Voice Activity Detection Via Noise Reducing Using Non-Negative Sparse Coding

Voiced/nonvoiced detection in compressively sensed speech signals

“…For V/NV detection voiced and nonvoiced regions of speech are considered for learning the dictionaries. NMF based methods (such as in Teng and Jia (2013) and Franois et al (2013)) are batch algorithms which perform well, but relies on pre-estimation of dictionaries for both noise and clean speech signal for efficient sparse representation, which might not be a suitable choice in the practical scenario. The supervised method proposed in Saeedi et al (2013) employ SVM models trained in different background noises for speech/non-speech classification.…”

“…Recent works in Teng and Jia (2013) and You et al (2012) have proposed methods based on sparse coding for VAD rather than V/NV detection. These methods are discussed here mainly due to their similarity in using a sparse coding framework (but on speech signal directly).…”

“…However, both the methods require several examples of clean speech to learn the dictionary. Moreover, the method in Teng and Jia (2013) based on non-negative matrix factorization (NMF), employs a separate dictionary for noise examples, which is not suitable in practical scenarios, as generally the type of noise is not known a priori. Method in Teng and Jia (2013) uses the maximum and mean value, while method in You et al (2012) uses power spectrum energy of the estimated sparse representation as a VAD feature, which might not be robust in low SNR conditions, and in the presence of highly non-stationary noises having sparse or speech like characteristics.…”

“…Therefore, it is very difficult to detect unvoiced regions as compared to voiced regions in the presence of noise. This is also the reason that recently proposed sparse coding based VAD algorithms (such as in Teng and Jia (2013) and You et al (2012)) have poor performance in detecting unvoiced regions.…”

A novel chicken voice recognition method using the orthogonal matching pursuit algorithm

“…In the aspect of voice recognition, sparse representation has achieved many successes [1][2][3][4][5][6][7][8][9]. G.S.V.S Sivaram et al [10] propose a novel feature extraction technique for speech recognition based on the principles of sparse coding.…”