A New VAD Algorithm using Sparse Representation and Updated Dictionary in Spectrogram Domain

This article proposes the new VAD (Voice Activity Detection) method was made using Spectrogram Domain (Spectro-Temporal Response Field) space based on sparse representation. Spectrogram Domain components have two dimensions of time and frequency. On the other hand, using sparse representation in learning dictionaries of speech and noise and updating dictionaries, causes better separation of speech and noise segments. In this algorithm, using auditory spectrogram and sparse representation, an updating dictionaries with different atom sizes and K-SVD (k-means clustering method) and NMF (non-negative matrix factorization) learning methods were constructed and the results indicate that this method works well. For example, the proposed VAD performance was obtained in SNRs greater than ۰dB is more than ۹۲.۷۱% and ۹۱.۲۱% in White noise and Car noise respectively, which shows the good performance of the proposed VAD compared to other methods. By comparing the NDS and MSC evaluation parameters with other methods, the results show better performance of the proposed method.

Keywords:

Spectro-Temporal Response Field , Voice Activity Detection (VAD) , sparse representation , updating dictionaries