Reverberation time (RT) is an important indicator of room acoustics, however, most studies focus on the mid-high frequency RT, and less on the low-frequency RT. In this paper, a hybrid approach based on geometric and wave methods was proposed to build a more accurate and wide frequency-band room acoustic impulse response. This hybrid method utilized the finite-difference time-domain (FDTD) method modeling at low frequencies and the Odeon simulation at mid-high frequencies, which was investigated in a university classroom. The influence of the low-frequency RT on speech intelligibility was explored. For the low-frequency part, different impedance boundary conditions were employed and the effectiveness of the hybrid method has also been verified. From the results of objective acoustical parameters and subjective listening experiments, the smaller the low-frequency RT was, the higher the Chinese speech intelligibility score was. The syllables, consonants, vowels, and the syllable order also had significant effects on the intelligibility score.

Snoring is a typical and intuitive symptom of the obstructive sleep apnea hypopnea syndrome (OSAHS), which is a kind of sleep-related respiratory disorder having adverse effects on people’s lives. Detecting snoring sounds from the whole night recorded sounds is the first but the most important step for the snoring analysis of OSAHS. An automatic snoring detection system based on the wavelet packet transform (WPT) with an eXtreme Gradient Boosting (XGBoost) classifier is proposed in the paper, which recognizes snoring sounds from the enhanced episodes by the generalization subspace noise reduction algorithm. The feature selection technology based on correlation analysis is applied to select the most discriminative WPT features. The selected features yield a high sensitivity of 97.27% and a precision of 96.48% on the test set. The recognition performance demonstrates that WPT is effective in the analysis of snoring and non-snoring sounds, and the difference is exhibited much more comprehensively by sub-bands with smaller frequency ranges. The distribution of snoring sound is mainly on the middle and low frequency parts, there is also evident difference between snoring and non-snoring sounds on the high frequency part.