The signal subspace approach for non-parametric speech enhancement is considered. Several algorithms have been proposed in the literature but only partly analyzed. Here, the different algorithms are compared, and the emphasis is put onto the limiting factors and practical behavior of the estimators. Experimental results show that the signal subspace approach may lead to a significant enhancement of the signal to noise ratio of the output signal.

The initial design considerations and research goals for an ATM network based virtual seminar room with five sites are presented. The basic observation behind the design of the virtual seminar room is, that besides the constant growth in available bandwidth for transmission in communication networks, many networks either already offer or are developing technologies to give quality of service (QoS) guarantees. This means that applications not only will support transmission of coded audio and video, but can be designed in a known and well-controlled network environment, which enables the applications to provide, in a broad sense, a high and reliable quality presented at the human computer interface.

Digital telecommunication networks may involve a multiple number of public switched telephone networks (PSTN), cellular and mobile systems and to some extent also satellite systems. Most of these networks contain non-linear speech coders and other speech algorithms which may degrade the overall end-to-end quality of speech. An important problem is how to assess the speech quality of such compounded systems. The object of this paper is to describe the first stage of the construction of a proposed three-layer model for speech quality assessment. A subjective test of the speech quality of 16 different compounded transmission paths (mixtures of PCM, GSM full and half rate, DECT, CELP, LD CELP, FS10-16) is carried out by 40 subjects using 21 different rating scales. The main result of this paper is the test results which lead to the definition of four main perceptual dimensions to be used in the second layer of the proposed model.

Digital telecommunication networks involve multiple number of public switched telephone networks (PSTN), cellular and mobile systems and to some extent also satellite systems. Most of these networks contain non-linear speech coders which may degrade the overall end-to-end quality of speech. An important problem is how to assess the speech quality of such compounded systems. The object of this paper is to describe the development of a proposed three-layer model for objective speech quality assessment. The design of the objective measure is based on a subjective test of the speech quality of 16 compounded transmission paths and involves 40 subjects using 21 different rating scales. The subjective test supports the definition of four main perceptual dimensions to be used in the second layer of the proposed model. These defined perceptual elements form the basis of designing an objective measure for predicting the total quality of a given telecommunication connection.

Discusses how to exploit the nonlinearities in speech with the main purpose of improving the prediction in speech coders. If non-linearities are absent from speech the linear technique is sufficient, but if non-linearities are present the technique is inadequate and more sophisticated predictors are called for. Thyssen et al. (1994) gave evidence for non-linearities in speech and presented two non-linear short-term predictors that both were superior to the linear predictor without quantization. The present authors give methods to design vector quantizers for the non-linear predictors and investigate how vector quantization of the non-linear predictors affects prediction. Furthermore, they compare the performance of the quantized non-linear predictors to the performance of traditional quantized linear predictors. The experiments show that 10-bit VQ of the non-linear predictor leads to similar performance as 20-bit state-of-the-art split VQ of the LSP-parameters.

We consider an algorithm for reduction of broadband noise in speech based on signal subspaces. The algorithm is formulated by means of the quotient singular value decomposition (QSVD). With this formulation, a prewhitening operation becomes an integral part of the algorithm. We demonstrate that this is essential in connection with updating issues in real-time recursive applications. We also illustrate by examples that we are able to achieve a satisfactory quality of the reconstructed signal.

Addresses the question of how to extract the nonlinearities in speech with the prime purpose of facilitating coding of the residual signal in residual excited coders. The short-term prediction of speech in speech coders is extensively based on linear models, e.g. the linear predictive coding technique (LPC), which is one of the most basic elements in modern speech coders. This technique does not allow extraction of nonlinear dependencies. If nonlinearities are absent from speech the technique is sufficient, but if the speech contains nonlinearities the technique is inadequate. The authors give evidence for nonlinearities in speech and propose nonlinear short-term predictors that can substitute the LPC technique. The technique, called nonlinear predictive coding, is shown to be superior to the LPC technique. Two different nonlinear predictors are presented. The first is based on a second-order Volterra filter, and the second is based on a time delay neural network. The latter is shown to be the more suitable for speech coding applications.>

An algorithm for 2.4 kb/s speech coding is described. The main problem addressed is the coding of voiced speech. A way of coding the pitch structure is introduced. Compared with traditional coding schemes, it results in a better compromise between bit allocation for short-term quantization and residual coding. The coder uses vector quantization of the short-term parameters (line spectrum frequencies). The residual is lowpass filtered to obtain the baseband signal. Unvoiced frames are coded by means of a method based on repetition and interpolation of pitch pulses. The method exploits the high correlation between pitch pulses. Harmonic postfiltering is applied to obtain an improved high-frequency regeneration.>

An algorithm for coding speech at 2.4 kbps is presented. By combining in a new way well-known coding strategies from both medium and low bit rate speech coding, a very promising configuration is developed. The coder is fundamentally a baseband coder where short-term correlation is predicted by LPC (linear predictive coding) analysis. Coding of the short-term parameters is performed by vector quantization. An open-loop long-term predictor is applied to the lowpass filtered short-term residual to reduce the quasi-periodic pitch structure before the signal is down-sampled. A novel method for coding the down-sampled residual based on voiced/unvoiced classification is proposed. For unvoiced frames a simple white Gaussian codebook is applied, and for voiced frames a new pulse codebook has been designed. The coding algorithm is demonstrated to be robust and to produce high speech quality at 2.4 kbps.>