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In this paper, we propose the regression-based packet loss concealment (PLC) for digital speech transmission by using deep neural networks (DNNs) with a multiple-layer deep architecture. For the DNN training, log-power spectra and phases are employed as features in the input layer for the large training set, which ensures non-linear mapping the frames from the last correctly received frame to the missing frame. Once the training is accomplished by the restricted Boltzmann machine (RBM)-based pre-training to initialize the DNN, minimum mean square error (MMSE)-based fine tuning is then performed based on the back-propagation algorithm. In the reconstruction stage, the trained DNN model is fed with the features of the previous frames in order to estimate the log-power spectra and phases of the missing frames. Reconstruction is further improved by using the cross-fading technique to mitigate discontinuity between the reconstruction signal and good frame signal in the time-domain. To demonstrate the performance of the proposed algorithm, hidden Markov model (HMM)-based PLC algorithm and the PLC algorithm standardized in adaptive multi-rate wideband (AMR-WB) Appendix I were used for comparison. The experimental results show that the proposed approach provides better speech quality and speech recognition accuracy than the conventional approaches.

Packet loss concealment based on deep neural networks for digital speech transmission

Structures and protocols are presented for signaling a status or decision (processing or transmitting a medical record or other resource, e.g.) conditionally. Such signaling may be partly based on one or more symptoms, regimen attributes, performance indicia (compliance indications, e.g.), privacy considerations (patient consent, e.g.), contextual considerations (being in or admitted by a care facility, e.g.), sensor data, or other such determinants. In some contexts this may trigger an incentive being manifested (as a dispensation of an item, e.g.), an intercommunication (telephone call, e.g.) beginning, a device being configured (enabled or customized, e.g.), data distillations being presented or tracked, or other such results.

Evidence-based healthcare information management protocols

A technique is described for concealing the effect of a lost frame in a series of frames representing an encoded audio signal in a sub-band predictive coding system. In accordance with the technique, a first synthesized sub-band audio signal is synthesized, wherein synthesizing the first synthesized sub-band audio signal comprises performing waveform extrapolation based on a stored first sub-band decoded audio signal. A second synthesized sub-band audio signal is also synthesized, wherein synthesizing the second synthesized sub-band audio signal comprises performing waveform extrapolation based on the stored second sub-band decoded audio signal. The first synthesized sub-band audio signal and the second synthesized sub-band audio signal are combined to generate a synthesized full-band output audio signal corresponding to a lost frame.

Packet loss concealment for sub-band predictive coding based on extrapolation of sub-band audio waveforms

A technique is described for reducing audible artifacts in an audio output signal generated by decoding a received frame in a series of frames. According to the technique, if the received frame is one of a predefined number of received frames following a lost frame, at least one parameter or signal associated with the decoding of the received frame is altered from a state associated with normal decoding. The received frame is then decoded in accordance with the at least one parameter or signal.

Constrained and controlled decoding after packet loss

This paper presents a candidate for the ITU-T G.722 packet loss concealment standard. The algorithm is based on waveform extrapolation in the speech domain. The strong backward adaptive nature of G.722 makes the state update during lost frames a challenge. The paper presents methods to update the G.722 subband decoder state memory during packet loss. Furthermore, novel techniques to facilitate smooth transition after packet loss are described. Formal subjective test results indicate that the algorithm far exceeds all requirements in the ITU-T terms of reference. Most techniques are applicable to G.726 as well, and some are easily extendable to other speech coders with memory.

A Candidate for the ITU-T G.722 Packet Loss Concealment Standard

Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted. Related apparatus, systems, techniques and articles are also described.

Method and system for predicting adherence to a treatment

The communication of voice over wireless local area networks (WLANs) is influenced by the choice of speech codec, packetization interval and PHY layer bit rates. These choices affect the number of voice users that can be supported on the WLAN as well as the speech quality experienced by each user. We investigate the effect of different combinations of these parameters for a 802.11a WLAN in different channel conditions with the objective of maximizing the number of voice users supported on the WLAN subject to a quality constraint. We use an indicator for assessing the speech quality experienced by a single user in a WLAN, based on a Perceptual Evaluation of Speech Quality (PESQ) Mean Opinion Score (MOS) constraint and the probability of a voice user achieving this constraint. The contributions of this paper are three-fold. First, a PHY layer rate adaptation scheme is proposed, in which the operating rate for each Signal to Noise Ratio (SNR) is chosen as the one that maximizes the capacity given a quality constraint. Second, based on the PHY layer rate adaptation scheme, we evaluate the effect of the choice of codec and voice payload size on the capacity values obtainable at different SNRs. Finally, we show the effect of channel conditions and the tightening of quality constraints on the capacity values at different SNRs.

/pdf/voice-capacity-under-quality-constraints-for-ieee-802-11a-2yodxth5kv.pdf

Voice capacity under quality constraints for IEEE 802.11a based WLANs

Since the G.722 wideband speech codec offers higher quality and naturalness than G.711, is low in complexity, has low delay, and tandems well with other codecs, it is an attractive codec for voice over IP and voice over wireless LANs. However, packet losses in G.722 not only require good concealment of the lost frame, but a lost frame results in a mismatch of the encoder/decoder states for the next correctly received frame following the lost frame. Although proprietary schemes exist, the G.722 codec has no standardized packet loss concealment (PLC) method. We present a new PLC method for G.722 and propose an efficient approach to sending the side information to resynchronize the encoder and decoder that greatly improves the robustness of the G.722 codec to packet losses

Improving the Robustness of the G.722 Wideband Speech Codec to Packet Losses for Voice Over Wlans

The G.722 wideband speech codec offers higher quality and better naturalness than G.711, is low in com- plexity, has low delay, and tandems well with other codecs. This makes it an attractive codec for Voice over IP and Voice over Wireless LANs. However, loss of a G.722 coded speech frame results in a mismatch of the encoder/decoder states that affects the decoding of subsequent correctly received frames. We present a new packet loss concealment (PLC) method for G.722 and propose an efficient approach to sending the side information to improve concealment of the lost frame and to resynchronize the encoder and decoder. The proposed method greatly improves the robustness of the G.722 codec to packet losses for Voice over Wireless LANs. Wideband Perceptual Evaluation of Speech Quality Mean Opinion Score (WB-PESQ MOS) is used as the quality metric for evaluating the performance of the PLC schemes.

/pdf/packet-loss-concealment-for-g-722-using-side-information-1rdvz164kj.pdf

Packet Loss Concealment for G.722 using Side Information with Application to Voice over Wireless LANs

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