Blind vehicle\u27s sound detec ting technique for advanced safety-driving system

IEEE International Conference on Consumer Electronics (ICCE2009), 10-14 January, 2009, Las Vegas, NV, US

Study of Utterance Support System using Word Input based on Word Prediction

Speech is very important for us to communicate with others. However, because of speech handicaps, there are many people who feel it difficult. Thus, we set our main purpose to study new utterance support system working on an information terminal. To make this system more effective, we made word prediction and added fixed phrase dictionary and class 2-gram as additional functions. Fixed phrase dictionary includes phrases often used in daily life conversations and class 2-gram using co-occurrence frequencies of 9 parts of speech in Japanese. And by using class 2-gram, we made two additional functions. One is generating next candidates and another is changing frequency. We did three evaluation experiments – necessary numbers of input, input speed and measurement experiments of voice synthesis. And results shows that our system needs less numbers of input but slower than other methods and it is faster to do voice synthesis when user input a sentence than other timing. Hereafter, we intend to create more effective dictionaries, improve class 2-gram and develop new GUI to make our system more effective and faster

Comparison of nonverbal feature of free conversation speech between elderly and young individuals

This study explores a system that estimates the degree of decline for elderly persons using nonverbal information from daily conversations. Factors useful in estimating the degree of decline and methods of estimation need to be determined. First, we confirmed whether we can estimate the speaker’s age and “elderly likeness” from the conversation data. We made several processed conversation sounds: (“only included Fillers,” “only included Laughter utterances,” and “only included Intonation information”) from the original conversation database. By listening to these sounds, we confirmed that it is possible to estimate whether the speaker is young or elderly. Next, we analyzed the nonverbal features of conversation sounds of young and elderly individuals. In this paper, we selected the F0 and power value of “laughter utterance” and “speech utterance,” and compared the difference between young and elderly individuals. By comparing the results, we discussed the possibility of estimating a degree of decline.5th IIAE International Conference on Intelligent Systems and Image Processing 2017 (ICISIP2017), September 7–12, 2017, Waikiki, Hawaii, US

Software Log Anomaly Detection Through One Class Clustering of Transformer Encoder Representation

For smart devices such as smartphones and tablets, developing new software using open source software (OSS) is becoming mainstream. While OSS-based development can greatly increase project productivity, it is more difficult to identify the cause of software defects. In this paper, we propose a deep learning model that performs unsupervised learning based on the log data accumulated in the project and calculates the degree of abnormality per line for newly given logs. The proposed method is evaluated using open supercomputer system log data, Blue Gene/L, and the accuracy of the proposed method is compared with the conventional log anomaly detection method using LSTM AutoEncoder. As a result of the comparative experiment, it was found that the proposed method performed better than the conventional method in the two scores of AUROC and F1 Score at the cutoff point.22nd International Conference on Human-Computer Interaction, HCI International 2020, 19-24 July 2020, Copenhagen, Denmark（新型コロナ感染拡大に伴い、現地開催中止

Verification of Generalizability in Software Log Anomaly Detection Models

With recent rapid technological advances, the automatic analysis of software logs has received particular attention. Currently, there is much research on the use of Deep Learning in the field of software log anomaly detection, and they have reported high accuracy of more than 0.9 in the f1-score. On the other hand, there are reports that it has not been used in the field of software development. We conducted a generalized evaluation against representative models for log anomaly detection to elucidate the cause of this problem. Five models were used in the subject: four representative models (two supervised and two unsupervised) and our proposed Neocortical Algorithm (supervised). We used the commonly used Blue Gene/L supercomputer log(BGL) dataset. The learning curves and cross-validation showed a tendency toward overfitting in all models. In addition, a survey of the frequency of log patterns confirmed the need for a more diverse dataset, as many of the patterns are a series of specific logs

A low-bit-rate audio codec using mel-scaled linear predictive analysis

In this paper, we propose a low-bit-rate audio codec using a new analysis method named mel-scaled linear predictive analysis (mel-LP analysis). In mel-LP analysis, a spectral envelope is estimated on a mel- or bark-frequency scale, so as to improve the spectral resolution in the low-frequency band. This analysis is accomplished with about a twofold increase in computation over standard LPC analysis. Our codec using mel-LP analysis consists of five key parts: time frequency transformation, flattening of MDCT coefficients using the mel-LP spectral envelope, power normalization, perceptual weighting estimation, and multistage VQ. In subjective experiments, we investigated the performance of our codec using the mel-LP analysis method, through 7-level paired comparison tests. The result shows that the codec using the mel-LP analysis method results in a good performance at a low bit rate, particularly at 16 kbps. In the cases of pop songs, piano music and male speech, sound quality was improved

Speech recognition interface system for digital TV control

In this paper, we describe a speech recognition interface system for digital TV (DTV) control. TV systems are currently undergoing digitalization and will become more multifunctional, leading to more complex TV operations. Thus, it is necessary for everyone to be able to use TVs easily, and a speech recognition interface is an important key technology. A speech recognition system, which is designed for home use, particularly for digital TV, must be simple and robust to environmental noises and speaker variations. To provide robustness to noise, we developed a noise reduction technique for house noise and an echo-canceling technique for TV sound. To achieve robustness to speaker variations, we developed new speaker adaptation techniques which are incorporated in the system. These of technologies results in a significant improvement in the recognition performance of the DTV

Large scale log anomaly detection via spatial pooling

Log data is an important clue to understanding the behaviour of a system at runtime, but the complexity of software systems in recent years has made the data that engineers need to analyse enormous and difficult to understand. While log-based anomaly detection methods based on deep learning have enabled highly accurate detection, the computational performance required to operate the models has become very high. In this study, we propose an anomaly detection method, SPClassifier, based on sparse features and the internal state of the model, and investigate the feasibility of anomaly detection that can be utilized in environments without computing resources such as GPUs. Benchmark with the latest deep learning models on the BGL dataset shows that the proposed method can achieve competitive accuracy with these methods and has a high level of anomaly detection performance even when the amount of training data is small

LWD-3D: Lightweight Detector Based on Self-Attention for 3D Object Detection

Lightweight modules play a key role in 3D object detection tasks for autonomous driving, which are necessary for the application of 3D object detectors. At present, research still focuses on constructing complex models and calculations to improve the detection precision at the expense of the running rate. However, building a lightweight model to learn the global features from point cloud data for 3D object detection is a significant problem. In this paper, we focus on combining convolutional neural networks with self-attention-based vision transformers to realize lightweight and high-speed computing for 3D object detection. We propose light-weight detection 3D (LWD-3D), which is a point cloud conversion and lightweight vision transformer for autonomous driving. LWD-3D utilizes a one-shot regression framework in 2D space and generates a 3D object bounding box from point cloud data, which provides a new feature representation method based on a vision transformer for 3D detection applications. The results of experiment on the KITTI 3D dataset show that LWD-3D achieves real-time detection (time per image < 20 ms). LWD-3D obtains a mean average precision (mAP) 75% higher than that of another 3D real-time detector with half the number of parameters. Our research extends the application of visual transformers to 3D object detection tasks