88 citations · 305 across the 22 of their papers we have counts for
31 papers
Speech-DRAME: A Framework for Human-Aligned Benchmarks in Speech Role-Play
Jiatong Shi, Jionghao Han, Yichen Lu +6
Role-play has become a key testbed for generative models, expanding from text-only dialogue to multimodal interaction. Extending role-play to speech captures prosody, emotion, and…
Joint Semantic Knowledge Distillation and Masked Acoustic Modeling for Full-band Speech Restoration with Improved Intelligibility
Xiaoyu Liu, Xu Li, Joan Serrà +1
Speech restoration aims at restoring full-band speech with high quality and intelligibility, considering a diverse set of distortions. MaskSR is a recently proposed generative mode…
Masked Generative Video-to-Audio Transformers with Enhanced Synchronicity
Santiago Pascual, Chunghsin Yeh, Ioannis Tsiamas +1
Video-to-audio (V2A) generation leverages visual-only video features to render plausible sounds that match the scene. Importantly, the generated sound onsets should match the visua…
Sequential Contrastive Audio-Visual Learning
Ioannis Tsiamas, Santiago Pascual, Chunghsin Yeh +1
Contrastive learning has emerged as a powerful technique in audio-visual representation learning, leveraging the natural co-occurrence of audio and visual modalities in webscale vi…
GASS: Generalizing Audio Source Separation with Large-scale Data
Jordi Pons, Xiaoyu Liu, Santiago Pascual +1
Universal source separation targets at separating the audio sources of an arbitrary mix, removing the constraint to operate on a specific domain like speech or music. Yet, the pote…
V2A-Mapper: A Lightweight Solution for Vision-to-Audio Generation by Connecting Foundation Models
Heng Wang, Jianbo Ma, Santiago Pascual +2
Building artificial intelligence (AI) systems on top of a set of foundation models (FMs) is becoming a new paradigm in AI research. Their representative and generative abilities le…
CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models
Hao-Wen Dong, Xiaoyu Liu, Jordi Pons +5
Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acqu…
Mono-to-stereo through parametric stereo generation
Joan Serrà, Davide Scaini, Santiago Pascual +4
Generating a stereophonic presentation from a monophonic audio signal is a challenging open task, especially if the goal is to obtain a realistic spatial imaging with a specific pa…
Full-band General Audio Synthesis with Score-based Diffusion
Santiago Pascual, Gautam Bhattacharya, Chunghsin Yeh +2
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 s…
Adversarial Permutation Invariant Training for Universal Sound Separation
Emilian Postolache, Jordi Pons, Santiago Pascual +1
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 t…
Universal Speech Enhancement with Score-based Diffusion
Joan Serrà, Santiago Pascual, Jordi Pons +2
Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings.…
On loss functions and evaluation metrics for music source separation
Enric Gusó, Jordi Pons, Santiago Pascual +1
We investigate which loss functions provide better separations via benchmarking an extensive set of those for music source separation. To that end, we first survey the most represe…
Upsampling layers for music source separation
Jordi Pons, Joan Serrà, Santiago Pascual +3
Upsampling artifacts are caused by problematic upsampling layers and due to spectral replicas that emerge while upsampling. Also, depending on the used upsampling layer, such artif…
Adversarial Auto-Encoding for Packet Loss Concealment
Santiago Pascual, Joan Serrà, Jordi Pons
Communication technologies like voice over IP operate under constrained real-time conditions, with voice packets being subject to delays and losses from the network. In such cases,…
On tuning consistent annealed sampling for denoising score matching
Joan Serrà, Santiago Pascual, Jordi Pons
Score-based generative models provide state-of-the-art quality for image and audio synthesis. Sampling from these models is performed iteratively, typically employing a discretized…
Automatic multitrack mixing with a differentiable mixing console of neural audio effects
Christian J. Steinmetz, Jordi Pons, Santiago Pascual +1
Applications of deep learning to automatic multitrack mixing are largely unexplored. This is partly due to the limited available data, coupled with the fact that such data is relat…
Upsampling artifacts in neural audio synthesis
Jordi Pons, Santiago Pascual, Giulio Cengarle +1
A number of recent advances in neural audio synthesis rely on upsampling layers, which can introduce undesired artifacts. In computer vision, upsampling artifacts have been studied…
SESQA: semi-supervised learning for speech quality assessment
Joan Serrà, Jordi Pons, Santiago Pascual
Automatic speech quality assessment is an important, transversal task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen recording condi…
Multi-task self-supervised learning for Robust Speech Recognition
Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual +4
Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recentl…
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space
Tina Raissi, Santiago Pascual, Maurizio Omologo
In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drop…
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Joan Serrà, Santiago Pascual, Carlos Segura
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. Voice conversion,…
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN
David Álvarez, Santiago Pascual, Antonio Bonafonte
Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems…
Towards Generalized Speech Enhancement with Generative Adversarial Networks
Santiago Pascual, Joan Serrà, Antonio Bonafonte
The speech enhancement task usually consists of removing additive noise or reverberation that partially mask spoken utterances, affecting their intelligibility. However, little att…
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Santiago Pascual, Mirco Ravanelli, Joan Serrà +2
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 l…