3,936 research outputs found

    Time and frequency domain algorithms for speech coding

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    The promise of digital hardware economies (due to recent advances in VLSI technology), has focussed much attention on more complex and sophisticated speech coding algorithms which offer improved quality at relatively low bit rates. This thesis describes the results (obtained from computer simulations) of research into various efficient (time and frequency domain) speech encoders operating at a transmission bit rate of 16 Kbps. In the time domain, Adaptive Differential Pulse Code Modulation (ADPCM) systems employing both forward and backward adaptive prediction were examined. A number of algorithms were proposed and evaluated, including several variants of the Stochastic Approximation Predictor (SAP). A Backward Block Adaptive (BBA) predictor was also developed and found to outperform the conventional stochastic methods, even though its complexity in terms of signal processing requirements is lower. A simplified Adaptive Predictive Coder (APC) employing a single tap pitch predictor considered next provided a slight improvement in performance over ADPCM, but with rather greater complexity. The ultimate test of any speech coding system is the perceptual performance of the received speech. Recent research has indicated that this may be enhanced by suitable control of the noise spectrum according to the theory of auditory masking. Various noise shaping ADPCM configurations were examined, and it was demonstrated that a proposed pre-/post-filtering arrangement which exploits advantageously the predictor-quantizer interaction, leads to the best subjective performance in both forward and backward prediction systems. Adaptive quantization is instrumental to the performance of ADPCM systems. Both the forward adaptive quantizer (AQF) and the backward oneword memory adaptation (AQJ) were examined. In addition, a novel method of decreasing quantization noise in ADPCM-AQJ coders, which involves the application of correction to the decoded speech samples, provided reduced output noise across the spectrum, with considerable high frequency noise suppression. More powerful (and inevitably more complex) frequency domain speech coders such as the Adaptive Transform Coder (ATC) and the Sub-band Coder (SBC) offer good quality speech at 16 Kbps. To reduce complexity and coding delay, whilst retaining the advantage of sub-band coding, a novel transform based split-band coder (TSBC) was developed and found to compare closely in performance with the SBC. To prevent the heavy side information requirement associated with a large number of bands in split-band coding schemes from impairing coding accuracy, without forgoing the efficiency provided by adaptive bit allocation, a method employing AQJs to code the sub-band signals together with vector quantization of the bit allocation patterns was also proposed. Finally, 'pipeline' methods of bit allocation and step size estimation (using the Fast Fourier Transform (FFT) on the input signal) were examined. Such methods, although less accurate, are nevertheless useful in limiting coding delay associated with SRC schemes employing Quadrature Mirror Filters (QMF)

    Speech coding at medium bit rates using analysis by synthesis techniques

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    Speech coding at medium bit rates using analysis by synthesis technique

    Training a Spiking Neural Network with Equilibrium Propagation

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    Backpropagation is almost universally used to train artificial neural networks. However, there are several reasons that backpropagation could not be plausibly implemented by biological neurons. Among these are the facts that (1) biological neurons appear to lack any mechanism for sending gradients backwards across synapses, and (2) biological “spiking” neurons emit binary signals, whereas back-propagation requires that neurons communicate continuous values between one another. Recently, Scellier and Bengio [2017], demonstrated an alternative to backpropagation, called Equilibrium Propagation, wherein gradients are implicitly computed by the dynamics of the neural network, so that neurons do not need an internal mechanism for backpropagation of gradients. This provides an interesting solution to problem (1). In this paper, we address problem (2) by proposing a way in which Equilibrium Propagation can be implemented with neurons which are constrained to just communicate binary values at each time step. We show that with appropriate step-size annealing, we can converge to the same fixed-point as a real-valued neural network, and that with predictive coding, we can make this convergence much faster. We demonstrate that the resulting model can be used to train a spiking neural network using the update scheme from Equilibrium propagation

    Perceptual models in speech quality assessment and coding

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    The ever-increasing demand for good communications/toll quality speech has created a renewed interest into the perceptual impact of rate compression. Two general areas are investigated in this work, namely speech quality assessment and speech coding. In the field of speech quality assessment, a model is developed which simulates the processing stages of the peripheral auditory system. At the output of the model a "running" auditory spectrum is obtained. This represents the auditory (spectral) equivalent of any acoustic sound such as speech. Auditory spectra from coded speech segments serve as inputs to a second model. This model simulates the information centre in the brain which performs the speech quality assessment. [Continues.
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