Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

A mobile terminal including a body; a touchscreen provided to a front and extending to side of the body and configured to display content; and a controller configured to detect one side of the body comes into contact with one side of an external terminal, display a first area on the touchscreen corresponding to a contact area of the body and the external terminal and a second area including the content, receive an input of moving the content displayed in the second area to the first area, display the content in the first area, and share the content in the first area with the external terminal.

Mobile terminal and method for controlling the same

Journal of the American Statistical Association: William S. Cleveland, Marylyn E. McGill and Robert McGill, “The shape parameter for a two variable graph” 83 (1988) 289–300

Robotics and autonomous systems

While a robot is moving, ego noise is generated due to the fans and motors of the robot. Furthermore, a robot is not only subject to the ego noise, but also to the ambient noise of the environment, both having different short-term signal characteristics. Because ego-motion noise generated by the motors is non-stationary, and the BackGround Noise (BGN) is stationary, one single noise estimation method is unable to track the changes in both noise spectra rapidly and accurately. Therefore, we propose to use the combination of two different noise estimation methods adequate for each one of co-existing noise types in a unified framework: 1) a stationary noise estimation method called Histogram-based Recursive Level Estimation (HRLE) and 2) a non-stationary noise estimation method called Template-based Estimation (TE). In this paper, we evaluate the performance of several single-channel based noise estimation techniques in terms of their prediction accuracy and quality of the speech signals enhanced by spectral subtraction methods. The experimental results show that our system, compared to the conventional single-stage noise estimation methods, achieves better performance in attaining signal quality and improving word correct rates.

http://www.jp.honda-ri.com/upload/document/entry/20111011/Ince_2011_864_5410.pdf

Assessment of single-channel ego noise estimation methods

Many previous studies have shown that the remote photoplethysmography (rPPG) can measure the Heart Rate (HR) signal with very high accuracy. The remote measurement of the Pulse Rate Variability (PRV) signal is also possible, but this is much more complicated because it is then necessary to detect the peaks on the temporal rPPG signal, which is usually quite noisy and has a lower temporal resolution than PPG signals obtained by contact equipment. Since the PRV signal is vital for various applications such as remote recognition of stress and emotion, the improvement of PRV measurement by rPPG is a critical task. Contact based PRV measurement has already been investigated, but the research on remotely measured PRV is very limited. In this paper, we propose to use the Periodic Variance Maximization (PVM) method to extract the rPPG signal and event-related Two-Window algorithm to improve the peak detection for PRV measurement. We have made several contributions. Firstly, we show that the newly proposed PVM method and Two-Window algorithm can be used for PRV measurement in the non-contact scenario. Secondly, we propose a method to adaptively determine the parameters of the Two-Window method. Thirdly, we compare the algorithm with other attempts for improving the non-contact PRV measurement such as the Slope Sum Function (SSF) method and the Local Maximum method. We calculated several features and compared the accuracy based on the ground truth provided by contact equipment. Our experiments showed that this algorithm performed the best of all the algorithms.

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02740220/document

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

54 Kawaharacho, Syogoin, Sakyo-ku, Kyoto City 606-8507, Japan E-mail: sugiyama@kuhp.kyoto-u.ac.jp ∗2Graduate School of Information Science and Engineering, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan ∗3Department of Electrical and Electronic Engineering, School of Engineering, Tokyo Institute of Technology 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan ∗4Honda Research Institute Japan Co., Ltd. 8-1 Honcho, Wako, Saitama 351-0188, Japan

Outdoor Acoustic Event Identification with DNN Using a Quadrotor-Embedded Microphone Array

In this paper, we show a method that significantly improved our previous work in single-channel dereverberation. The proposed method is more robust to changes in speaker position in distant talking ASR. First, we update the room transfer function (RTF) and weighting parameters for dereverberation to the target speaker position. This scheme corrects speech power variation as a function of position in the waveform level. Consequently, its impact to the acoustic model is verified. Then, we implement a fast acoustic model update reflective of the speech power level of the target speaker position. Furthermore, the scheme in updating the model is simple and precludes time-consuming model re-estimation. As a result, the proposed method can be executed online. The synergy of these corrective measures significantly minimizes the mismatch between training and testing conditions. We test our method using real reverberant data with different locations inside the room. Experimental results show that the proposed method outperforms the conventional methods in terms of ASR performance. Moreover, our fast acoustic model update scheme is at par in terms of recognition performance against time-consuming model re-estimation.

http://www.jp.honda-ri.com/upload/document/entry/20130610/Gomez_2013_997_6600.pdf

Robustness to speaker position in distant-talking automatic speech recognition

In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a staterecovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e.g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e.g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition.

/pdf/live-assessment-of-beat-tracking-for-robot-audition-3jsjd7jsf1.pdf

Keisuke Nakamura

Papers

Assessment of single-channel ego noise estimation methods

Video-Based Pulse Rate Variability Measurement Using Periodic Variance Maximization and Adaptive Two-Window Peak Detection.

Outdoor Acoustic Event Identification with DNN Using a Quadrotor-Embedded Microphone Array

Robustness to speaker position in distant-talking automatic speech recognition

Live assessment of beat tracking for robot audition