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Master thesis

Gaussian Framework for Interference Reduction in Live Recordings

Abstract : In this study, typical live full-length music recordings are considered. In this scenarios, some instrumental voices are captured by microphones intended to other voices, leading to so-called “interferences”. Reducing this phenomenon is desirable because it opens new possibilities for sound engineers and also it has been proven that it increase performances of music analysis and processing tools (e.g. pitch tracking). Extending state-of-the-art methods, we propose an NMF-based algorithm that iteratively estimate each source contribution, i.e. the power spectral densities (PSDs), and the corresponding strength in each microphone signal, modeled in a interference matrix. Unfortunately our approach suffer of a huge computational load. To address this issue, We show that using random projection method the method is able to process full-length live multi-track recoding in a acceptable time. Experimental results demonstrate the efficiency and the effectiveness of the approach.
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Contributor : Diego Di Carlo Connect in order to contact the contributor
Submitted on : Monday, September 10, 2018 - 10:45:51 AM
Last modification on : Friday, February 4, 2022 - 3:19:59 AM
Long-term archiving on: : Tuesday, December 11, 2018 - 1:18:55 PM


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  • HAL Id : hal-01870918, version 1



Diego Di Carlo. Gaussian Framework for Interference Reduction in Live Recordings. Signal and Image processing. 2017. ⟨hal-01870918⟩



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