A Voice Activity Detection Algorithm Using Sparse Non-negative Matrix Factorization-based Model Learning in Spectro-Temporal Domain
Publish Year: 1402
نوع سند: مقاله ژورنالی
زبان: English
View: 93
This Paper With 11 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJE-36-8_008
تاریخ نمایه سازی: 10 مرداد 1402
Abstract:
Voice activity detectors are presented to extract silence/speech segments of the speech signal to eliminate different background noise signals. A novel voice activity detector is proposed in this paper using spectro-temporal features extracted from the auditory model of the speech signal. After extracting the scale, rate, and frequency features from this feature space, a sparse structured principal component analysis algorithm is used to consider the basic components of these features and reduce the dimension of learning data. Then these feature vectors are employed to learn the models by the sparse non-negative matrix factorization algorithm. The model learning procedure is performed to represent each feature vector with a proper sparse rate based on the selected atoms. Voice activity detection of the input frames is performed by computing the energy of the sparse representation for each input frame over the composite model. If the calculated energy exceeds a specified threshold, it indicates that the input frame has a structure similar to the atoms of the learned models and concludes that the observed frame has voice content. The results of the proposed detector were compared with other baseline methods and classifiers in this processing field. These results in the presence of stationary, non-stationary and periodic noises were investigated and they are shown that the proposed method based on model learning with spectro-temporal features can correctly detect the silence/speech activities.
Keywords:
Voice Activity Detector , Spectro-temporal domain , Sparse structured principal component analysis , Sparse non-negative matrix factorization
Authors
S. Mavaddati
Faculty of Engineering and Technology, University of Mazandaran, Babolsar, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :