Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression

Echo cancellation and noise reduction are essential for full-duplex communication, yet most existing neural networks have high computational costs and are inflexible in tuning model complexity. In this paper, we introduce time-frequency dual-path compression to achieve a wide range of compression ratios on computational cost. Specifically, for frequency compression, trainable filters are used to replace manually designed filters for dimension reduction. For time compression, only using frame skipped prediction causes large performance degradation, which can be alleviated by a post-processing network with full sequence modeling. We have found that under fixed compression ratios, dual-path compression combining both the time and frequency methods will give further performance improvement, covering compression ratios from 4x to 32x with little model size change. Moreover, the proposed models show competitive performance compared with fast FullSubNet and DeepFilterNet. A demo page can be found at hangtingchen.github.io/ultra_dual_path_compression.github.io/.Comment: Accepted by Interspeech 202

Articulatory Copy Synthesis Based on the Speech Synthesizer VocalTractLab

Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process. The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values. The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features. The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton

Proceedings of the 5th international conference on disability, virtual reality and associated technologies (ICDVRAT 2004)

The proceedings of the conferenc

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Exponential families on resource-constrained systems

This work is about the estimation of exponential family models on resource-constrained systems. Our main goal is learning probabilistic models on devices with highly restricted storage, arithmetic, and computational capabilities—so called, ultra-low-power devices. Enhancing the learning capabilities of such devices opens up opportunities for intelligent ubiquitous systems in all areas of life, from medicine, over robotics, to home automation—to mention just a few. We investigate the inherent resource consumption of exponential families, review existing techniques, and devise new methods to reduce the resource consumption. The resource consumption, however, must not be reduced at all cost. Exponential families possess several desirable properties that must be preserved: Any probabilistic model encodes a conditional independence structure—our methods keep this structure intact. Exponential family models are theoretically well-founded. Instead of merely finding new algorithms based on intuition, our models are formalized within the framework of exponential families and derived from first principles. We do not introduce new assumptions which are incompatible with the formal derivation of the base model, and our methods do not rely on properties of particular high-level applications. To reduce the memory consumption, we combine and adapt reparametrization and regularization in an innovative way that facilitates the sparse parametrization of high-dimensional non-stationary time-series. The procedure allows us to load models in memory constrained systems, which would otherwise not fit. We provide new theoretical insights and prove that the uniform distance between the data generating process and our reparametrized solution is bounded. To reduce the arithmetic complexity of the learning problem, we derive the integer exponential family, based on the very definition of sufficient statistics and maximum entropy estimation. New integer-valued inference and learning algorithms are proposed, based on variational inference, proximal optimization, and regularization. The benefit of this technique is larger, the weaker the underlying system is, e.g., the probabilistic inference on a state-of-the-art ultra-lowpower microcontroller can be accelerated by a factor of 250. While our integer inference is fast, the underlying message passing relies on the variational principle, which is inexact and has unbounded error on general graphs. Since exact inference and other existing methods with bounded error exhibit exponential computational complexity, we employ near minimax optimal polynomial approximations to yield new stochastic algorithms for approximating the partition function and the marginal probabilities. Changing the polynomial degree allows us to control the complexity and the error of our new stochastic method. We provide an error bound that is parametrized by the number of samples, the polynomial degree, and the norm of the model’s parameter vector. Moreover, important intermediate quantities can be precomputed and shared with the weak computational device to reduce the resource requirement of our method even further. All new techniques are empirically evaluated on synthetic and real-world data, and the results confirm the properties which are predicted by our theoretical derivation. Our novel techniques allow a broader range of models to be learned on resource-constrained systems and imply several new research possibilities