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20242026
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cs.SD2026

Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal

Modan Tailleur, Mathieu Lagrange, Pierre Aumond +1

Environmental sound recordings often contain intelligible speech, raising privacy concerns that limit analysis, sharing and reuse of data. In this paper, we introduce a method that…

cs.SD2026

Predicting Timbre Traits for Interpretable Assessment of Musical Sound Synthesizers

Théo Chasle Cauchy, Modan Tailleur, Lindsey Reymore +2

Measuring neural audio synthesizers' performance is now routinely conducted using distribution based metrics such as the Fréchet Audio Distance (FAD). Although this metric can be…

cs.SD2024

Challenge on Sound Scene Synthesis: Evaluating Text-to-Audio Generation

Junwon Lee, Modan Tailleur, Laurie M. Heller +5

Despite significant advancements in neural text-to-audio generation, challenges persist in controllability and evaluation. This paper addresses these issues through the Sound Scene…

cs.SD2024

Machine listening in a neonatal intensive care unit

Modan Tailleur, Vincent Lostanlen, Jean-Philippe Rivière +1

Oxygenators, alarm devices, and footsteps are some of the most common sound sources in a hospital. Detecting them has scientific value for environmental psychology but comes with c…

cs.SD2024

EMVD dataset: a dataset of extreme vocal distortion techniques used in heavy metal

Modan Tailleur, Julien Pinquier, Laurent Millot +2

In this paper, we introduce the Extreme Metal Vocals Dataset, which comprises a collection of recordings of extreme vocal techniques performed within the realm of heavy metal music…

cs.SD2024

Detection of Deepfake Environmental Audio

Hafsa Ouajdi, Oussama Hadder, Modan Tailleur +2

With the ever-rising quality of deep generative models, it is increasingly important to be able to discern whether the audio data at hand have been recorded or synthesized. Althoug…