514 citations · 586 across the 44 of their papers we have counts for
7 papers · 2 filters
Listen-to-Reason: Listen with Experts, Retrieve over a Graph, Reason with LLMs
Pooneh Mousavi, Mirco Ravanelli, Cem Subakan
Large audio-language models (LALMs) fuse an audio encoder into a large language model (LLM) through multi-stage training. This coupling means that a new domain or a stronger LLM re…
AnchorPrompt: Self-Distilled Soft Prompts for Robust Audio-Language Models
Pooneh Mousavi, Amir Ivry, Mirco Ravanelli +1
Large audio-language models (LALMs) are sensitive to input perturbations, such as noise, waveform corruption, and adversarial injections. We propose AnchorPrompt, an efficient adap…
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…
Listen First, Then Answer: Timestamp-Grounded Speech Reasoning
Jihoon Jeong, Pooneh Mousavi, Mirco Ravanelli +1
Large audio-language models (LALMs) can generate reasoning chains for their predictions, but it remains unclear whether these reasoning chains remain grounded in the input audio. I…
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…
Toward Faithful Explanations in Acoustic Anomaly Detection
Maab Elrashid, Anthony Deschênes, Cem Subakan +3
Interpretability is essential for user trust in real-world anomaly detection applications. However, deep learning models, despite their strong performance, often lack transparency.…