5 citations · 11 across the 12 of their papers we have counts for
12 papers
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
Luca Della Libera, Cem Subakan, Mirco Ravanelli
Neural audio codecs are a fundamental component of modern speech generation systems. While recent codecs achieve increasingly low bitrates, reducing frame rate remains challenging,…
Exploring Token-Space Manipulation in Latent Audio Tokenizers
Francesco Paissan, Luca Della Libera, Mirco Ravanelli +1
Neural audio codecs provide compact discrete representations for speech generation and manipulation. However, most codecs organize tokens as frame-level sequences, making it diffic…
LL-SDR: Low-Latency Speech enhancement through Discrete Representations
Jingyi Li, Luca Della Libera, Mirco Ravanelli +2
Many speech enhancement (SE) methods rely on continuous representations. Recently, discrete audio tokens have been explored to enable autoregressive generation for SE. However, it…
WavSLM: Single-Stream Speech Language Modeling via WavLM Distillation
Luca Della Libera, Cem Subakan, Mirco Ravanelli
Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the enta…
How to Choose a Reinforcement-Learning Algorithm
Fabian Bongratz, Vladimir Golkov, Lukas Mautner +5
The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an…
How Should We Extract Discrete Audio Tokens from Self-Supervised Models?
Pooneh Mousavi, Jarod Duret, Salah Zaiem +4
Discrete audio tokens have recently gained attention for their potential to bridge the gap between audio and language processing. Ideal audio tokens must preserve content, paraling…
DASB - Discrete Audio and Speech Benchmark
Pooneh Mousavi, Jarod Duret, Darius Petermann +5
Discrete audio tokens have recently gained considerable attention for their potential to bridge audio and language processing, enabling multimodal language models that can both gen…
Listenable Maps for Zero-Shot Audio Classifiers
Francesco Paissan, Luca Della Libera, Mirco Ravanelli +1
Interpreting the decisions of deep learning models, including audio classifiers, is crucial for ensuring the transparency and trustworthiness of this technology. In this paper, we…
Focal Modulation Networks for Interpretable Sound Classification
Luca Della Libera, Cem Subakan, Mirco Ravanelli
The increasing success of deep neural networks has raised concerns about their inherent black-box nature, posing challenges related to interpretability and trust. While there has b…
CL-MASR: A Continual Learning Benchmark for Multilingual ASR
Luca Della Libera, Pooneh Mousavi, Salah Zaiem +2
Modern multilingual automatic speech recognition (ASR) systems like Whisper have made it possible to transcribe audio in multiple languages with a single model. However, current st…
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan, Luca Della Libera, Zhepei Wang +3
In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits that remain faithful to the input audio.…
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods
Luca Della Libera, Vladimir Golkov, Yue Zhu +2
Convolutional networks are successful due to their equivariance/invariance under translations. However, rotatable data such as images, volumes, shapes, or point clouds require proc…