Noise robust model-based voice activity detection

This paper describes a study of noise-robust voice activity detection (VAD) utilizing the periodicity of the signal, full band signal energy and high band to low band signal energy ratio. Conventional VADs are sensitive to a variably noisy environment especially with low SNR, and also result in cutting off unvoiced regions of speech as well as random oscillating of output VAD decisions. To overcome these problems, the proposed algorithm first identifies voiced regions of speech and then differentiates unvoiced regions from silence or background noise using the energy ratio and total signal energy. The performance of the proposed VAD algorithm is tested on real speech signals. Comparisons confirm that the proposed VAD algorithm outperforms the conventional VAD algorithms, especially in the presence of background noise.

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An Impulse Noise Robust Voice Activity Detection Algorithm Applied for Low Signal-to-Noise Ratio Digital Communication

2006 8th international Conference on Signal Processing ◽

10.1109/icosp.2006.345861 ◽

2006 ◽

Author(s):

Tong Wang ◽

Hui-juan Cui ◽

Kun Tang

Keyword(s):

Impulse Noise ◽

Digital Communication ◽

Signal To Noise Ratio ◽

Detection Algorithm ◽

Voice Activity Detection ◽

Activity Detection ◽

Signal To Noise ◽

Noise Ratio ◽

Noise Robust ◽

Voice Activity

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Combining augmented statistical noise suppression and framewise speech/non-speech classification for robust voice activity detection

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2017.8 ◽

2017 ◽

Vol 6 ◽

Author(s):

Yasunari Obuchi

Keyword(s):

Noise Suppression ◽

Frequency Component ◽

Voice Activity Detection ◽

Training Data ◽

Activity Detection ◽

Noisy Environments ◽

Model Based ◽

Speech Classification ◽

Voice Activity ◽

Statistical Noise

This paper proposes a new voice activity detection (VAD) algorithm based on statistical noise suppression and framewise speech/non-speech classification. Although many VAD algorithms have been developed that are robust in noisy environments, the most successful ones are related to statistical noise suppression in some way. Accordingly, we formulate our VAD algorithm as a combination of noise suppression and subsequent framewise classification. The noise suppression part is improved by introducing the idea that any unreliable frequency component should be removed, and the decision can be made by the remaining signal. This augmentation can be realized using a few additional parameters embedded in the gain-estimation process. The framewise classification part can be either model-less or model-based. A model-less classifier has the advantage that it can be applied to any situation, even if no training data are available. In contrast, a model-based classifier (e.g., neural network-based classifier) requires training data but tends to be more accurate. The accuracy of the proposed algorithm is evaluated using the CENSREC-1-C public framework and confirmed to be superior to many existing algorithms.

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