An algebraic principle for blind source separation is presented in this paper. This separation principle identi"es a (smaller) set of equations whose solutions can blindly extract non-Gaussian signals. The concept of `Mth-order uncorrelatednessa is introduced and it is proven that for Mth-order uncorrelated source signals, signals with nonzero kth-order cumulant (2(k)M) can always be extracted ...

Autoregressive models have achieved impressive results over a wide range of domains in terms generation quality and downstream task performance. In the continuous domain, key factor behind this success is usage quantized latent spaces (e.g., obtained via VQ-VAE autoencoders), which allow for dimensionality reduction faster inference times. However, using existing pre-trained to perform new non-...

blind source separation technique separates mixed signals blindly without any information on the mixing system. in this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. in these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. in order to evalu...

In this paper, we describe a method for recognizing sound sources in a mixture. While many audio-based content analysis methods focus on detecting or classifying target sounds in a discriminative manner, we approach this as a regression problem, in which we estimate the relative proportions of sound sources in the given mixture. Using certain source separation ideas, we directly estimate these ...

This paper deals with the general problem of separating two independent wideband sources when they are mixed by unknown filters. In order to solve this problem. a backward framework is proposed which is composed of two different stages. The fiist one consists of two linear predictors devoted to improve the source separation, whitening the input signals. Their coefficients are calculated applyin...