Deep Learning for Individual Listening Zone
| Autor: | Stefano Squartini, Leonardo Gabrielli, Giovanni Pepe, Luca Cattani, Carlo Tripodi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Frequency response
business.product_category Finite impulse response Computer science Flatness (systems theory) Acoustics 020206 networking & telecommunications 02 engineering and technology Impulse (physics) Signal GeneralLiterature_MISCELLANEOUS 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Loudspeaker business Binaural recording Headphones |
| Zdroj: | MMSP |
| DOI: | 10.1109/mmsp48831.2020.9287161 |
| Popis: | A recent trend in car audio systems is the generation of Individual Listening Zones (ILZ), allowing to improve phone call privacy and reduce disturbance to other passengers, without wearing headphones or earpieces. This is generally achieved by using loudspeaker arrays. In this paper, we describe an approach to achieve ILZ exploiting general purpose car loudspeakers and processing the signal through carefully designed Finite Impulse Response (FIR) filters. We propose a deep neural network approach for the design of filters coefficients in order to obtain a so-called bright zone, where the signal is clearly heard, and a dark zone, where the signal is attenuated. Additionally, the frequency response in the bright zone is constrained to be as flat as possible. Numerical experiments were performed taking the impulse responses measured with either one binaural pair or three binaural pairs for each passenger. The results in terms of attenuation and flatness prove the viability of the approach. |
| Databáze: | OpenAIRE |
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