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Found 21 papers, 4 papers with code
Sampling-Frequency-Independent Universal Sound Separation
The USS aims at separating arbitrary sources of different types and can be the key technique to realize a source separator that can be universally used as a preprocessor for any downstream tasks.
Versatile Time-Frequency Representations Realized by Convex Penalty on Magnitude Spectrogram
Sparse time-frequency (T-F) representations have been an important research topic for more than several decades.
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
The constituent samples of LibriTTS-R are identical to those of LibriTTS, with only the sound quality improved.
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web.
WaveFit: An Iterative and Non-autoregressive Neural Vocoder based on Fixed-Point Iteration
The DDPMs and GANs can be characterized by the iterative denoising framework and adversarial training, respectively.
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping
Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features.
Wearable SELD dataset: Dataset for sound event localization and detection using wearable devices around head
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction.
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
Still, they cannot perform well if the training data have mismatches and/or constraints in the time domain are not imposed.
Design of Tight Minimum-Sidelobe Windows by Riemannian Newton's Method
The short-time Fourier transform (STFT), or the discrete Gabor transform (DGT), has been extensively used in signal analysis and processing.
Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method
Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals.
Sparse time-frequency representation via atomic norm minimization
In this paper, we propose a method of estimating a sparse T-F representation using atomic norm.
Noisy-target Training: A Training Strategy for DNN-based Speech Enhancement without Clean Speech
This requirement currently restricts the amount of training data for speech enhancement to less than 1/1000 of that of speech recognition which does not need clean signals.
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.
Gamma Boltzmann Machine for Simultaneously Modeling Linear- and Log-amplitude Spectra
Its conditional distribution of the observable data is given by the gamma distribution, and thus the proposed RBM can naturally handle the data represented by positive numbers as the amplitude spectra.
Consistent ICA: Determined BSS meets spectrogram consistency
Multichannel audio blind source separation (BSS) in the determined situation (the number of microphones is equal to that of the sources), or determined BSS, is performed by multichannel linear filtering in the time-frequency domain to handle the convolutive mixing process.
Determined BSS based on time-frequency masking and its application to harmonic vector analysis
This paper proposes harmonic vector analysis (HVA) based on a general algorithmic framework of audio blind source separation (BSS) that is also presented in this paper.
Phase reconstruction based on recurrent phase unwrapping with deep neural networks
In the proposed method, DNNs estimate phase derivatives instead of phase itself, which allows us to avoid the sensitivity problem.
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance.
Stable Training of DNN for Speech Enhancement based on Perceptually-Motivated Black-Box Cost Function
For evaluating the subjective quality, several methods related to perceptually-motivated objective sound quality assessment (OSQA) have been proposed such as PESQ (perceptual evaluation of speech quality).
Invertible DNN-based nonlinear time-frequency transform for speech enhancement
Therefore, some end-to-end methods used a DNN to learn the linear T-F transform which is much easier to understand.
Audio and Speech Processing Sound
Deep Griffin-Lim Iteration
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN).
