A noise suppression apparatus that estimates a desired information from only an observed information in which noise is mixed in with the desired information, the noise suppression apparatus using a state model and an observation model, the state model being defined as: x p 1 n + 1 = Φ p 1 x p 1 n + δ p 1 n + 1 where x p 1 ( n), resp. x p 1 ( n +1) is a state vector at time n, resp. n +1, of dimension L p 1, Φ p 1 is the state transition matrix of the state model, of dimension L p 1 × L p 1, δ p 1 ( n +1) is the driving source vector at time n +1, of dimension L p 1, and the observation model being defined as: y p 1 n + 1 = m p 1 T x p 1 n + 1 + ε p 1 n + 1 where y p 1 ( n +1) and ε p 1 ( n +1) are respectively an observation signal of the model and an observation noise of the model at time n +1, y p 1 ( n +1) and ε p 1 ( n +1) being scalars, m p 1 T being a vector of dimension L p 1 and: x p 1 n + 1 = d n + 1, d n,..., d n − L p 1 + 2 T δ p 1 n + 1 = d n + 1,0,...,0 T y p 1 n + 1 = r n + 1 ε p 1 n + 1 = ν n + 1 Φ p 1 = 0 0...... 0 1 0...... 0 0 ⋱ ⋱... ⋮ ⋮ ⋱ ⋱ ⋱ ⋮ 0... 0 1 0 m p 1 = 1,0,...,0 T where d ( n +1) is a desired signal at time n +1, v ( n +1) is a noise affecting the desired signal and r ( n +1)= d ( n +1)+ v ( n +1) is an observed signal at time n +1, said apparatus comprising: a correlation computation section that calculates a correlation matrix ( P p 1 ( n +1| n)) of the estimation error on the state vector at time n +1, when the state vector at time n +1 is estimated on information up to time n; a weighting vector calculation section that uses said correlation matrix calculated by the correlation computation section to calculate a weighting vector ( k p1 ( n +1)) for specifying a relationship between an optimum estimate ( x̂ p 1 ( n +1| n +1)) of the state vector at that time n +1, based on information up to time n +1, and an optimum estimate ( ( x̂ p 1 ( n +1| n)) of the state vector at time n+1 based on information up to time n, and an estimation error on the observed signal at time n +1; and an optimum estimate calculation section that uses said weighting vector calculated by the weighting vector calculation section to calculate an optimum estimate of the state vector at time n +1 based on information up to time n+1 ( x̂ p 1 ( n +1| n +1)). a correlation computation section that calculates a correlation matrix ( P p 1 ( n +1| n)) of the estimation error on the state vector at time n +1, when the state vector at time n +1 is estimated on information up to time n; a weighting vector calculation section that uses said correlation matrix calculated by the correlation computation section to calculate a weighting vector ( k p1 ( n +1)) for specifying a relationship between an optimum estimate ( x̂ p 1 ( n +1| n +1)) of the state vector at that time n +1, based on information up to time n +1, and an optimum estimate ( ( x̂ p 1 ( n +1| n)) of the state vector at time n+1 based on information up to time n, and an estimation error on the observed signal at time n +1; and an optimum estimate calculation section that uses said weighting vector calculated by the weighting vector calculation section to calculate an optimum estimate of the state vector at time n +1 based on information up to time n+1 ( x̂ p 1 ( n +1| n +1)).