514 citations · 599 across the 69 of their papers we have counts for
8 papers · 2 filters
Investigating the Effectiveness of Explainability Methods in Parkinson's Detection from Speech
Eleonora Mancini, Francesco Paissan, Paolo Torroni +2
Speech impairments in Parkinson's disease (PD) provide significant early indicators for diagnosis. While models for speech-based PD detection have shown strong performance, their i…
LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Eleonora Mancini, Francesco Paissan, Mirco Ravanelli +1
Neural networks are typically black-boxes that remain opaque with regards to their decision mechanisms. Several works in the literature have proposed post-hoc explanation methods t…
What Are They Doing? Joint Audio-Speech Co-Reasoning
Yingzhi Wang, Pooneh Mousavi, Artem Ploujnikov +1
In audio and speech processing, tasks usually focus on either the audio or speech modality, even when both sounds and human speech are present in the same audio clip. Recent Audito…
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…