Acoustic signals picked up by microphones in speech recognition, video production, or the like also include ambient sound signals such as background noise and music in addition to voice signals of voice of users or actors. There are source separation technologies as technologies for extracting only desired signals from acoustic signals in which voice signals and ambient sound signals are mixed.
For example, a source separation technology using non-negative matrix factorization (NMF) is known. The NMF is a technique of decomposing a plurality of spectra into a product of a basis matrix and a coefficient matrix. If a basis matrix of ambient sound is generated in advance by using training samples, a high-variance, non-stationary ambient sound spectrum can be estimated by adjusting a coefficient matrix according to the spectrum of an acoustic signal to be processed. A voice spectrum can also be estimated similarly to the ambient sound spectrum, and ambient sound signals and voice signals can be extracted.
In order to perform source separation with high accuracy on mixtures of various ambient sounds and scene changes, it is desirable to use acoustic signals previously picked up as training samples. On the other hand, in order to realize source separation at a low computational cost, such as to minimize delays, it is desirable to estimate ambient sound by using few samples smaller than several seconds, for example.
With the technologies of the conventional system, however, spectral distribution of ambient noise cannot be estimated with high accuracy by using a small number of samples. That is, there have been disadvantages that the accuracy of estimation of average components of ambient sound is lowered (estimated ambient sound components are insufficient) and that much ambient sound remains in extracted voice (separation performance is lowered).