Recently, lots of algorithms using machine learning approaches have been proposed in the speech enhancement area. One of the most well-known approaches is the non-negative matrix factorization (NMF) -based one which analyzes noisy speech with speech and noise bases. However, NMF-based algorithms have difficulties in estimating speech and noise encoding vectors when their subspaces overlap. In this paper, we propose a novel speech enhancement algorithm which uses deep neural network (DNN) to improve the encoding vector estimation of the NMF-based technique. A DNN is trained to represent the mapping from noisy speech to corresponding encoding vectors. The quality of the enhanced speech from the proposed NMF-based scheme adopting DNN-based encoding vector estimation is compared with that from the conventional NMF-based technique. The experimental results showed that the proposed speech enhancement algorithm outperformed the conventional NMF-based speech enhancement technique.
Bibliographic reference. Kang, Tae Gyoon / Kwon, Kisoo / Shin, Jong Won / Kim, Nam Soo (2014): "NMF-based speech enhancement incorporating deep neural network", In INTERSPEECH-2014, 2843-2846.