Shunsuke Kidani

Behavioral tests in mice and other animals are widely used to study anxiety and to evaluate the efficacy of anxiolytic compounds. However, the validity of behavioral tests used to assess anxiety‐like behavior has been questioned because their results are often inconsistent and not readily replicated across studies. We developed a simple falling‐trap test to examine whether the number of falls could serve as an index of anxiety‐like behavior. The test is designed to produce falls from an elevated path, based on the hypothesis that less‐anxious mice would be more prone to fall. Male C57BL/6N mice were given vehicle or chlordiazepoxide (CDP), and their behavior was evaluated using both the novel test and the elevated plus maze (EPM). The outcomes were the number of falls in the falling‐trap test and open‐arm time in the EPM. In the falling‐trap test, CDP‐treated mice did not show a statistically significant increase in the number of falls compared with control mice. In contrast, CDP significantly increased open‐arm time in the EPM. Thus, under the present experimental conditions, these findings did not support the hypothesis that the number of falls in the falling‐trap test could serve as a measure of anxiety‐like behavior. Further studies are needed to determine the conditions under which this assay could be useful and to establish its reliability and validity.

Distinguishing human-imitated speech from genuine speech presents a significant challenge for listeners due to their natural resemblance. Human auditory perception (HAP) has been widely studied to understand its mechanisms, with HAP-based acoustic features and metrics applied in various applications to assess sound quality and discriminate sound events. Leveraging these insights, this study specifically aims to evaluate HAP’s effectiveness in differentiating genuine from imitated speech through a systematic subject test. To this end, the study applies HAP to the task of distinguishing genuine from imitated speech, using a specially developed dataset of human-imitated speech, due to the lack of comparable publicly available datasets. A three-phase, human-centered approach was used to evaluate HAP ability, and participants achieved an average accuracy of 70.10% in distinguishing genuine from imitated speech in the final test. Additionally, a feasibility study was conducted using two feature sets for machine classification; among the timbral features, boominess and depth performed best with accuracies of 62% and 60%, respectively, while general features like Mel-spectrograms reached 51%. These results underscore the importance of auditory-related features in effectively detecting imitated speech.

Sound-quality metrics (SQMs), such as sharpness, roughness, and fluctuation strength, are calculated using a standard method for calculating loudness (Zwicker method, ISO532B, 1975). Since ISO 532 had been revised to contain the Zwicker method (ISO 5321) and Moore-Glasberg method (ISO 532-2) in 2017, the classical computational SQM model should also be revised in accordance with these revisions. A roex auditory filterbank used with the Moore-Glasberg method is defined separately in the frequency domain not to have impulse responses. It is therefore difficult to construct a computational SQM model, e.g., the classical computational SQM model, on the basis of ISO 532-2. We propose a method for calculating loudness using the time-domain gammatone or gammachirp auditory filterbank instead of the roex auditory filterbank to solve this problem. We also propose three computational SQM models based on ISO 532-2 to use with the proposed loudness method. We evaluated the root-mean squared errors (RMSEs) of the calculated loudness with the proposed and Moore-Glasberg methods. We then evaluated the RMSEs of the calculated SQMs with the proposed method and human data of SQMs. We found that the proposed method can be considered as a time-domain method for calculating loudness on the basis of ISO 532-2 because the RMSEs are very small. We also found that the proposed computational SQM models can effectively account for the human data of SQMs compared with the classical computational SQM model in terms of RMSEs.

State-of-the-art speech watermarking techniques enable speech signals to be authenticated and protected against any malicious attack to ensure secure speech communication. In general, reliable speech watermarking methods must satisfy four requirements: inaudibility, robustness, blind-detectability, and confidentiality. We previously proposed a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction (LP) scheme to solve the first two issues (inaudibility and robustness) due to distortion by spread spectrum. This method not only effectively embeds watermarks with small distortion but also has the same robustness as the DSS method. There are, however, two remaining issues with blind-detectability and confidentiality. In this work, we attempt to resolve these issues by developing an approach called the LP-DSS scheme, which takes two forms of data embedding for blind detection and frame synchronization. We incorporate blind detection with frame synchronization into the scheme to satisfy blind-detectability and incorporate two forms of data embedding process, front-side and back-side embedding for blind detection and frame synchronization, to satisfy confidentiality. We evaluated these improved processes by carrying out four objective tests (PESQ, LSD, Bit-error-rate, and accuracy of frame synchronization) to determine whether inaudibility and blind-detectability could be satisfied. We also evaluated all combinations with the two forms of data embedding for blind detection with frame synchronization by carrying out BER tests to determine whether confidentiality could be satisfied. Finally, we comparatively evaluated the proposed method by carrying out ten robustness tests against various processing and attacks. Our findings showed that an inaudible, robust, blindly detectable, and confidential speech watermarking method based on the proposed LP-DSS scheme could be achieved.

Japan's largest platform for academic e-journals: J-STAGE is a full text database for reviewed academic papers published by Japanese societies