【摘要】：針對非結構噪聲難以去除的問題,基于字典訓練和稀疏表示提出一種無監(jiān)督語音增強算法。該算法通過構造過完備字典并使用帶噪語音樣本對其進行訓練來實現(xiàn)。首先指出K-奇異值分解算法(K-SVD)存在的不足并提出一種新的改進的字典訓練算法:K-雙邊隨機投影算法(K-BRP);然后使用K-BRP算法不斷更新字典矩陣和相應的增益系數(shù)矩陣,從被非結構化噪聲所污染的帶噪語音中提取出結構性強的純凈語音。大量實驗結果表明,由于訓練樣本考慮到了語音信號的時頻域局部結構特征,所提算法能夠很好地消除隨機噪聲,并且在低信噪比情況下仍然能夠保持較高的語音質量和可懂度。
[Abstract]:An unsupervised speech enhancement algorithm based on dictionary training and sparse representation is proposed. The algorithm is implemented by constructing a complete dictionary and using noisy speech samples to train it. Firstly, the shortcomings of the K-SVD algorithm (K-SVD) are pointed out, and a new improved dictionary training algorithm: the K-bilateral Random projection algorithm (K-BRP) is proposed. Then the dictionary matrix and the corresponding gain coefficient matrix are updated by K-BRP algorithm to extract the strong structural pure speech from the noisy speech contaminated by unstructured noise. A large number of experimental results show that the proposed algorithm can eliminate random noise very well because the training samples take into account the local structural characteristics of speech signals in time and frequency domain. And it can still maintain high speech quality and intelligibility under low SNR.
【作者單位】： 解放軍理工大學指揮信息系統(tǒng)學院;
【基金】：江蘇省自然科學基金資助項目(BK2012510)
【分類號】：TN912.3

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