34 accepted papers
Catastrophic forgetting is when a neural network loses previously learnt information after learning a new task sequentially. Avoiding catastrophic forgetting could reduce the resources necessary to update neural networks. Recently, Kolmogorov–Arnold Networks (KAN) gained the community's attention as…
In this work we present the BDIA-transformer, which is an exact bit-level reversible transformer that uses an unchanged standard architecture for inference. The basic idea is to first treat each transformer block as the Euler integration approximation for solving an ordinary differential equation (O…
For communication-efficient decentralized learning, it is essential to employ dynamic graphs designed to improve the expected spectral gap by reducing deviations from global averaging. The $1$-peer exponential graph demonstrates its finite-time convergence property--achieved by maximizing the expect…
One of the most effective online dereverberation algorithms is the weighted prediction error (WPE) method and its improved version, switching WPE (SwWPE). This paper proposes a practical online dereverberation approach to improve SwWPE by introducing a datareuse technique, DR-SwWPE; we then show ana…
Text-guided diffusion models such as DALLE-2, Imagen, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content. In many cases the images are of very high quality. However, these models often struggle to com…
"Recently, various methods have been proposed to address the inconsistency issue of DDIM inversion to enable image editing, such as EDICT [?] and Null-text inversion [?]. However, the above methods introduce considerable computational overhead. In this paper, we propose a new technique, named bidire…
A popular approach to sample a diffusion-based generative model is to solve an ordinary differential equation (ODE). In existing samplers, the coefficients of the ODE solvers are pre-determined by the ODE formulation, the reverse discrete timesteps, and the employed ODE methods. In this paper, we co…
We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec trains a Transformer language…
We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward proces…
We present a multi-channel audio signal generation scheme based on machine-learning and probabilistic modeling. We start from modeling a multi-channel single-source signal. Such signals are naturally modeled as a single-channel reference signal and a spatial-arrangement (SA) model specified by an SA…
Fixed beamforming based on uniform linear microphone arrays often suffers from non-optimal performance for broadband signals. This paper addresses the issue by jointly optimizing the array geometry and spatial filters through a neural network based model. The model, composed of two feed forward neur…
Active control of noise propagating through apertures is commonly realized with closed-loop LMS algorithms. However, these algorithms require a large number of error microphones and provide only local attenuation. Slow convergence and high computational effort are additional disadvantages. We propos…
A novel asynchronous decentralized optimization method that follows Stochastic Variance Reduction (SVR) is proposed. Average consensus algorithms, such as Decentralized Stochastic Gradient Descent (DSGD), facilitate distributed training of machine learning models. However, the gradient will drift wi…
The recent emergence of machine-learning based generative models for speech suggests a significant reduction in bit rate for speech codecs is possible. However, the performance of generative models deteriorates significantly with the distortions present in real-world input signals. We argue that thi…
Detection of statistical dependence between random variables is an essential component in many machine learning algorithms. We propose a novel independence criterion for two random variables with linear-time complexity. We establish that our independence criterion is an upper bound of the Hirschfeld…
Generalization of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new technique, named projected weight regularization (PWR), to improve the generalization capacity of a DNN model. Consider a weight…
Rapid advances in machine-learning based generative modeling of speech make its use in speech coding attractive. However, the current performance of such models drops rapidly with noise contamination of the input, preventing use in practical applications. We present a new speech-coding scheme that i…
In this paper, we compare the performance of two active dereverberation techniques using a planar array of microphones and loudspeakers. The two techniques are based on a solution to the Kirchhoff-Helmholtz Integral Equation (KHIE). We adapt a Wave Field Synthesis (WFS) based method to the applicati…
In this work, we propose to train a deep neural network by distributed optimization over a graph. Two nonlinear functions are considered: the rectified linear unit (ReLU) and a linear unit with both lower and upper cutoffs (DCutLU). The problem reformulation over a graph is realized by explicitly re…
Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operatin…
In this paper, we investigate the effects of compensating for wave-domain filtering delay in an active speech control system. An active control system utilising wave-domain processed basis functions is evaluated for a linear array of dipole secondary sources. The target control soundfield is matched…
Distributed image fusion over networks has had little coverage in the literature, particularly considering the recent emergence of large networks of imaging sensors such as radio telescope arrays and wireless self-contained node networks. We present a fully asynchronous and distributed approach for…
We present a method that improves the objective quality estimation of a speech utterance. We show that including raw features that are presumably redundant reduces the effect of input noise and improves the performance of linear regressors. To exploit this effect we propose the novel idea to augment…
In this paper we present a non-iterative impulse response shortening method aiming to reduce the latency of a system. Our method exploits that smoothing the frequency-domain response generally leads to a shorter time-domain response. The method is simple to implement and has a computational complexi…
The key to the success of speech-based technology is an understanding of human speech communication. While significant advances have been made, a unified theory of speech communication that is both comprehensive and quantitative is yet to emerge. In this paper we approach speech communication from a…
In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks,…
Broad areal coverage and low cost make wireless sensor networks natural platforms for blind source separation (BSS). In this context, distributed processing is attractive because of low power requirements and scalability. However, existing distributed BSS algorithms either require a fully connected…
Until now, distributed acoustic beamforming has focused on optimizing for a beamformer over an entire network, with each node contributing to the beamformer output. We present a novel approach that introduces sparsity to this beamformer computation, where we attempt to optimize for a subset of nodes…
Existing post-filtering techniques for microphone array speech enhancement have two common deficiencies. First, they assume that the noise is either white or diffuse and cannot deal with point inter-ferers. Second, they estimate the post-filter coefficients using only two microphones at a time and t…
This paper proposes two methods for providing speech privacy between spatial zones in anechoic and reverberant environments. The methods are based on masking the content leaked between regions. The masking is optimised to maximise the speech intelligibility contrast (SIC) between the zones. The firs…
The processing required for the global maximization of the intelligibility of speech acquired by multiple microphones and rendered by a single loudspeaker, is considered in this paper. The intelligibility is quantized, based on the mutual information rate between the message spoken by the talker and…
In this paper, a broadband region-based near-field beamforming algorithm is proposed and demonstrated for acoustic applications. We use an eigenfilter structure with a minimum-energy cost function based on desired and undesired near-field regions. Robustness is thus achieved by focusing on signals g…
The problem of domain generalization is to take knowledge acquired from a number of related domains, where training data is available, and to then successfully apply it to previously unseen domains. We propose a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good genera…
We propose a sparse hidden Markov model (HMM)-based single-channel speech enhancement method that models the speech and noise gains accurately in both stationary and nonstationary environments. The objective function is augmented with an lp regularization term resulting in a sparse autoregressive HM…