514 citations · 586 across the 44 of their papers we have counts for
49 papers
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…
SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression
Benjamin Léger, Shubham Gupta, Samy Mammeri +3
Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scientific discovery. Symbolic Reg…
Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting
Fırat Öncel, Salman Hussain Ali, Mirco Ravanelli +2
Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pretraining (CPT) is the natural…
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,…
Enhancing Audio Reasoning via Semantic Summary Prediction
Francesco Bonzi, Pooneh Mousavi, Cem Subakan +1
Large Audio Language Models (LALMs) perform well on complex question answering but often show a reasoning gap, where explicit Chain-of-Thought (CoT) reduces accuracy compared to di…
Controllable and Content-Based Recommendations
Fırat Öncel, Jihoon Jeong, Emiliano Penaloza +3
Traditional recommendation systems rely on latent (dense) representations, making them difficult to interpret and control. We propose the Controllable and Content-Based Recommendat…
PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems
Shubham Gupta, Nazanin Mohammadi Sepahvand, Abhinav Kumar +6
As LLM agents evolve from single-user assistants into shared organizational infrastructure, new privacy risks emerge: inappropriate information may not only be exposed through outp…
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…
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…
Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization
Luca Della Libera, Cem Subakan, Mirco Ravanelli
Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete tokens that can be processed by LLMs. Howe…
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.…
Virtual Consistency for Audio Editing
Matthieu Cervera, Francesco Paissan, Mirco Ravanelli +1
Free-form, text-based audio editing remains a persistent challenge, despite progress in inversion-based neural methods. Current approaches rely on slow inversion procedures, limiti…
FocalCodec-Stream: Streaming Low-Bitrate Speech Coding via Causal Distillation
Luca Della Libera, Cem Subakan, Mirco Ravanelli
Neural audio codecs are a fundamental component of modern generative audio pipelines. Although recent codecs achieve strong low-bitrate reconstruction and provide powerful represen…
Investigating Faithfulness in Large Audio Language Models
Pooneh Mousavi, Lovenya Jain, Mirco Ravanelli +1
Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks. While these models can generate Ch…
Audio Prototypical Network For Controllable Music Recommendation
Fırat Öncel, Emiliano Penaloza, Haolun Wu +4
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While these models often provide strong recommenda…
Autoregressive Speech Enhancement via Acoustic Tokens
Luca Della Libera, Cem Subakan, Mirco Ravanelli
In speech processing pipelines, improving the quality and intelligibility of real-world recordings is crucial. While supervised regression is the primary method for speech enhancem…
Discrete Audio Tokens: More Than a Survey!
Pooneh Mousavi, Gallil Maimon, Adel Moumen +18
Discrete audio tokens are compact representations that aim to preserve perceptual quality, phonetic content, and speaker characteristics while enabling efficient storage and infere…
LiSTEN: Learning Soft Token Embeddings for Neural Audio LLMs
Pooneh Mousavi, Shubham Gupta, Cem Subakan +1
Foundation models based on large language models (LLMs) have shown great success in handling various tasks and modalities. However, adapting these models for general-purpose audio-…
ALAS: An Automatic Latent Alignment Score for Audio Language Models
Pooneh Mousavi, Yingzhi Wang, Mirco Ravanelli +1
Large Language Models (LLMs) are extended into Speech-LLMs, and the quality of the audio--text alignment they learn affects most downstream Spoken Language Understanding (SLU) beha…
Sample Compression for Self Certified Continual Learning
Jacob Comeau, Mathieu Bazinet, Pascal Germain +1
Continual learning algorithms aim to learn from a sequence of tasks. In order to avoid catastrophic forgetting, most existing approaches rely on heuristics and do not provide compu…
Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Shubham Gupta, Zichao Li, Tianyi Chen +4
Information retrieval is a core component of many intelligent systems as it enables conditioning of outputs on new and large-scale datasets. While effective, the standard practice…