New Channel-Aware Modes for VoIP in 3GPP EVS Codec

Aiming at improving rate flexibility of the enhanced voice services (EVS) channel-aware mode for various VoIP applications, two new bit-rate channel-aware modes are proposed in this paper in addition to the existing 13.2 kbit/s mode. Channel-aware mode uses forward error correction by transmitting re-encoded information redundantly for use when the original information is lost or discarded due to late arrival to the receiver. The primary frame bit rate is reduced for the redundant accommodation. A modified quantization scheme is proposed for core encoding regarding the quality degradation. Partial redundant coding is a simplification of that in the existing 13.2 kbit/s channel-aware mode due to the bit constraint. The objective evaluation results of PESQ show that the additional channel-aware modes achieve similar performance in improving the error robustness against missing packets as that of the existing 13.2 kbit/s mode. Multiple bit-rate modes can be dynamically selected in the communication system for more voice services in different bandwidths. On the other hand, optimal allocation based on real-time feedback can adapt to the rapidly-changing network environment as well as possible.

Exploiting Sparse Representation in the P300 Speller Paradigm

A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SNR). In this paper, a novel method is proposed to cope with this problem through sparse representation for the P300 speller paradigm. This work is distinguished using two key contributions. First, we investigate sparse coding and its feasibility for brain signal classification. Training signals are used to learn the dictionaries and test signals are classified according to their sparse representation and reconstruction errors. Second, sample selection and a channel-aware dictionary are proposed to reduce the effect of noise, which can improve performance and enhance the computing efficiency simultaneously. A novel classification method from the sample set perspective is proposed to exploit channel correlations. Specifically, the brain signal of each channel is classified jointly using its spatially neighboring channels and a novel weighted regulation strategy is proposed to overcome outliers in the group. Experimental results have demonstrated that our methods are highly effective. We achieve a state-of-the-art recognition rate of 72.5%, 88.5%, and 98.5% at 5, 10, and 15 epochs, respectively, on BCI Competition Ⅲ Dataset Ⅱ.