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Found 22 papers, 12 papers with code
Full-band General Audio Synthesis with Score-based Diffusion
Recent works have shown the capability of deep generative models to tackle general audio synthesis from a single label, producing a variety of impulsive, tonal, and environmental sounds.
Adversarial Permutation Invariant Training for Universal Sound Separation
Universal sound separation consists of separating mixes with arbitrary sounds of different types, and permutation invariant training (PIT) is used to train source agnostic models that do so.
Universal Speech Enhancement with Score-based Diffusion
We hope that both our methodology and technical contributions encourage researchers and practitioners to adopt a universal approach to speech enhancement, possibly framing it as a generative task.
On loss functions and evaluation metrics for music source separation
We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation.
Upsampling layers for music source separation
Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling.
On tuning consistent annealed sampling for denoising score matching
Score-based generative models provide state-of-the-art quality for image and audio synthesis.
Upsampling artifacts in neural audio synthesis
We then compare different upsampling layers, showing that nearest neighbor upsamplers can be an alternative to the problematic (but state-of-the-art) transposed and subpixel convolutions which are prone to introduce tonal artifacts.
Automatic multitrack mixing with a differentiable mixing console of neural audio effects
Applications of deep learning to automatic multitrack mixing are largely unexplored.
Audio and Speech Processing Sound
SESQA: semi-supervised learning for speech quality assessment
Automatic speech quality assessment is an important, transversal task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen recording conditions, and a lack of flexibility of existing approaches.
Multi-task self-supervised learning for Robust Speech Recognition
We then propose a revised encoder that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks.
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space
The candidate time windows are selected from a set of large time intervals, possibly including a sample drop, and by using a preprocessing step.
Sound Audio and Speech Processing I.2.7
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
This way we feed the acoustic model with speaker acoustically dependent representations that enrich the waveform generation more than discrete embeddings unrelated to these factors.
Sound Audio and Speech Processing
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations.
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure.
Ranked #2 on
Distant Speech Recognition
on DIRHA English WSJ
Towards Generalized Speech Enhancement with Generative Adversarial Networks
The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility.
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.
Self-Attention Linguistic-Acoustic Decoder
The conversion from text to speech relies on the accurate mapping from linguistic to acoustic symbol sequences, for which current practice employs recurrent statistical models like recurrent neural networks.
Whispered-to-voiced Alaryngeal Speech Conversion with Generative Adversarial Networks
Most methods of voice restoration for patients suffering from aphonia either produce whispered or monotone speech.
Towards a universal neural network encoder for time series
We evaluate the performance of the proposed approach on a well-known time series classification benchmark, considering full adaptation, partial adaptation, and no adaptation of the encoder to the new data type.
Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
In this work, we present the results of adapting a speech enhancement generative adversarial network by finetuning the generator with small amounts of data.
SEGAN: Speech Enhancement Generative Adversarial Network
In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them.
Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks
This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.
