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Article Dans Une Revue Signal Processing Année : 2022

A Non Intrusive Audio Clarity Index (NIAC) and its Application to Blind Source Separation

Résumé

We propose a Non-Intrusive (or reference-free) Audio Clarity index (NIAC), inspired from previous works on image sharpness and defined as the sensitivity of the spectrogram sparsity to a convolution of the audio signal with a white noise. A closed-form formula is provided, which only involves the signal itself and very little parameter setting. Tested in various noise and reverberation conditions, the NIAC exhibits a high correlation with the well-established Speech Transmission Index, both for speech and music. It can also be used as a clarity criterion to drive sound enhancement algorithms. We propose a NIAC-based source separation algorithm, and show that its performance is comparable to that of a state-of-the-art algorithm, FastICA.
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Dates et versions

hal-03195338 , version 1 (11-04-2021)

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Gaël Mahé, Giulio G R Suzumura, Lionel Moisan, Ricardo Suyama. A Non Intrusive Audio Clarity Index (NIAC) and its Application to Blind Source Separation. Signal Processing, 2022, 194, ⟨10.1016/j.sigpro.2021.108448⟩. ⟨hal-03195338⟩
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