic sound increases roughly in proportion to the eighth power of the Doppler factor. Sound radiated from an aerodynamic source moving at low Mach numbers leads to a greater amplitude modulation due to source movement compared with sound radiated from a vibrational source.

Road traffic noise is one of the most widespread environmental noise sources, significantly impacting public health. To control traffic noise pollution, the European Union requires countries to prepare strategic road traffic noise maps using prediction models every five years since 2007. Similarly, Japan has developed its own road traffic noise maps based on regular on-site observations. However, traditional methods of collecting traffic data through on-site measurements are labor-intensive and costly. Therefore, in this work, we present a method for creating road traffic noise maps using an object detection deep learning algorithm to extract road traffic conditions, such as vehicle types and speeds, from aerial photographs. On the basis of ASJ RTN-Model 2018, the traffic condition data serves as the foundation for calculating the sound power levels of road traffic noise for different roads. The road traffic noise map is then created directly from the sound pressure level distribution within the considered regions. We validated the accuracy of the detection model and the calculated sound pressure level along the road from the aerial photographs against the published measurements. Using the proposed method, we created the road traffic noise map for Meguro City, Tokyo.

Sound fields around a barrier edge with an open-tube array are numerically calculated to investigate how the tube array affects barrier performance. Results show that the open tubes arrayed on the source side of the barrier edge are effective in some cases. On basis of the results, a hypothesis regarding the effects of the open tubes at the barrier edge is considered and tested. It is revealed that sound pressure levels behind the barrier are significantly suppressed when the phase difference between incident waves from the source to the upper and lower ends of a tube is small.

We surveyed to explore how Japanese cochlear implant users perceive and enjoy music, as well as factors related to their music enjoyment. The survey gathered responses from 102 participants, showing that many continue to enjoy music after getting the implant and are keen on enjoying it even more. A time series analysis revealed that enjoyment of music decreased with hearing level, but after implantation, it improved to the same level as before the hearing loss. Additionally, there was a tendency for lower ratings of sound quality and music listening with increasing age, but participants with musical experience perceived music better than those without it. The study also suggested that practicing listening to music with cochlear implants may improve music perception. However, there were significant individual differences in the results. Especially after implantation, some people enjoyed music, and others did not. The findings indicate that future research should focus on enhancing subjective enjoyment of music.

This paper proposes an unsupervised DNN-based speech enhancement approach founded on deep priors (DPs). Here, DP signifies that DNNs are more inclined to produce clean speech signals than noises. Conventional methods based on DP typically involve training on a noisy speech signal using a random noise feature as input, stopping training only a clean speech signal is generated. However, such conventional approaches encounter challenges in determining the optimal stop timing, experience performance degradation due to environmental background noise, and suffer a trade-off between distortion of the clean speech signal and noise reduction performance. To address these challenges, we utilize two DNNs: one to generate a clean speech signal and the other to generate noise. The combined output of these networks closely approximates the noisy speech signal, with a loss term based on spectral kurtosis utilized to separate the noisy speech signal into a clean speech signal and noise. The key advantage of this method lies in its ability to circumvent trade-offs and early stopping problems, as the signal is decomposed by enough steps. Through evaluation experiments, we demonstrate that the proposed method outperforms conventional methods in the case of white Gaussian and environmental noise while effectively mitigating early stopping problems.

This study introduces bidirectional stepwise-based algorithms designed to optimize loudspeaker array configurations for Multizone Sound Field Reproduction systems. An initial arrangement selection method based on loudspeaker magnitude enhances the optimization process. These algorithms were validated using the Acoustic Contrast Control and Pressure Matching methods across free-field conditions and a comprehensive Room Impulse Response database including various room conditions. Comparative experiments against traditional unidirectional iterative strategies demonstrate that the proposed algorithms significantly outperform existing methods in terms of efficiency and effectiveness, especially in configurations with fewer loudspeakers. For example, in a small meeting room with 16 loudspeakers, the stepwise-based approaches achieved higher acoustic contrast and required substantially fewer iterations than conventional methods. Specifically, optimization efficiency improvements were about 55.2% and 77.8% in Acoustic Contrast Control and 36.7% and 68.6% in Pressure Matching, compared to conventional iteratively adding or removing approaches.

The finite difference time domain (FDTD) method has been proposed and used for sound field simulation. To reproduce actual sound wave propagation in sound field simulations, it is necessary to apply the radiation characteristics. With the FDTD method, radiation characteristics can be applied by setting sound pressure in a dense grid arrangement. However, conventional techniques for capturing radiation characteristics use a sparse array of microphones and are considered insufficient for the FDTD simulation. Furthermore, the technique required to apply captured acoustic signals in a dense grid arrangement with the FDTD method has not been considered. In this paper, we propose a novel hardware and software system that captures the radiation characteristics for a dense grid arrangement and applies them to the FDTD method, while controlling the sound wave propagation with the non-propagation region. The proposed system produces the average differences from measured values of sound pressure, propagation time, center frequency, and log-spectral distortion of 1.8 dB, 0.04 ms, 700 Hz, and 3.5 dB, respectively, which is more accurate than the conventional techniques. The result shows that this system is useful for improving the accuracy of sound wave propagation reproduction with the sound field simulation.

The harmonic structure of the speech spectrum is crucial for accurate pitch detection. This study presents a method for enhancing the harmonic structure, leading to robust pitch estimation. After analyzing the speech of four males and four females, the results clearly show that the improved harmonic structure ensures pitch estimation independent of traditional issues.