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This study presents a novel method for source extraction, referred to as thesimilarity-and-independence-aware beamformer (SIBF). The SIBF extracts thetarget signal using a rough magnitude spectrogram as the reference signal. Theadvantage of the SIBF is that it can obtain an accurate target signal, comparedto the spectrogram generated by target-enhancing methods such as the speechenhancement based on deep neural networks (DNNs). For the extraction, we extendthe framework of the deflationary independent component analysis, byconsidering the similarity between the reference and extracted target, as wellas the mutual independence of all potential sources. To solve the extractionproblem by maximum-likelihood estimation, we introduce two source model typesthat can reflect the similarity. The experimental results from the CHiME3dataset show that the target signal extracted by the SIBF is more accurate thanthe reference signal generated by the DNN.  Index Terms: semiblind source separation, similarity-and-independence-awarebeamformer, deflationary independent component analysis, source model

Similarity-and-Independence-Aware Beamformer: Method for Target Source Extraction using Magnitude Spectrogram as Reference