Abstract: Sound source enhancement technology is provided that is capable of improving sound source enhancement capabilities in accordance with settings of usage and applications. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value {circumflex over (?)}?S(?, ?), an interference noise PSD input value {circumflex over (?)}?IN(?, ?), and a background noise PSD input value {circumflex over (?)}?BN(?, ?) as input, and generates a target sound PSD output value ?S(?, ?), an interference noise PSD output value {circumflex over (?)}?IN(?, ?), and a background noise PSD output value {circumflex over (?)}?BN(?, ?), by solving an optimization problem for a cost function relating to a variable uS representing a target sound PSD, a variable uIN representing an interference noise PSD, and a variable uBN representing a background noise PSD.

Abstract: There are provided a detection method, a detection device, and a program that do not cause a difference in events to be detected even when physical characteristics of an acoustic signal change. The detection method includes: a step of acquiring a target sound for detecting an event; and a detecting step of detecting a desired event included in the acquired sound, and in the detecting step, even when any one of a distance and a direction of a sound source of the event, which are based on a position where the target sound is collected, and an occurrence time of the event changes, the events are always detected as the same event.

Abstract: Provided is sound source enhancement technology that is capable of improving sound source enhancement capabilities in a configuration using a beamformer to suppress interference noise. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value, an interference noise PSD input value, and a background noise PSD input value as input, and generates a target sound PSD output value, an interference noise PSD output value, and a background noise PSD output value, by solving an optimization problem for a cost function relating to a variable representing a target sound PSD, a variable representing an interference noise PSD, and a variable representing a background noise PSD.

Abstract: Provided is a filter coefficient optimization technology that makes it possible to design a stable beamformer having a good quality by considering the relationship of a filter coefficient between adjacent frequency bins. A filter coefficient optimization apparatus includes an optimization unit that calculates an optimum value of a filter coefficient w={w1, . . .

Abstract: A sound source separation device (10) acquires, from a mixed signal including sounds that came from a plurality of sound sources, a separated signal including an emphasized sound for every sound source. A signal conversion unit (1) converts the mixed signal into the frequency domain. A separated signal estimation unit (2) acquires the separated signals from the mixed signal using an optimized filter. A gradient calculation unit (3) calculates the gradient of a cost function using the mixed signal and the separated signals. A filter update unit (4) optimizes the filter to fulfill separating, for every sound source, a sound emitted from the sound source, and to fulfill having, for every sound source, strong directivity in a direction of the sound source compared with a direction not of the sound source. A signal inverse conversion unit (5) converts the separated signals into the time domain.

Abstract: Provided is a technology of optimizing a latent variable by solving a convex optimization problem equivalent to a non-convex optimization problem instead of solving the non-convex optimization problem. A latent variable optimization apparatus includes an optimization unit that calculates an optimum value ˜w* of a latent variable ˜w based on an optimization problem min˜w(Lconvex(˜w)+?d=1DLd(˜w)), Lconvex being a strongly convex function relevant to the latent variable ˜w, Ld being a function relevant to the latent variable ˜w, Sd,1, . . . , Sd,C being a region that is obtained by dividing a domain of the function Ld into C closed convex sets, ?d,c being a convex function that is defined on the region Sd,c and that approximates the function Ld, cd being a discrete variable that has a value of 1, . . . , C, the optimization unit calculating the optimum value ˜w* by solving an optimization problem minc_1, . . . , c_D (min˜w(Lconvex (˜w)+?d=1D?d,c_d(˜w))) instead of solving the above optimization problem.

Abstract: Provided is a technology that optimizes a variable being an optimization target at high speed. A variable optimization apparatus includes a variable update unit configured to, by assuming that w is a variable being an optimization target. G(w)(=G1(w)+G2(w)) is a cost function for optimizing the variable w, calculated by using input data. D is a strictly convex function that is differentiable and satisfies ?D(0)=0. Ri and Ci are a D-resolvent operator and a D-Cayley operator, respectively and ?Gi(w) is a strongly convex function approximating a function Gi(w), recursively calculate a value of the variable w by using the D-resolvent operator Ri and the D-Cayley operator Ci. When the variable update unit calculates ?D(w), for a D-resolvent operator R1 and a D-Cayley operator C1, T1(w)=??G1(w)???G1(0) is used for calculation of ?D(w), and for a D-resolvent operator R2 and a D-Cayley operator C2, ?T2(w)=??G2(w)???G2(0) is used for calculation of ?D(w).

Abstract: Sound source enhancement technology is provided that is capable of improving sound source enhancement capabilities in accordance with settings of usage and applications. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value {circumflex over (?)}?S(?, ?), an interference noise PSD input value {circumflex over (?)}?IN(?, ?), and a background noise PSD input value {circumflex over (?)}?BN(?, ?) as input, and generates a target sound PSD output value ?S(?, ?), an interference noise PSD output value {circumflex over (?)}?IN(?, ?), and a background noise PSD output value {circumflex over (?)}?BN(?, ?), by solving an optimization problem for a cost function relating to a variable uS representing a target sound PSD, a variable uIN representing an interference noise PSD, and a variable uBN representing a background noise PSD.

