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Monitoring domestic activities helps us to understand user behaviors in indoor environments, which has garnered interest as it aids in understanding human activities in context-aware computing. In the field of acoustics, this goal has been achieved through studies employing machine learning techniques, which are widely used for classification tasks involving sound recognition and other objectives. Machine learning typically achieves better performance with large amounts of high-quality training data. Given the high cost of data collection, development datasets often suffer from imbalanced data or lack high-quality samples, leading to performance degradations in machine learning models. The present study aims to address this data issue through data augmentation techniques. Specifically, since the proposed method targets indoor activities in domestic activity detection, room transfer functions were used for data augmentation. The results show that the proposed method achieves a 0.59% improvement in the F1-Score (micro) from that of the baseline system for the development dataset. Additionally, test data including microphones that were not used during training achieved an F1-Score improvement of 0.78% over that of the baseline system. This demonstrates the enhanced model generalization performance of the proposed method on samples having different room transfer functions to those of the trained dataset.

Abstract Layered‐type transition metal (TM) oxides are considered as one of the most promising cathodes for K‐ion batteries because of the large theoretical gravimetric capacity by low molar mass. However, they suffer from severe structural change by de/intercalation and diffusion of K+ ions with large ionic size, which results in not only much lower reversible capacity than the theoretical capacity but also poor power capability. Thus, it is important to enhance the structural stability of the layered‐type TM oxides for outstanding electrochemical behaviors under the K‐ion battery system. Herein, it is investigated that the substitution of the appropriate Ti4+ contents enables a highly enlarged reversible capacity of P3‐type KxCrO2 using combined studies of first‐principles calculation and various experiments. Whereas the pristine P3‐type KxCrO2 just exhibits the reversible capacity of ∼120 mAh g−1 in the voltage range of 1.5–4.0 V (vs. K+/K), the ∼0.61 mol K+ corresponding to ∼150 mAh g−1 can be reversible de/intercalated at the structure of P3‐type K0.71[Cr0.75Ti0.25]O2 under the same conditions. Furthermore, even at the high current density of 788 mA g−1, the specific capacity of P3‐type K0.71[Cr0.75Ti0.25]O2 is ∼120 mAh g−1, which is ∼81 times larger than that of the pristine P3‐type KxCrO2. It is believed that this research can provide an effective strategy to improve the electrochemical performances of the cathode materials suffered by severe structural change that occurred during charge/discharge under not only K‐ion battery system but also other rechargeable battery systems.

High duty cycle sonar (HDCS) systems have a high potential for improving tracking performance compared to conventional pulsed active sonar systems, but their implementation has been challenging to achieve. This is because conventional waveform design studies for HDCS systems had focused primarily on solving direct blast interference problems caused by continuous transmission and reception of pulse train waveforms. So far, there are no studies for waveform design to improve HDCS tracking performance. In this paper, we proposed a generalized sinusoidal frequency modulated (GSFM) pulse train waveform design scheme to improve HDCS tracking performance. The proposed design scheme utilizes the trade-off relationship between detection performance in a reverberation environment and measurement uncertainty according to the parameter $\rho $ of the GSFM waveform. To accomplish the goal, we developed a framework for pulse train waveform design considering HDCS tracking performance. In the framework, the detection probability and measurement noise covariance matrix of the Kalman filter are calculated based on the designed GSFM pulse train waveform. Therefore, the pulse train waveform design and HDCS tracking performance can be associated. The simulation using the HDCS tracking framework demonstrated that optimal tracking performance was obtained when the parameter $\rho $ was 1.07.

Active sonar systems are used in the detection and localization of underwater targets. While traditional approaches use pulsed active sonar (PAS) to transmit short bursts, high duty cycle (HDC) sonar systems have been the focus of recent research and can overcome the shortcomings of PAS. Since HDC sonar systems transmit a long pulse train waveform, we must address sub-pulse interference issues, which requires multiple orthogonal sub-pulses and generalized sinusoidal frequency modulated (GSFM) pulses are suitable for this purpose. Unfortunately, conventional GSFM pulse train design methods do not generate an adequate number of orthogonal sub-pulses. Therefore, we propose an improved GSFM pulse train waveform design approach for overcoming the limitations of these conventional methods. The proposed method is organized into two parts, the first focused on the assessment of the auto-correlation, and the second focused on the optimizing the orthogonality between sub-pulses. Cost function is also carefully designed for this purpose. From simulation experiments, we found that the proposed method was able to produce 30 orthogonal sub-pulses and an optimized waveform that demonstrated better detection performance than conventional waveforms under most conditions. We also verified the performance of the proposed method via actual sea experiments, with analysis showing superior performance matching that of simulations. An analysis on the bandwidth parameter $K$ is also conducted and it is found $K = 1.25$ to be the best option.

