A sound source separation device, comprising: a plurality of microphones configured to receive a plurality of microphone input signals transmitted from a plurality of sound sources; a matrix unit configured to generate an objective function according to an estimated source vector and an estimated noise vector estimated based on the plurality of microphone input signals, and replace a first term and a second term included in the objective function using a log-likelihood function to estimate a demixing matrix; and an output unit configured to provide output vectors calculated based on the microphone input signals and the demixing matrix, wherein the objective function J ( W t) is defined as J W t = I y ̄ 1, t ⋯ y ̄ K, t z ̄ K + 1, t ⋯ z ̄ M, t = ∑ k = 1 K H y ̄ k, t + ∑ m = K + 1 M H z ̄ m, t − H y ̄ 1, t ⋯ y ̄ K, t z ̄ K + 1, t ⋯ z ̄ M, t = ∑ k = 1 K E G y ̄ k, t + ∑ m = K + 1 M E G z ̄ m, t − ∑ f = 1 F log det W t, f + C where W t is the demixing matrix, Kis the number of the sound sources, M is the number of the microphones, is mutual information, (·) is entropy, y k,t is kth estimated source vector for all frequencies at time t, z m,t is mth estimated noise vector for all frequencies at time t, G ( r) = - log q ( r) is a contrast function, q ( r) is an assumed model probability density function for a random variable r, E [·] is an expected value, and C is a constant, wherein the log-likelihood function ( W t, Λ t, Σ t) is defined as L W t Λ t Σ t = − ∑ k = 1 K ∑ t = 1 T F log πλ k, t + y ̄ k, t 2 λ k, t − ∑ m = K + 1 M ∑ t = 1 T ∑ f = 1 F log πσ m, t, f + Z m, t, f 2 σ m, t, f, where λ k,t is a variance of kth estimated source vector at time t, y k,t is the estimated source vector, Z m,t,f is mth estimated noise constituting the estimated noise vector, and σ m,t,f is a variance of the mth estimated noise vector at time t and frequency f, wherein, in the objective function, the sum over k of expected values E is the first term, the sum over m of expected values is the second term, and the sum over f of log magnitudes is a third term greater than 0, and wherein a Lagrangian function is maximized to maximize the log-likelihood function under a constraint that the third term is not negative. a plurality of microphones configured to receive a plurality of microphone input signals transmitted from a plurality of sound sources; a matrix unit configured to generate an objective function according to an estimated source vector and an estimated noise vector estimated based on the plurality of microphone input signals, and replace a first term and a second term included in the objective function using a log-likelihood function to estimate a demixing matrix; and an output unit configured to provide output vectors calculated based on the microphone input signals and the demixing matrix, wherein the objective function J ( W t) is defined as J W t = I y ̄ 1, t ⋯ y ̄ K, t z ̄ K + 1, t ⋯ z ̄ M, t = ∑ k = 1 K H y ̄ k, t + ∑ m = K + 1 M H z ̄ m, t − H y ̄ 1, t ⋯ y ̄ K, t z ̄ K + 1, t ⋯ z ̄ M, t = ∑ k = 1 K E G y ̄ k, t + ∑ m = K + 1 M E G z ̄ m, t − ∑ f = 1 F log det W t, f + C where W t is the demixing matrix, Kis the number of the sound sources, M is the number of the microphones, is mutual information, (·) is entropy, y k,t is kth estimated source vector for all frequencies at time t, z m,t is mth estimated noise vector for all frequencies at time t, G ( r) = - log q ( r) is a contrast function, q ( r) is an assumed model probability density function for a random variable r, E [·] is an expected value, and C is a constant, wherein the log-likelihood function ( W t, Λ t, Σ t) is defined as L W t Λ t Σ t = − ∑ k = 1 K ∑ t = 1 T F log πλ k, t + y ̄ k, t 2 λ k, t − ∑ m = K + 1 M ∑ t = 1 T ∑ f = 1 F log πσ m, t, f + Z m, t, f 2 σ m, t, f, where λ k,t is a variance of kth estimated source vector at time t, y k,t is the estimated source vector, Z m,t,f is mth estimated noise constituting the estimated noise vector, and σ m,t,f is a variance of the mth estimated noise vector at time t and frequency f, wherein, in the objective function, the sum over k of expected values E is the first term, the sum over m of expected values is the second term, and the sum over f of log magnitudes is a third term greater than 0, and wherein a Lagrangian function is maximized to maximize the log-likelihood function under a constraint that the third term is not negative.