Two main spatial cues that can be exploited for dual microphone voice activity detection (VAD) are the interchannel time difference (ITD) and the interchannel level difference (ILD). While both ITD and ILD provide information on the location of audio sources, they may be impaired in different manners by background noises and reverberation and therefore can have complementary information. Conventional approaches utilize the statistics from all frequencies with fixed weight, although the information from some time–frequency bins may degrade the performance of VAD. In this letter, we propose a dual microphone VAD scheme based on the spatial cues in reliable frequency bins only, considering the sparsity of the speech signal in the time–frequency domain. The reliability of each time–frequency bin is determined by three conditions on signal energy, ILD, and ITD. ITD-based and ILD-based VADs and statistics are evaluated using the information from selected frequency bins and then combined to produce the final VAD results. Experimental results show that the proposed frequency selective approach enhances the performances of VAD in realistic environments.
Audio codecs generate notable artifacts when operating at low bitrates, which degrade the quality of the coded audio significantly. There have been several approaches to enhance the quality of decoded signals with and without side information. While pre-or post-processing approaches without side information can be applied directly to existing systems without modifying codecs, approaches utilizing side information can further enhance the performance while maintaining backward-compatibility with existing codecs. In this paper, we propose a method to improve decoded signals using neural network-based side information. A neural network in the transmitter side that generates the side information and another neural network in the receiver side that estimates the log power spectra (LPS) of the original signal from the decoded signal and the side information are jointly trained to accurately reconstruct the original signal. In the same line with the analysis-by-synthesis, the neural network that generates the side information in the transmitter side takes not only the LPS of the original signal but also the LPS of the decoded signal as the input by decoding the encoded bitstream at the transmitter side. Experimental results show that the proposed audio codec enhancement scheme using neural network-based side information outperformed the audio codec enhancement without side information for the same codec operating at higher bitrates.
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