Decimation of a discrete-time signal below the Nyquist rate without applying an appropriate lowpass filter results in a distortion called aliasing. If wideband speech sampled at 16 kHz is decimated by 2 to result in a signal sampled at 8 kHz with aliasing, the decimated signal would be the summation of two speech-like signals, which are the narrowband speech covering 0-4 kHz and the spectrally flipped aliasing component coming from 8-4 kHz. Recently, the performance of speech separation has been remarkably improved with deep learning-based approaches, implying that the narrowband and aliasing components may be able to be separated. In this letter, we propose a novel method for low-rate wideband speech coding utilizing a standard narrowband codec. Instead of coding wideband speech using a wideband codec with a limited bitrate, we propose to decimate the input wideband speech incurring aliasing, and then encode it with a narrowband codec by allocating all the allowed bitrate to 0-4 kHz. After decoding the encoded bitstream, we apply a speech separation technique to obtain the narrowband and aliasing signals, which are then used to reconstruct the wideband speech by expansion, low/highpass filtering, and summation. Experimental results showed that the proposed method could achieve subjective quality comparable to the speeches coded by wideband codecs at higher bitrates in a subjective MUSHRA test.

The interchannel phase difference (IPD) may be one of the most widely-used spatial cues in multichannel speech processing, and has been used in beamformers and post filters for speech enhancement. The coherence, which is also used as a feature for speech enhancement, can provide information on the reliability of the IPD for the estimation of the speech presence probability (SPP). In this paper, we propose dual microphone speech enhancement adoptinga posterioriSPP estimation based on statistical modeling of the IPD. The marginal distribution of the IPD is derived from the distribution of the relative transfer function which is parameterized with the IPD and coherence, with a single assumption that the observed discrete Fourier transform (DFT) coefficients in each frequency are distributed according to a complex bivariate Gaussian distribution. Given the direction of arrival of the desired signal, thea posterioriSPP is obtained using the IPD distributions with and without the information on the location of the interfering source, and is applied to speech enhancement. Experimental results for various types and locations of noise, signal-to-noise ratios, reverberation times, and locations of the target source showed that the proposed method outperformed previously proposed approaches utilizing IPD information.

The two most important spatial cues in human auditory system may be the interaural time difference and the interaural level difference. There have been many attempts to utilize the time difference of arrival (TDoA) and level difference between two microphone signals for voice activity detection (VAD). In this letter, we propose a dual microphone VAD algorithm based on a support vector machine for which the input vector consists of both TDoA-based and level difference-based features. Several candidates for the feature combination have been compared using various TDoA-related and level difference-related features. Experimental results showed that the proposed VAD algorithm outperformed a standardized single microphone VAD, VADs based on the TDoA or level difference, and logical combination of them in various noise environments.