1. A signal separation process, comprising: receiving a plurality of mixed input signals at a data processing apparatus, each mixed signal being a mixture of a plurality of signal sources; using the data processing apparatus, sampling each mixed input signal using a respective rolling sampling window; using the data processing apparatus, transforming signal data in each current sampling window to frequency domain data sets; receiving the frequency domain data sets as inputs to an inter-frequency dependent separation process at the data processing apparatus; operating the inter-frequency dependent separation process at the data processing apparatus, the inter-frequency dependent separation process comprising adapting a learning algorithm using an inter-frequency dependency; identifying, by the data processing apparatus, each component of the frequency domain data according to its correct signal source; and generating, by the data processing apparatus, a separated signal for at least one of the signal sources, wherein the inter-frequency dependent separation process uses a multivariate score function defined by the equation: φ ( k ) ⁡ ( s ^ i ( 1 ) , … ⁢ , s ^ i ( K ) ) = - ψ ′ ⁡ ( δ λ ⁡ ( s i ) ) ψ ⁡ ( δ λ ⁡ ( s i ) ) · q λ ′ ⁡ ( s i ) = ξ ⁡ ( δ λ ⁡ ( s i ) ) · s i ( k ) δ λ ⁡ ( s i ) δ λ ⁡ ( s i ) = ( ∑ k ⁢ (  s i ( k ) - μ i ( k )  / σ i ( k ) ) λ ) 1 / λ wherein k represents a frequency bin within range 1 to K, φ(•) is the score function, s i k is the i th source signal for frequency bin k, ŝ i k is the separated i th source signal for frequency k, ψ(•) is an arbitrary function, μ i k and σ i k are mean and variance, respectively, of the k th frequency bin within i th signal, q′ is first derivative of approximated probability density function q(s), δ λ (s) is the λ th norm of vector s, and ξ(x) is an arbitrary non-linear function of x.