Tensor completion algorithms for estimating missing values in multi-channel audio signals
Audio inpainting is a widely used technology in the real world since audio signals with missing data are pervasive in many scenarios. The majority of existing works address the time gaps in single-channel audio signals, while completing multi-channel audio signals is rarely investigated.In this work...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/148943 |
| Acceso en línea: | http://hdl.handle.net/11336/148943 |
| Access Level: | acceso abierto |
| Palabra clave: | audio impainting tensor completion signal reconstruction multichannel signals https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Audio inpainting is a widely used technology in the real world since audio signals with missing data are pervasive in many scenarios. The majority of existing works address the time gaps in single-channel audio signals, while completing multi-channel audio signals is rarely investigated.In this work, we tackle this issue using four different tensor completion algorithms and we evaluate them on speech audio datasets with gaps in the time domain. Based on extensive quantitative and qualitative experiments, the tensor completion algorithms generally achieve a superior predictive performance, including when the gap duration of the signals reaches values of up to 200 ms. Specifically, the experimental results illustrate that all of the applied tensor completion algorithms yield at least 56% improvement in signal restoration performance compared with single-channel based methods. Therefore, the tensor based approaches can capture the underlying latent structure over different channels to reconstruct incomplete multi-channel data. |
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