Sound source separation is a method to extract a target sound source from a mixture of various sound sources and noises. One of the typical sound source separation methods is beamforming, which can separate sound sources by direction based on the phase difference between channels from the recorded signal of a microphone array, a multi-channel recording system. However, beamforming is a direction-based method and cannot separate multiple sources in the same direction. In this paper, we propose a method for separating sources in the same direction using multiple microphone arrays. The proposed method performs beamforming using multiple microphone arrays and extracts only the target sound source from the separated sound by the Non-negative Matrix Factorization (NMF), thus reducing the influence of other sources in the same direction. In this paper, to investigate the effectiveness of the proposed method, experiments were conducted assuming the presence of another sound source in the same direction from an arbitrary microphone array. The results show that the proposed method outperforms the delay-sum method in a simulation environment. In addition, experiments were conducted in a real environment to verify the effect of reverberation.

For robot and drone auditions, microphone arrays have been used for estimating sound source directions and sound source locations. By using sound source localization techniques, for example, drones can detect people calling for help even if the target person is not visible. Most sound source localization methods are based on estimated sound source directions and triangulation. However, when it comes to situations using drones, severe drone noise distorts direction estimation results which could worsen the localization results badly due to the discreteness of direction estimation. In this perspective, the authors have proposed a sound source localization method that can omit outlying triangulation points, which could improve its localization performance. In this paper, an outdoor experiment has been held, and the proposed method is evaluated whether it can localize a sound source even if real drone noise is added to the recordings. Experiment results show that the proposed method can localize with 4.15 m of estimation error for a sound source up to 50 m away, suppress the impact of outliers, and use only plausible triangulation points.

Abstract This paper proposes a multichannel environmental sound segmentation method. Environmental sound segmentation is an integrated method to achieve sound source localization, sound source separation and classification, simultaneously. When multiple microphones are available, spatial features can be used to improve the localization and separation accuracy of sounds from different directions; however, conventional methods have three drawbacks: (a) Sound source localization and sound source separation methods using spatial features and classification using spectral features trained in the same neural network, may overfit to the relationship between the direction of arrival and the class of a sound, thereby reducing their reliability to deal with novel events. (b) Although permutation invariant training used in autonomous speech recognition could be extended, it is impractical for environmental sounds that include an unlimited number of sound sources. (c) Various features, such as complex values of short time Fourier transform and interchannel phase differences have been used as spatial features, but no study has compared them. This paper proposes a multichannel environmental sound segmentation method comprising two discrete blocks, a sound source localization and separation block and a sound source separation and classification block. By separating the blocks, overfitting to the relationship between the direction of arrival and the class is avoided. Simulation experiments using created datasets including 75-class environmental sounds showed the root mean squared error of the proposed method was lower than that of conventional methods.

This paper proposes an environmental sound segmentation method using Mask U-Net. Recent research in robot audition has analyzed noise reduction, section detection, and sound source separation for use in a real-world environment with many noises and overlaps. However, conventional methods apply respective functions in cascades. The biggest problem of cascade systems is the accumulation of errors generated at each function block. Although many methods of human voice separation have been proposed, robots operating in a real-world environment must be able to separate not only human voices but other environmental sounds. Unlike traditional sound source separation using spatial information, environmental sound segmentation must simultaneously detect sections and separate sound sources based on pre-trained features. One such method, U-Net, which was proposed for semantic segmentation of images, has been applied to the separation of singing voices. However, this method deals only with limited classes of sounds. The current study proposes an environmental sound segmentation method using Mask U-Net, which combines segmentation using U-Net with sound event detection using CNN to 75-classes of environmental sounds. Experimental application confirmed that this method improved learning speed and sound source separation compared with the conventional method.

This paper addresses restoration of acoustic signals for robot audition. A robot usually listens to target acoustic signals such as speech and music in noisy conditions. Acoustic information on such signals inevitably contaminated with noise. Even when noise reduction techniques such as sound source separation are performed, the noise-reduced acoustic signals contain distortion and/or residual noise after the noise reduction to some extent. The distortion and residual noise basically degrade the performance of recognition processes such as automatic speech recognition (ASR). We decided to use bidirectional long short-term memory (Bi-LSTM) for acoustic signal restoration since it can represent dynamic behaviors well for a temporal sequence in the forward and backward directions. When applying Bi-LSTM to recover acoustic signals, there is an issue, that is, acoustic signals tend to be sparse in high frequencies, and thus Bi-LSTM training becomes insufficient in such high frequencies due to a lack of training data. Therefore, we propose a new restoration method based on Bi-LSTM with spectral filtering. The spectral filter and the corresponding inverse filter are introduced to a Bi-LSTM framework to accelerate training in high frequencies. Preliminary results showed that the proposed Bi-LSTM with spectral filtering can perform signal restoration even when a small amount of training data is available.