In active sonar systems, detection always suffers from reverberation interference from multiple scatterers in oceanic environments; therefore, numerous studies have been conducted on reverberation suppression. Recently, a non-negative matrix factorization (NMF)-based method was proposed and successfully applied to reverberation suppression. However, the conventional NMF-based method makes convergence challenging because the frequency basis matrix is initialized without considering reverberation characteristic information from oceanic environments. To solve these problems, We propose an improved NMF-based reverberation suppression method adopting a pre-trained reverberation basis matrix and modified sparse update rule. The proposed method is evaluated by analyzing simulation and sea experiment data and the study confirmed that the detection performance was improved compared to the conventional method under various signal-to-reverberation ratio conditions. Several topics are also discussed to analyze the proposed method in detail.

In this study, we propose a reverberation suppression algorithm for linear frequency-modulated (LFM) pulse sonar systems using a non-negative matrix factorization (NMF) method. Because conventional NMF-based reverberation suppression algorithms are only applicable to continuous wave reverberation, we propose two pre-processing methods, namely dechirping transformation and modulo operation, to facilitate application of the NMF method to LFM reverberation. Moreover, we impose additional sparse constraints on the NMF method to improve its performance. To evaluate the proposed algorithm, an experiment involving simulated LFM reverberation is performed. The results thereof show improved detection performance at several signal-to-reverberation ratios and false alarm conditions. Moreover, the proposed algorithm is also applied to sea experiment data. According to the sea experiment analysis, the algorithm is able to suppress the LFM reverberation effectively and improve detection performance in practical LFM pulse sonar systems.

In this paper, a feature extraction method is proposed based on the non-negative matrix factorization (NMF) for classifiers for monitoring domestic activities with acoustic signals. Most of the classifiers of the acoustic signals use data-independent spectral features (e.g., log-Mel spectrum and Mel-frequency cepstral coefficients). Recently, some novel feature extraction methods have been researched, including convolution-NMF-based features combined with K-means clustering. This study proposes an enhanced NMF-based feature extraction method that is inspired by the NMF-based noise reduction algorithm. The proposed method independently estimates the frequency basis matrix for each class, and then cascades the basis matrices to form the entire frequency bases, where the acoustic signal is transformed to the proposed feature by estimating the temporal basis matrix with the trained frequency bases. In addition, this study proposes a data augmentation method for the proposed feature that is inspired by the “mix and shuffle” method for audio waveforms. In order to evaluate the proposed system, which consists of the proposed NMF-based feature and the convolutional-neural-network-based classifier, some evaluations were performed using the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Task 5 - Monitoring of Domestic Activities Based on Multi-channel Acoustics - Database. The results showed that the proposed system has comparable performance to that of state-of-the-art algorithms and that it has enhanced the F1-score performance of 6%-12% in comparison with the conventional NMF-based feature extraction method that is based on convolutional NMF and K-means clustering.

Abstract In active sonar systems, the detection of echo from targets can deteriorate due to reverberation. Detection becomes more difficult if targets have low-Doppler frequency and are located near the reverberation band, especially in an environment with low signal-to-reverberation ratio. In this paper, we propose an algorithm for the reverberation suppression of continuous wave signals using non-negative matrix factorization. To extract the target echo signal mixed with reverberations, the bases for the target echo and the reverberation are independently defined, and different constraints are applied for their corresponding estimation. We also derive constraints on temporal continuity and temporal length to estimate bases for the target echo. Experiments using simulated reverberations are performed to evaluate the proposed algorithm, and the results show an enhancement in the signal-to-noise ratio by 6–15 dB, as well as in the detection probability at several signal-to-reverberation ratios. Moreover, an experiment is conducted using reverberation measured from an ocean, and the results show that the proposed algorithm can effectively suppress reverberation and enhance detection performance in practical settings.

Salt stress constitutes a major form of abiotic stress in plants. Histone modification plays an important role in stress tolerance, with particular reference to salt stress resistance. In the current study, we found that HDA15 overexpression confers salt stress resistance to young seedling stages of transgenic plants. Furthermore, salt stress induces HDA15 overexpression. Transcription levels of stress-responsive genes were increased in transgenic plants overexpressing HDA15 (HDA15 OE). NCED3, an abscisic acid (ABA) biosynthetic gene, which is highly upregulated in HDA15 transgenic plants, enhanced the accumulation of ABA, which promotes adaptation to salt stress. ABA homeostasis in HDA15 OE plants is maintained by the induction of CYP707As, which optimize endogenous ABA levels. Lastly, we found that the double-mutant HDA15 OE/hy5 ko plants are sensitive to salt stress, indicating that interaction between HDA15 and ELONGATED HYPOCOTYL 5 (HY5) is crucial to salt stress tolerance shown by HDA15 OE plants. Thus, our findings indicate that HDA15 is crucial to salt stress tolerance in Arabidopsis.

