Speech self-supervised learning (SSL) represents has achieved state-of-the-art (SOTA) performance in multiple downstream tasks. However, its application in speech enhancement (SE) tasks remains immature, offering opportunities for improvement. In this study, we introduce a novel cross-domain feature fusion and multi-attention speech enhancement network, termed BSS-CFFMA, which leverages self-supervised embeddings. BSS-CFFMA comprises a multi-scale cross-domain feature fusion (MSCFF) block and a residual hybrid multi-attention (RHMA) block. The MSCFF block effectively integrates cross-domain features, facilitating the extraction of rich acoustic information. The RHMA block, serving as the primary enhancement module, utilizes three distinct attention modules to capture diverse attention representations and estimate high-quality speech signals. We evaluate the performance of the BSS-CFFMA model through comparative and ablation studies on the VoiceBank-DEMAND dataset, achieving SOTA results. Furthermore, we select three types of data from the WHAMR! dataset, a collection specifically designed for speech enhancement tasks, to assess the capabilities of BSS-CFFMA in tasks such as denoising only, dereverberation only, and simultaneous denoising and dereverberation. This study marks the first attempt to explore the effectiveness of self-supervised embedding-based speech enhancement methods in complex tasks encompassing dereverberation and simultaneous denoising and dereverberation. The demo implementation of BSS-CFFMA is available online\footnote[2]{https://github.com/AlimMat/BSS-CFFMA. \label{s1}}.

Missing data is a pervasive and challenging issue in various applications of statistical inference, such as healthcare, economics, and the social sciences. Data are said to be Missing at Random (MAR) when the mechanism of missingness depends only on the observed data. Strategies to deal with MAR have been extensively investigated in the literature (Dempster et al., 1977; Robins et al., 1994; Tsiatis, 2006; Little and Rubin, 2019). In many practical settings, MAR is not a realistic assumption. Instead, missingness often depends on variables that are themselves missing. Such settings are said to exhibit nonignorable missingness, with the resulting data being Missing Not at Random (MNAR) (Fielding et al., 2008; Schafer and Graham, 2002), A classic example of a scenario with MNAR data occurs in longitudinal studies, due to the treatment's toxicity, some patients may become too ill to visit the clinic, leading to the situation where the outcome of certain patients with circumstances associated with those outcomes are more likely to be lost to follow-up (Ibrahim et al., 2012). Previous MNAR models have typically imposed constraints on the target distribution and its missingness mechanism, ensuring the parameter of interest can be identified. This approach goes back to the work of Heckman (1979), who proposed an outcome-selection model based on parametric modeling of the outcome variable and missing pattern. Little (1993) introduced the pattern-mixture model where one needs to specify the distribution for each missing data pattern independently.

Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domains, where a minority class in one domain could have abundant instances from other domains. We formalize the task of Multi-Domain Long-Tailed Recognition (MDLT), which learns from multi-domain imbalanced data, addresses label imbalance, domain shift, and divergent label distributions across domains, and generalizes to all domain-class pairs. We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT. We then propose BoDA, a theoretically grounded learning strategy that tracks the upper bound of transferability statistics, and ensures balanced alignment and calibration across imbalanced domain-class distributions. We curate five MDLT benchmarks based on widely-used multi-domain datasets, and compare BoDA to twenty algorithms that span different learning strategies. Extensive and rigorous experiments verify the superior performance of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on Domain Generalization benchmarks, highlighting the importance of addressing data imbalance across domains, which can be crucial for improving generalization to unseen domains. Code and data are available at: https://github.com/YyzHarry/multi-domain-imbalance.

We present an empirical study of scaling properties of encoder-decoder Transformer models used in neural machine translation (NMT). We show that cross-entropy loss as a function of model size follows a certain scaling law. Specifically (i) We propose a formula which describes the scaling behavior of cross-entropy loss as a bivariate function of encoder and decoder size, and show that it gives accurate predictions under a variety of scaling approaches and languages; we show that the total number of parameters alone is not sufficient for such purposes. (ii) We observe different power law exponents when scaling the decoder vs scaling the encoder, and provide recommendations for optimal allocation of encoder/decoder capacity based on this observation. (iii) We also report that the scaling behavior of the model is acutely influenced by composition bias of the train/test sets, which we define as any deviation from naturally generated text (either via machine generated or human translated text). We observe that natural text on the target side enjoys scaling, which manifests as successful reduction of the cross-entropy loss. (iv) Finally, we investigate the relationship between the cross-entropy loss and the quality of the generated translations. We find two different behaviors, depending on the nature of the test data. For test sets which were originally translated from target language to source language, both loss and BLEU score improve as model size increases. In contrast, for test sets originally translated from source language to target language, the loss improves, but the BLEU score stops improving after a certain threshold. We release generated text from all models used in this study.

Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been created, it is often expensive to allow it to adapt to new conditional controls, especially the network structure is relatively deep. We propose a conditioned latent domain transfer framework across latent spaces of unconditional variational autoencoders(VAE). With this framework, we can allow unconditionally trained VAEs to generate images in its domain with conditionals provided by a latent representation of another domain. This framework does not assume commonalities between two domains. We demonstrate effectiveness and robustness of our model under widely used image datasets.

We propose a probabilistic model for non-exhaustive and overlapping (NEO) bi-clustering. Our goal is to extract a few sub-matrices from the given data matrix, where entries of a sub-matrix are characterized by a specific distribution or parameters. Existing NEO biclustering methods typically require the number of sub-matrices to be extracted, which is essentially difficult to fix a priori. In this paper, we extend the plaid model, known as one of the best NEO bi-clustering algorithms, to allow infinite bi-clustering; NEO bi-clustering without specifying the number of sub-matrices. Our model can represent infinite sub-matrices formally. We develop a MCMC inference without the finite truncation, which potentially addresses all possible numbers of sub-matrices. Experiments quantitatively and qualitatively verify the usefulness of the proposed model. The results reveal that our model can offer more precise and in-depth analysis of sub-matrices.

In this paper we computationally examine how subjective experience may help or harm the decision maker's learning under uncertain outcomes, frames and their interactions. To model subjective experience, we propose the "experienced-utility function" based on a prospect theory (PT)-based parameterized subjective value function. Our analysis and simulations of two-armed bandit tasks present that the task domain (underlying outcome distributions) and framing (reference point selection) influence experienced utilities and in turn, the "subjective discriminability" of choices under uncertainty. Experiments demonstrate that subjective discriminability improves on objective discriminability by the use of the experienced-utility function with appropriate framing for a given task domain, and that bigger subjective discriminability leads to more optimal decisions in learning under uncertainty.