Abstract: Prior information has been effectively exploited mainly using probabilistic models. In this paper, by focus-ing on the bias embedded in the classifier, we propose a novel method to discriminatively learn the prior bias based on the extra prior information assigned to the samples other than the class category, e.g., the 2-D position where the local image feature is extracted. The proposed method is formulated in the framework of maximum margin to adaptively optimize the biases, improving the classification performance. We also present the computationally efficient optimization approach that makes the method even faster than the standard SVM of the same size. The experimental results on patch labeling in the on-board camera images demonstrate the favorable performance of the proposed method in terms of both classification accuracy and computation time. 1

A novel algorithm for tracking by shape with deforming prediction is proposed. The algorithm is based on the similarity of the predicted and actual object shape. Second order approximation for feature point movement by Taylor expansion is adopted for shape prediction, and the similarity is measured by using chamfer matching of the predicted and the actual shape. Chamfer matching is also used to detect the feature point movements to predict the object deformation. The proposed algorithm is applied to the tracking of a skier and showed a good tracking and shape prediction performance.

A novel visual tracking algorithm is proposed in this paper. The algorithm uses pixel-pair features to discriminate between an image patch with an object in the correct position and image patches with an object in an incorrect position. The pixel-pair feature is considered to be robust for the illumination change, and also is robust for partial occlusion when appropriate features are selected in every video frame. The tracking precision for a deforming object (skier) is examined and also the occlusion detection method is described.

An example-based classification algorithm to improve generalization performance for detecting objects in images is presented. The classifier integrates component-based classifiers according to the AdaBoost algorithm. A probability estimate by a kernel-SVM is used for the outputs of base learners, which are independently trained for local features. The base learners are determined by selecting the optimal local feature according to sample weights determined by the boosting algorithm with cross-validation. Our method was applied to the MIT CBCL pedestrian image database, and 54 sub-regions were extracted from each image as local features. The experimental results showed a good classification ratio for unlearned samples.

Face tracking continues to be an important topic in computer vision. We describe a tracking algorithm based on a static face detector. Our face detector is a rectanglefeature- based boosted classifier, which outputs the confidence whether an input image is a face. The function that outputs this confidence, called a score function, contains important information about the location of a moving target. A target that has moved will be located in the gradient direction of a score function from the location before moving. Therefore, our tracker will go to the region where the score is maximum using gradient information of this function. We show that this algorithm works by the combination of jumping to the gradient direction and precise search at the local region.

The design and performance of a priority forwarding router chip are presented. The chip has four input and four output ports, employs clock-synchronized packet switching, and facilitates 32-bit priority arbitration by means of a priority forwarding scheme that prevents priority inversion and enables accurate priority control within a network. Packets are of a fixed size, each having three 38-bit segments. Each input port has an 8-packet priority queue that enables virtual cut-through switching and pipelined-simultaneous output to at most three different output ports. The chip has two 25-ns pipeline stages and its data transmission rate is 190 MByte/s per port. Clock level simulation shows that the chip can attain high throughput, 9 GByte/s and 34 GByte/s at 64-node and 256-node omega networks with random communication, and excellent real-time performance. Very small laxities are required for in-time delivery of all input packets where the packets exhibit a degree of deadline distribution.>

The authors propose a priority control scheme for packet switching multistage networks, called priority forwarding, which prevents priority inversion, a situation in which higher priority packets are blocked by lower priority packets. In an N*N omega network, the worst case delay of the priority forwarding scheme on the highest priority packet is O(log/sup 2/ N), while that for round-robin arbitration is O(N). Simulation results show that the priority forwarding scheme offers shorter delays for higher priority packets without throughput degradation and fits least-laxity-first control. The hardware implementation cost of this scheme is relatively small and requires no extra signal lines between routers. Consequently, this scheme offered predictability, scalability, and implementation eligibility.>

A processing element and a structure element of data flow computer SIGMA-1 for scientific computations is now operational. The elements are evaluated for several benchmark programs. For efficient execution of loop constructs, the sticky token mechanism which holds loop invariants is evaluated and exhibits a remarkable effect. From the standpoint that performance of a single processor of a data flow computer must be comparable to that of a Von Neumann computer, comparison of both computers is discussed and improvement of the SIGMA-1 instruction set is proposed.

Two parallel hashing algorithms based on open and chained hashing are discussed. The parallel chained hashing algorithm is efficient even in the environment where deletion and insertion frequently occur. The chained hashing memory with fewer memory banks has a performance equivalent to that of the open hashing memory.