A common issue in coding speech and audio in the frequency domain, which appears with decreasing bitrate, is that quantization levels become increasingly sparse. With low accuracy, high-frequency components are typically quantized to zero, which leads to a muffled output signal and musical noise. Band-width extension and noise-filling methods attempt to treat the problem by inserting noise of similar energy as the original signal, at the cost of low signal to noise ratio. Dithering methods however provide an alternative approach, where both accuracy and energy are retained. We propose a hybrid coding approach where low-energy samples are quantized using dithering, instead of the conventional uniform quantizer. For dithering, we apply 1 bit quantization in a randomized sub-space. We further show that the output energy can be adjusted to the desired level using a scaling parameter. Objective measurements and listening tests demonstrate the advantages of the proposed methods.

Efficient coding of speech and audio in a distributed system requires that quantization errors across nodes are uncorrelated. Yet, with conventional methods at low bitrates, quantization levels become increasingly sparse, which does not correspond to the distribution of the input signal and, importantly, also reduces coding efficiency in a distributed system. We have recently proposed a distributed speech and audio codec design, which applies quantization in a randomized domain such that quantization errors are randomly rotated in the output domain. Similar to dithering, this ensures that quantization errors across nodes are uncorrelated and coding efficiency is retained. In this paper, we improve this approach by proposing faster randomization methods, with a computational complexity of O(N log N). The presented experiments demonstrate that the proposed randomizations yield uncorrelated signals, that perceptual quality is competitive, and that the complexity of the proposed methods is feasible for practical applications.

S.1730-1734

Main-stream speech codecs are based on modelling the speech source by a linear predictor. An efficient domain for quantization and coding of this linear predictor is the line spectral frequency representation, where the predictor is encoded into an ordered set of frequencies that correspond to the roots of the corresponding line spectral polynomials. While this representation is robust in terms of quantization, methods available for finding the line spectral frequencies are computationally complex. In this work, we present a method for finding these frequencies using the FFT, including methods for limiting numerical range in fixed-point implementations. Our experiments show that, in comparison to a zero-crossing search in the Chebyshev domain, the proposed method reduces complexity and improves robustness, while retaining accuracy.

In speech communication systems the received microphone signals are commonly degraded by reverberation and ambient noise that can decrease the fidelity and intelligibility of a desired speaker. Reverberation can be modeled as non-stationary diffuse sound which is not directly observable. In this work, we derive a multichannel Wiener filter in the spherical harmonic domain to reduce both reverberation and noise. The filter depends on the direction-of-arrival of the direct sound of the desired speaker and an interference power spectral density matrix for which an estimator is developed. The resulting informed spatial filter incorporates instantaneous information about the diffuseness of the sound field into the design of the filter. In addition, it is shown how the proposed filter relates to the well-known robust minimum variance distortionless response filter that is also used for comparison in the evaluation. Experimental results show that the proposed spatial filter provides a tradeoff between noise reduction and dereverberation depending on the diffuse sound PSD.