Techniques for the Regeneration of Wideband Speech from Narrowband Speech

This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system’s ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals

On Predictive Coding for Erasure Channels Using a Kalman Framework

We present a new design method for robust low-delay coding of autoregressive (AR) sources for transmission across erasure channels. It is a fundamental rethinking of existing concepts. It considers the encoder a mechanism that produces signal measurements from which the decoder estimates the original signal. The method is based on linear predictive coding and Kalman estimation at the decoder. We employ a novel encoder state-space representation with a linear quantization noise model. The encoder is represented by the Kalman measurement at the decoder. The presented method designs the encoder and decoder offline through an iterative algorithm based on closed-form minimization of the trace of the decoder state error covariance. The design method is shown to provide considerable performance gains, when the transmitted quantized prediction errors are subject to loss, in terms of signal-to-noise ratio (SNR) compared to the same coding framework optimized for no loss. The design method applies to stationary auto-regressive sources of any order. We demonstrate the method in a framework based on a generalized differential pulse code modulation (DPCM) encoder. The presented principles can be applied to more complicated coding systems that incorporate predictive coding as well

Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink

We consider the downlink of a massive multiuser (MU) multiple-input multiple-output (MIMO) system in which the base station (BS) is equipped with low-resolution digital-to-analog converters (DACs). In contrast to most existing results, we assume that the system operates over a frequency-selective wideband channel and uses orthogonal frequency division multiplexing (OFDM) to simplify equalization at the user equipments (UEs). Furthermore, we consider the practically relevant case of oversampling DACs. We theoretically analyze the uncoded bit error rate (BER) performance with linear precoders (e.g., zero forcing) and quadrature phase-shift keying using Bussgang's theorem. We also develop a lower bound on the information-theoretic sum-rate throughput achievable with Gaussian inputs, which can be evaluated in closed form for the case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet accurate, expressions for the distortion caused by low-precision DACs, which can be used to establish lower bounds on the corresponding sum-rate throughput. Our results demonstrate that, for a massive MU-MIMO-OFDM system with a 128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an uncoded BER of 10^-4 with a negligible performance loss compared to the infinite-resolution case at the cost of additional out-of-band emissions. Furthermore, our results highlight the importance of taking into account the inherent spatial and temporal correlations caused by low-precision DACs

QUANTIZATION NOISE ESTIMATION FOR LOG-PCM

ITU-T G.711.1 is a multirate wideband extension for the wellknown ITU-T G.711 pulse code modulation of voice frequencies. The extended system is fully interoperable with the legacy narrowband one. In the case where the legacy G.711 is used to code a speech signal and G.711.1 is used to decode it, quantization noise may be audible. For this situation, the standard proposes an optional postfilter. The application of postfiltering requires an estimation of the quatization noise. In this paper we review the process of estimating this coding noise and we propose a better noise estimator. Index Terms — Postfilter, quantization noise 1