A new speech enhancement method using sparse reconstruction of the log-spectra is presented. Similar to the traditional sparse coding methods, the proposed algorithm makes use of the least angle regression (LARS) with a coherence criterion (LARC) algorithm to reconstruct the log power spectrum of clean speech. However, a new stopping criterion is introduced to allow the LARC algorithm to adapt to various background noise environments. In addition, a modified two-step noise reduction with a log-MMSE filter is applied which solves the bias of estimated a-priori signal-to-noise ratio (SNR). A notable improvement in the proposed algorithm over traditional speech enhancement methods is its adaptability to the changes in the SNR of noisy speech. The performance of the proposed algorithm is evaluated using standard measures based on a large set of speech and noise signals. The results show that a significant improvement is achieved compared to traditional approaches, especially in non-stationary noise environments where most traditional algorithms fail to perform.
|Number of pages||11|
|Journal||HKIE Transactions Hong Kong Institution of Engineers|
|Publication status||Published - 1 Jan 2017|
- dictionary learning
- sparse representation
- Speech enhancement
ASJC Scopus subject areas