Signal separation techniques based on frequency dependency are described. In one implementation, a blind signal separation process is provided that avoids the permutation problem of previous signal separation processes. In the process, two or more signal sources are provided, with each signal source having recognized frequency dependencies. The process uses these inter-frequency dependencies to more robustly separate the source signals. The process receives a set of mixed signal input signals, and samples each input signal using a rolling window process. The sampled data is transformed into the frequency domain, which provides channel inputs to the inter-frequency dependent separation process. Since frequency dependencies have been defined for each source, the process is able to use the frequency dependency to more accurately separate the signals. The process can use a learning algorithm that preserves frequency dependencies within each source signal, and can remove dependencies between or among the signal sources.