A method for determining a dictionary of base components from an audio signal, the audio signal being represented by an input matrix which columns comprise features of the audio signal at different instances in time, the method comprising: decomposing the input matrix into a product of a non-negative base matrix and a non-negative weight matrix, the decomposing being constrained by a Wiener entropy measure with respect to elements of the non-negative weight matrix, wherein components of the non-negative base matrix represent the dictionary of base components of the audio signal (401; wherein the decomposing constrained by the Wiener entropy measure comprises: minimizing a weighted sum of Wiener entropy values of columns of the non-negative weight matrix by using a cost function, and wherein the cost function is according to ‖ V ⊗ ln V W ⋅ H − V + W ⋅ H ‖ 1 + λ ∑ j = 1 n ω j ∏ i = 1 r ⌊ H i, j ⌋ ε 1 / r 1 r ∑ i = 1 r H i, j where V denotes the input matrix, W denotes the non-negative base matrix, H denotes the non-negative weight matrix with elements H i,j, the operation ∥·∥ 1 denotes the vector 1-norm, the symbol ⊗ denotes the Hadamard product, i.e. element-wise multiplication, the symbol ÷ denotes the element-wise division, λ is a real non-negative parameter, ω j denote non-negative weighting parameters which can depend on the matrix H, the symbol ε denotes a positive real number and the operator [·] ε is defined as ⌊ x ⌋ ε = max x ε. decomposing the input matrix into a product of a non-negative base matrix and a non-negative weight matrix, the decomposing being constrained by a Wiener entropy measure with respect to elements of the non-negative weight matrix, wherein components of the non-negative base matrix represent the dictionary of base components of the audio signal (401; wherein the decomposing constrained by the Wiener entropy measure comprises: minimizing a weighted sum of Wiener entropy values of columns of the non-negative weight matrix by using a cost function, and wherein the cost function is according to ‖ V ⊗ ln V W ⋅ H − V + W ⋅ H ‖ 1 + λ ∑ j = 1 n ω j ∏ i = 1 r ⌊ H i, j ⌋ ε 1 / r 1 r ∑ i = 1 r H i, j where V denotes the input matrix, W denotes the non-negative base matrix, H denotes the non-negative weight matrix with elements H i,j, the operation ∥·∥ 1 denotes the vector 1-norm, the symbol ⊗ denotes the Hadamard product, i.e. element-wise multiplication, the symbol ÷ denotes the element-wise division, λ is a real non-negative parameter, ω j denote non-negative weighting parameters which can depend on the matrix H, the symbol ε denotes a positive real number and the operator [·] ε is defined as ⌊ x ⌋ ε = max x ε. minimizing a weighted sum of Wiener entropy values of columns of the non-negative weight matrix by using a cost function, and wherein the cost function is according to ‖ V ⊗ ln V W ⋅ H − V + W ⋅ H ‖ 1 + λ ∑ j = 1 n ω j ∏ i = 1 r ⌊ H i, j ⌋ ε 1 / r 1 r ∑ i = 1 r H i, j where V denotes the input matrix, W denotes the non-negative base matrix, H denotes the non-negative weight matrix with elements H i,j, the operation ∥·∥ 1 denotes the vector 1-norm, the symbol ⊗ denotes the Hadamard product, i.e. element-wise multiplication, the symbol ÷ denotes the element-wise division, λ is a real non-negative parameter, ω j denote non-negative weighting parameters which can depend on the matrix H, the symbol ε denotes a positive real number and the operator [·] ε is defined as ⌊ x ⌋ ε = max x ε.