Volodya Grancharov

Noise-dependent postfiltering

The paper introduces a modification of the commonly used postfilter that improves performance when acoustic background noise is present. The modification consists of replacing the nonadaptive postfilter parameters that govern the degree of spectral emphasis (commonly denoted as /spl gamma//sub 1/ and /spl gamma//sub 2/) with parameters that adapt to the noise statistics. We describe an effective mapping from the noise statistics to the emphasis parameters and provide a low complexity noise estimation algorithm that is sufficient for this application. The resulting noise-adaptive postfilter successfully attenuates the background noise and naturally converges to the conventional postfilter at high SNR conditions. Thus, the speech enhancement problem is solved with minimal modification of legacy codecs, since the existing structure of the speech codec is used. Test results indicate that the presented algorithm significantly outperforms the standard postfilter with non-adaptive parameters.

Low-Complexity, Nonintrusive Speech Quality Assessment

Monitoring of speech quality in emerging heterogeneous networks is of great interest to network operators. The most efficient way to satisfy such a need is through nonintrusive, objective speech quality assessment. In this paper, we describe a low-complexity algorithm for monitoring the speech quality over a network. The features used in the proposed algorithm can be computed from commonly used speech-coding parameters. Reconstruction and perceptual transformation of the signal is not performed. The critical advantage of the approach lies in generating quality assessment ratings without explicit distortion modeling. The results from the performed experiments indicate that the proposed nonintrusive objective quality measure performs better than the ITU-T P.563 standard

On causal algorithms for speech enhancement

Kalman filtering is a powerful technique for the estimation of a signal observed in noise that can be used to enhance speech observed in the presence of acoustic background noise. In a speech communication system, the speech signal is typically buffered for a period of 10-40 ms and, therefore, the use of either a causal or a noncausal filter is possible. We show that the causal Kalman algorithm is in conflict with the basic properties of human perception and address the problem of improving its perceptual quality. We discuss two approaches to improve perceptual performance. The first is based on a new method that combines the causal Kalman algorithm with pre- and postfiltering to introduce perceptual shaping of the residual noise. The second is based on the conventional Kalman smoother. We show that a short lag removes the conflict resulting from the causality constraint and we quantify the minimum lag required for this purpose. The results of our objective and subjective evaluations confirm that both approaches significantly outperform the conventional causal implementation. Of the two approaches, the Kalman smoother performs better if the signal statistics are precisely known, if this is not the case the perceptually weighted Kalman filter performs better.

Speech Quality Assessment

In this chapter, we provide an overview of methods for speech quality assessment. First, we define the term speech quality and outline in Sect. 5.1 the main causes of degradation of speech quality. Then, we discuss subjective test methods for quality assessment, with a focus on standardized methods. Section 5.3 is dedicated to objective algorithms for quality assessment. We conclude the chapter with a reference table containing common quality assessment scenarios and the corresponding most suitable methods for quality assessment.

Generalized Postfilter for Speech Quality Enhancement

Postfilters are commonly used in speech coding for the attenuation of quantization noise. In the presence of acoustic background noise or distortion due to tandeming operations, the postfilter parameters are not adjusted and the performance is, therefore, not optimal. We propose a modification that consists of replacing the nonadaptive postfilter parameters with parameters that adapt to variations in spectral flatness, obtained from the noisy speech. This generalization of the postfiltering concept can handle a larger range of noise conditions, but has the same computational complexity and memory requirements as the conventional postfilter. Test results indicate that the presented algorithm improves on the standard postfilter, as well as on the combination of a noise attenuation preprocessor and the conventional postfilter.

Super-wideband bandwidth extension for speech in the 3GPP EVS codec

This paper describes the time-domain bandwidth extension (TBE) framework employed to code wideband and super-wideband speech in the newly standardized 3GPP EVS codec. The TBE algorithm uses a nonlinear harmonic modeling technique that incorporates principles of time-domain envelope-modulated noise mixing. At 13.2 kbps, the super-wideband coding of speech uses as low as 1.55 kbps for encoding the spectral content from 6.4-14.4 kHz. Subjective evaluation results from ITU-T P.800 Mean Opinion Score (MOS) tests are provided, showing significantly improved quality compared to the other standardized SWB codecs under both clean speech and speech with background noise.

MDCT audio coding with pulse vector quantizers

This paper describes a novel audio coding algorithm that is a building block in the recently standardized 3GPP EVS codec [1]. The presented scheme operates in the Modified Discrete Cosine Transform (MDCT) domain and deploys a Split-PVQ pulse coding quantizer, a noise-fill, and a gain control optimized for the quantizers properties. A complexity analysis in terms of WMOPS is presented to illustrate that the proposed Split-PVQ concept and dynamic range optimized MPVQ-indexing are suitable for real-time audio coding. Test results from formal MOS subjective evaluations and objective performance figures are presented to illustrate the competitiveness of the proposed algorithm.

Double-Ended Quality Assessment System for Super-Wideband Speech

This paper describes a double-ended quality assessment system for speech with a bandwidth of up to 14 kHz (so-called super-wideband speech). The quality assessment system is based on a combination of local and global features, where the local features are dependent on a time alignment procedure and the global features are not. The system is evaluated over a large set of subjectively scored narrowband, wideband and super-wideband speech databases. The system performs similarly to PESQ for narrowband speech and significantly better for wideband speech.

Harmonic Vector Quantization

Audio coding of harmonic signals is a challenging task for conventional MDCT coding schemes. In this paper we introduce a novel algorithm for improved transform coding of harmonic audio. The algorithm does not deploy the conventional scheme of splitting the input signal into a spectrum envelope and a residual, but models the spectral peak regions. The presented coding scheme is part of the recently standardized 3GPP EVS codec.