In recent acoustic scene classification (ASC) models, various auxiliary methods to enhance performance have been applied, e.g., subsystem ensembles and data augmentations. Particularly, the ensembles of several submodels may be effective in the ASC models, but there is a problem with increasing the size of the model because it contains several submodels. Therefore, it is hard to be used in model-complexity-limited ASC tasks. In this paper, we would like to find the performance enhancement method while taking advantage of the model ensemble technique without increasing the model size. Our method is proposed based on a mean-teacher model, which is developed for consistency learning in semi-supervised learning. Because our problem is supervised learning, which is different from the purpose of the conventional mean-teacher model, we modify detailed strategies to maximize the consistency learning performance. To evaluate the effectiveness of our method, experiments were performed with an ASC database from the Detection and Classification of Acoustic Scenes and Events 2021 Task 1A. The small-sized ASC model with our proposed method improved the log loss performance up to 1.009 and the F1-score performance by 67.12%, whereas the vanilla ASC model showed a log loss of 1.052 and an F1-score of 65.79%.

In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l1-norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attempted to tackle the problems of acoustic scene classification and sound event detection. However, most of these systems use data-independent spectral features, e.g., Mel-spectrogram, log-Mel-spectrum, and gammatone filterbank. Some data-dependent feature extraction methods, including the NMF-based methods, recently demonstrated the potential to tackle the problems mentioned above for long-term acoustic signals. In this paper, we further develop the recently proposed NMF-based feature extraction method to enable its application in weakly supervised sound event detection. To achieve this goal, we develop a strategy for training the frequency basis matrix using a heterogeneous database consisting of strongly- and weakly-labeled data. Moreover, we develop a non-iterative version of the NMF-based feature extraction method so that the proposed feature extraction method can be applied as a part of the model structure similar to the modern “on-the-fly” transform method for the Mel-spectrogram. To detect the sound events, the temporal basis is calculated using the NMF method and then used as a feature for the mean-teacher-model-based classifier. The results are improved for the event-wise post-processing method. To evaluate the proposed system, simulations of the weakly supervised sound event detection were conducted using the Detection and Classification of Acoustic Scenes and Events 2020 Task 4 database. The results reveal that the proposed system has F1-score performance comparable with the Mel-spectrogram and gammatonegram and exhibits 3–5% better performance than the log-Mel-spectrum and constant-Q transform.

In this paper, methods to estimate the number of basis vectors of the nonnegative matrix factorization (NMF) of automatic music transcription (AMT) systems are proposed. Previously, studies on NMF-based AMT have demonstrated that the number of basis vectors affects the performance and that the number of note events can be a good selection as the rank of NMF. However, many NMF-based AMT methods do not provide a method to estimate the appropriate number of basis vectors; instead, the number is assumed to be given in advance, even though the number of basis vectors significantly affects the algorithm’s performance. Recently, based on Bayesian methods, certain estimation algorithms for the number of basis vectors have been proposed; however, they are not designed to be used as music transcription algorithms but are components of specific NMF methods and thus cannot be used generally as NMF-based transcription algorithms. Our proposed estimation algorithms are based on eigenvalue decomposition and Stein’s unbiased risk estimator (SURE). Because the SURE method requires variance in undesired components as a priori knowledge, the proposed algorithms estimate the value using random matrix theory and first and second onset information in the input music signal. Experiments were then conducted for the AMT task using the MIDI-aligned piano sounds (MAPS) database, and these algorithms were compared with variational NMF, gamma process NMF, and NMF with automatic relevance determination algorithms. Based on experimental results, the conventional NMF-based transcription algorithm with the proposed rank estimation algorithms demonstrated enhanced F1 score performances of 2–3% compared to the algorithms. While the performance advantages are not significantly large, the results are meaningful because the proposed algorithms are lightweight, are easy to combine with any other NMF methods that require an a priori rank parameter, and do not have setting parameters that considerably affect the performance.

This paper presents a new l1-RLS method to estimate a sparse impulse response estimation. A new regularization factor calculation method is proposed for l1-RLS that requires no information of the true channel response in advance. In addition, we also derive a new model to compensate for uncertainty in the regularization factor. The results of the estimation for many different kinds of sparse impulse responses show that the proposed method without a priori channel information is comparable to the conventional method with a priori channel information.