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Found 31 papers, 9 papers with code
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.
Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
In 2023 Task 2, we focus on solving the first-shot problem, which is the challenge of training a model on a completely novel machine type.
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.
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''.
Mask scalar prediction for improving robust automatic speech recognition
Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech.
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.
SNRi Target Training for Joint Speech Enhancement and Recognition
Furthermore, by analyzing the predicted target SNRi, we observed the jointly trained network automatically controls the target SNRi according to noise characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
To make the model computationally feasible, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers.
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identifying whether a given sound was normal or anomalous without anomalous training data.
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.
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.
Audio Captioning using Pre-Trained Large-Scale Language Model Guided by Audio-based Similar Caption Retrieval
Then, the caption of the audio input is generated by using a pre-trained language model while referring to the guidance captions.
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning
The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning.
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning.
Ranked #4 on
Audio captioning
on Clotho
A Transformer-based Audio Captioning Model with Keyword Estimation
TRACKE estimates keywords, which comprise a word set corresponding to audio events/scenes in the input audio, and generates the caption while referring to the estimated keywords to reduce word-selection indeterminacy.
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data.
Listen to What You Want: Neural Network-based Universal Sound Selector
In this paper, we propose instead a universal sound selection neural network that enables to directly select AE sounds from a mixture given user-specified target AE classes.
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.
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.
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
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation
The same transformation is applied also to the labels, in order to maintain consistency between input data and target labels.
DOA Estimation by DNN-based Denoising and Dereverberation from Sound Intensity Vector
We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation.
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them.
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch.
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).
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation
To address this difficulty, we propose AdaFlow, a new DNN-based density estimator that can be easily adapted to the change of the distribution.
Trainable Adaptive Window Switching for Speech Enhancement
To overcome this problem, we incorporate AWS into the speech enhancement procedure, and the windowing function of each time-frame is manipulated using a DNN depending on the input signal.
DNN-based Source Enhancement to Increase Objective Sound Quality Assessment Score
Since OSQA scores have been used widely for sound-quality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create high-quality output signals.
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm.
