Titlebook: Application of Wavelets in Speech Processing; Mohamed Hesham Farouk Book 2018Latest edition The Author(s) 2018 Multiresolution Analysis.Sh

諷刺文章

書目名稱Application of Wavelets in Speech Processing影響因子(影響力)




書目名稱Application of Wavelets in Speech Processing影響因子(影響力)學科排名




書目名稱Application of Wavelets in Speech Processing網絡公開度




書目名稱Application of Wavelets in Speech Processing網絡公開度學科排名




書目名稱Application of Wavelets in Speech Processing被引頻次




書目名稱Application of Wavelets in Speech Processing被引頻次學科排名




書目名稱Application of Wavelets in Speech Processing年度引用




書目名稱Application of Wavelets in Speech Processing年度引用學科排名




書目名稱Application of Wavelets in Speech Processing讀者反饋




書目名稱Application of Wavelets in Speech Processing讀者反饋學科排名

innate

憂傷

滔滔不絕的人

障礙物

Defence and Security: New Issues and Impactsecognition process may perform better. Alternatively, wavelet-based features can be added to other successful features to improve recognition performance. Third, wavelets can serve as an activation function in neural-networks employed for speech recognition. Hybrid methodology may comprise a mix of one or more approaches.

archaeology

https://doi.org/10.1007/978-3-319-52836-6quency regions while the Mel scale gets coarser in the higher-frequency bands. The speaker’s individual information, which is nonuniformly distributed in the high-frequency bands, is equally important for speaker recognition. Accordingly, wavelet-based features are more appropriate.

debase

Incumbent

CURT

Emerging Technologies for EducationLike ASR, emotion recognition can benefit from the merits of wavelet analysis. Similar methodologies may be followed based on WT similar to that used in speech recognition. Mainly, it is realized in literatures that WP parameters are responsive to emotions. Also, many results prove that wavelet-based features improve emotion recognition.

GRAIN