Performance Evaluation of Independent Low-rank Matrix Analysis for Short Signals
Résumé
In this paper, we evaluate the performance of independent low-rank matrix analysis (ILRMA) for short signals. ILRMA is a state-of-the-art blind source separation (BSS) technique based on the assumptions that sources are statistically independent, and their spectrograms are approximately expressed as low-rank matrices. Because ILRMA estimates many parameters such as demixing matrices, spectral bases, and source activations, it needs a sufficient-length observation for the stable estimation. Then, the performance of ILRMA could degrade when the available signals are short. For improving this, we apply the latest ILRMA algorithms to a short mixture and investigate the dependence of the performance on the signal length.
Origine | Fichiers éditeurs autorisés sur une archive ouverte |
---|