This paper proposes a speech enhancement method using the multi-scales and multi-thresholds of the auditory perception wavelet transform, which is suitable for a low SNR (signal to noise ratio) environment. This method achieves the goal of noise reduction according to the threshold processing of the human ear's auditory masking effect on the auditory perception wavelet transform parameters of a speech signal. At the same time, in order to prevent high frequency loss during the process of noise suppression, we first make a voicing decision based on the speech signals. Afterwards, we process the unvoiced sound segment and the voiced sound segment according to the different thresholds and different judgments. Lastly, we perform objective and subjective tests on the enhanced speech. The results show that, compared to other spectral subtractions, our method keeps the components of unvoiced sound intact, while it suppresses the residual noise and the background noise. Thus, the enhanced speech has better clarity and intelligibility.
This paper provides an overview of the effects of timing jitter in audio sampling analog-to-digital converters (ADCs), i.e. PCM (conventional or Nyquist sampling) ADCs and sigma-delta (ΣΔ) ADCs. Jitter in a digital audio is often defined as short-term fluctuations of the sampling instants of a digital signal from their ideal positions in time. The influence of the jitter increases particularly with the improvements in both resolution and sampling rate of today's audio ADCs. At higher frequencies of the input signals the sampling jitter becomes a dominant factor in limiting the ADCs performance in terms of signal-to-noise ratio (SNR) and dynamic range (DR).