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FastWave: Optimized Diffusion Model for Audio Super-Resolution
P. 4506–4510.
Kuznetsov N., Kaledin M.
Audio Super-Resolution is a set of techniques aimed at high-quality estimation of the given signal as if it would be sampled with higher sample rate. Among suggested methods there are diffusion and flow models (which are considered slower), generative adversarial networks (which are considered faster), however both approaches are currently presented by high-parametric networks, requiring high computational costs both for training and inference. We propose a solution to both these problems by re-considering the recent advances in the training of diffusion models and applying them to super-resolution from any to 48 kHz sample rate. Our model called FastWave has around 50 GFLOPs of computational complexity and 1.3 M parameters and can be trained with less resources and significantly faster than the majority of recently proposed diffusion- and flow-based solutions. FastWave also has comparable performance to state-of-the-art models. We provide our implementation on GitHub.
In book
International Speech Communication Association, 2026.
OrthoFuse: Training-free Riemannian Fusion of Orthogonal Style-Concept Adapters for Diffusion Models
Ali Aliev, Garifullin K., Yudin N. et al., , in: 2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).: IEEE, 2026. P. 36009–36018.
In a rapidly growing field of model training there is a constant practical interest in parameter-efficient fine-tuning and various techniques that use a small amount of training data to adapt the model to a narrow task. However, there is an open question: how to combine several adapters tuned for different tasks into one which is ...
Added: July 13, 2026
Oganov A., Bykov I., Neudachina E. et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 18372.
While diffusion models achieve state-of-the-art generation quality, they still suffer from computationally expensive sampling. Recent works address this issue with gradient-based optimization methods that distill a few-step ODE diffusion solver from the full sampling process, reducing the number of function evaluations from dozens to just a few. However, these approaches often rely on intricate training ...
Added: July 13, 2026
Vladimir Bogachev, Aletov V., Alexander Molozhavenko et al., , in: The Fourteenth International Conference on Learning Representations (ICLR 2026).: ICLR, 2026. Ch. 20503 P. 1–26.
This work presents a novel, fully Riemannian framework for Low-Rank Adaptation (LoRA) that geometrically treats low-rank adapters by optimizing them directly on the fixed-rank manifold. This formulation eliminates the parametrization ambiguity present in standard Euclidean optimizers. Our framework integrates three key components to achieve this: (1) we derive Riemannion, a new Riemannian optimizer on the fixed-rank ...
Added: April 29, 2026
Dogonasheva O., Giraud A., Zakharov D. et al., Neural Networks 2025 Vol. 195 Article 108194
In this review, we examine computational models that explore the role of neural oscillations in speech perception, spanning from early auditory processing to higher cognitive stages. We focus on models that use rhythmic brain activities, such as gamma, theta, and delta oscillations, to encode phonemes, segment speech into syllables and words, and integrate linguistic elements ...
Added: October 16, 2025
Deeb B., Savchenko A., Makarov I., , in: ECAI 2024. 27th European Conference on Artificial Intelligence, October 19 – 24 October 2024, Santiago de Compostela, Spain – Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024).: IOS Press, 2024. P. 4479–4482.
In this paper, we introduce a novel tool for speech emotion recognition, CA-SER, that borrows self-supervised learning to extract semantic speech representations from a pre-trained wav2vec 2.0 model and combine them with spectral audio features to improve speech emotion recognition. Our approach involves a self-attention encoder on MFCC features to capture meaningful patterns in audio ...
Added: February 15, 2025
Alexeeva M., Myachykov A., Shtyrov Y., Журнал высшей нервной деятельности им. И.П. Павлова 2022 Т. 72 № 5 С. 666–677
Language functioning as a communicative system is described by a multitude of linguistic theories, which are not always consistent with each other and do not have strong cognitive and/or neurobiological bases. One of the most striking examples is the “zero morpheme” proposed by the Universal Grammar theory, which has only an abstract meaning and no ...
Added: September 20, 2022
Savchenko A., Savchenko V., Savchenko L., Optimization Letters 2022 Vol. 16 No. 7 P. 2095–2113
This paper considers an assessment and evaluation of speech sound pronunciation quality in computer-aided language learning systems. We examine the gain optimization of spectral distortion measures between the speech signals of a native speaker and a learner. During training, a learner has to achieve stable pronunciation of all sounds. This is measured by computing the ...
Added: August 18, 2022
Савченко В. В., Savchenko A., Радиотехника и электроника 2020 Т. 65 № 11 С. 1101–1108
The article considers the problem of automatic segmentation of a speech signal into phonetic units in conditions of their a priori uncertain spectral composition and correlation properties. A guaranteed significance level criterion is developed based on the information–theoretic approach. An example of practical application of this criterion is considered; a full-scale experiment is set up ...
Added: November 28, 2020
Savchenko V., Savchenko A., Measurement Techniques 2020 Vol. 63 No. 5 P. 391–400
The article continues our previous work [V. V. Savchenko and A. V. Savchenko, Izmeritel’naya Tekhnika, No. 12, 46–52 (2019)]. We study the problem of automatic control of the quality of voice samples recorded and stored in the Unified Biometric System. We propose a solution of the problem of timely updates of the collected samples required ...
Added: October 1, 2020
Savchenko V., Savchenko A., Measurement Techniques 2020 Vol. 62 No. 12 P. 1071–1078
The problem of automated quality control of audio recordings containing voice samples of individuals is considered. It is shown that in solving this problem the most acute impediment is the problem of small samples of observations. To overcome the problem, a new, high-speed method of acoustic measurements is proposed, based on the principle of relative ...
Added: April 22, 2020
Springer, 2019.
Speech and Computer ...
Added: October 28, 2019
Savchenko A., Savchenko V., Radioelectronics and Communications Systems 2019 Vol. 62 No. 5 P. 223–231
It is researched a wide class of parametric estimations of power spectral density based on principle of entropy maximum and autoregression observation model. At that there is distinguished the key parameter which is used model order. It is considered a problem of a priori uncertainty when true value of order is a priori unknown. It ...
Added: August 16, 2019
Savchenko A., Savchenko V., Measurement Techniques 2019 Vol. 62 No. 3 P. 282–288
We developed a new method for measuring the pitch frequency of speech signals with elevated noise immunity. The problem of protection against intense background noise is solved in this method by the frequency selection of vocalized segments of speech signals according to a scheme with comb filter of interperiodic accumulation. The efficiency of the method ...
Added: August 16, 2019
Karpov N., Системы управления и информационные технологии 2012 Т. 48 № 2.1 С. 145–149
In this article analyzed some methods of speech quality estimation based on State Standard and Informational Theory of Speech Perception. Experimentally examine effectiveness and boundaries of free methods for speech parameterization and using it with deferent metrics. ...
Added: September 11, 2012