Subband Based Spectrum Subtraction Algorithm

Abstract

This paper first proposes a classification algorithm which detects a voiced, unvoiced, and silence signal using distance measure, logarithm power and root mean square methods at each frame, then a spectrum subtraction algorithm based on a subband filter. The proposed algorithm subtracts spectrums of white noise and street noise from noisy signal based on the subband filter at each frame. In this experiment, experimental results of the proposed spectrum subtraction algorithm demonstrate using the speech and noise data of Aurora-2 database. Based on measuring the speech-to-noise ratio (SNR), experiments confirm that the proposed algorithm is effective for the speech by contaminated the noise. From the experiments, the improvement in the output SNR values was approximately 2.1 dB and 1.91 dB better for white noise and street noise, respectively.

Applying the Bi-level HMM for Robust Voice-activity Detection

“…The main purpose of a VAD system is the correct identification of the presence of either speech frames or the separate speech and non-speech patterns of a given signal. In the current context, VAD has been treated as a preprocessing technology for plentiful voice-related algorithms such as voice query, effective voice coding under a limited bandwidth, speaker recognition, and speech enhancement [1][2][3][4].…”