Abstract: An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Abstract: Provided s sound source enhancement technology that is capable of improving sound source enhancement capabilities a configuration using a beamformer to suppress interference noise. A PSD optimization device includes a PSD updating unit that takes a target sound PSD input value, an interference noise PSD input value, and a background noise PSD input value as input, and generates a target sound PSD output value, an interference noise PSD output value, and a background noise PSD output value, by solving an optimization problem for a cost function relating to a variable representing a target sound PSD, a variable representing an interference noise PSD, and a variable representing a background noise PSD.

Abstract: There is provided a technique for optimizing a latent variable by using a cost function in which a plurality of probabilistic assumptions are reflected simultaneously. A latent variable optimization apparatus includes an optimization unit which optimizes a latent variable ˜w*, vj(1?j?J) is an auxiliary variable of the latent variable ˜w* expressed as vj=Dj˜w*+bj by using a matrix Dj and a vector bj, a cost term of the auxiliary variable vj is expressed by using a probability distribution of the auxiliary variable vj which is log-concave, and the optimization unit optimizes the latent variable ˜w* by solving a minimization problem of a cost function including the sum of the cost term of the auxiliary variable vj.

Abstract: Provided is a non-negative matrix factorization technology of achieving high-speed and stable convergence. A non-negative matrix factorization optimization device includes an optimization unit configured to optimize a non-negative matrix {A, B}, which is factorization of a matrix Z satisfying Z=ABT, by inputting a measurement matrix Y, where Y represents a non-negative measurement matrix representing a measurement signal or measurement data, and Z represents a non-negative matrix constructing the measurement matrix Y, and a cost function J to be used for optimization by the optimization unit is defined by an expression of J(A, B)=G(Y?ABT)+HA(A)+HB(B), where G represents a loss term, HA represents a normalization term for the matrix A, and HB represents a normalization term for the matrix B, and the optimization unit optimizes the matrix {A, B} based on Bregman monotone operator splitting.

Abstract: Machine learning techniques which allow machine learning to be performed even when a cost function is not a convex function are provided. A machine learning system includes a plurality of node portions which learn mapping that uses one common primal variable by machine learning based on their respective input data while sending and receiving information to and from each other. The machine learning is performed so as to minimize, instead of a cost function of a non-convex function originally corresponding to the machine learning, a proxy convex function serving as an upper bound on the cost function. The proxy convex function is represented by a formula of a first-order gradient of the cost function with respect to the primal variable or by a formula of a first-order gradient and a formula of a second-order gradient of the cost function with respect to the primal variable.

Abstract: A portable band saw has a substantially equally balanced weight in the lateral direction. The portable band saw includes a battery mount to which a battery is attachable, a motor driven with electric power from the battery, a band saw blade being endless and rotatable with power generated by the motor, a body supporting the motor and the band saw blade, and a handle including a support connected to the body and extending in a vertical direction, and a grip connected to the support and extending in a front-rear direction. The motor has a center on one side of a center of the handle, and the battery has a center on another side of the center of the handle in a lateral direction.

Abstract: Techniques for achieving a scalable DNN and a multi-task DNN, for example, are provided. A neural network system is a neural network system including a plurality of models. Each one of the plurality of models is a DNN including a plurality of layers. Some or all of the plurality of models include at least one layer for which some or all of model variables are equivalent or common (hereinafter referred to as a “shared layer”) and also include at least one layer for which the model variables are not equivalent nor common (hereinafter referred to as an “unshared layer”).

Abstract: A jigsaw includes a jigsaw main body, a handle, and a battery. The jigsaw main body includes a housing, a motor disposed inside the housing, and a battery mounting portion having a first mounting surface facing downward. The handle is disposed at an upper portion of the jigsaw main body and above the motor. The battery has a second mounting surface and supplies the motor with an electric power. The battery is slidable along the first mounting surface and mountable to the battery mounting portion with the first and second mounting surfaces facing each other.

Abstract: An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.

Abstract: A jigsaw includes a jigsaw main body, a handle, and a battery. The jigsaw main body includes a housing, a motor disposed inside the housing, and a battery mounting portion having a first mounting surface facing downward. The handle is disposed at an upper portion of the jigsaw main body and above the motor. The battery has a second mounting surface and supplies the motor with an electric power. The battery is slidable along the first mounting surface and mountable to the battery mounting portion with the first and second mounting surfaces facing each other.

Abstract: A signal processing technique the noise suppressing performance of which is more improved than conventional one is provided. A first component extraction unit 14 extracts a non-stationary component ^?S(A)(?, ?) derived from a sound coming from a target area and a stationary component ^?S(B)(?, ?) derived from an incoherent noise from a power spectrum density ^?S(?, ?) of the target area through processing of time average. A second component extraction unit 15 extracts a non-stationary component ^?N(A)(?, ?) derived from an interference noise and a stationary component ^?N(B)(?, ?) derived from an incoherent noise from a power spectrum density ^?N(?, ?) of a noise area.

Abstract: A jigsaw includes a motor, a rod having a blade attached thereto, a power transmission portion that converts rotation transmitted from the motor to vertical movement of the rod to vertically move the blade, and a counterweight that moves vertically in a direction of the vertical movement of the rod outside a range of the vertical, movement of the rod and the blade. At least a part of the counterweight is disposed so as to cross an extended line of a trajectory of the vertical movement of the rod. The counterweight moves vertically independently of the vertical movement of the rod which is produced by the power transmission portion.