Abstract: The transfer function estimation device includes: a correlation matrix computing unit 43 computing a correlation matrix of N frequency domain signals y(f,l); a signal space basis vector computing unit 44 obtaining M vectors v1(f), . . . , vM(f) from eigenvectors of the correlation matrix from highest in the order of corresponding eigenvalues; and a plural RTF estimation unit 45 determining ti(f), . . . , tM(f) that satisfy the relationship of Expression (1), determining a matrix D(f) that is not a zero matrix and that makes ui(f), . . . , uM(f) defined by Expression (2) sparse in a time direction, determining ci,1(f), . . . , cM,N(f) that satisfy the relationship of Expression (3), and outputting c1(f)/c1,j(f), . . . , cM(f)/cM,j(f) as a relative transfer function, where j is an integer of 1 or more and not more than N.

Abstract: A neural network is learned that uses learning data including a low-bit signal obtained by quantizing a signal to a first number of quantization bits and a high-bit signal obtained by quantizing the signal to a second number of quantization bits larger than the first number of quantization bits, to receive as an input a low-bit input signal obtained by quantizing an input signal to the first number of quantization bits and output an estimated signal of a high-bit output signal obtained by quantizing the input signal to the second number of quantization bits. This neural network has a multilayer structure including an input layer and an output layer, and obtains and outputs an estimated signal of a high-bit output signal obtained by adding to a low-bit input signal a signal output from the output layer in response to the low-bit input signal being input to the input layer.

Abstract: A clipped signal is accurately restored with a constant computational amount. A frame division unit (11) generates input data including a post-clip signal and clip information representing a clipped part in the post-clip signal. A waveform restoration unit (12) estimates a pre-clip signal from the input data using a signal restoring neural network. Using a pre-clip signal, a post-clip signal, and clip information as learning data, the signal restoring neural network is made to learn to receive the input data as input, and output an estimated value of the pre-clip signal. A frame combination unit (13) combines frames of the pre-clip signal.

Abstract: The transfer function estimation device includes: a correlation matrix computing unit 43 computing a correlation matrix of N frequency domain signals y(f,l); a signal space basis vector computing unit 44 obtaining M vectors v1(f), . . . , vM(f) from eigenvectors of the correlation matrix from highest in the order of corresponding eigenvalues; and a plural RTF estimation unit 45 determining ti(f), . . . , tM(f) that satisfy the relationship of Expression (1), determining a matrix D(f) that is not a zero matrix and that makes ui(f), . . . , uM(f) defined by Expression (2) sparse in a time direction, determining ci,1(f), . . . , cM,N(f) that satisfy the relationship of Expression (3), and outputting c1(f)/c1,j(f), . . . , cM(f)/cM,j(f) as a relative transfer function, where j is an integer of 1 or more and not more than N.

Abstract: N correlated signals are processed by N pre-filters whose transfer characteristics have different zero points, then the processed signals are input into an N-input M-output linear FIR system, and its transfer characteristics are estimated from its response outputs and the processed signals from the pre-filters.