Revista: | Computación y sistemas |
Base de datos: | |
Número de sistema: | 000560528 |
ISSN: | 1405-5546 |
Autores: | Maldonado, Alejandro1 Rascón, Caleb2 Vélez, Ivette1 |
Instituciones: | 1Universidad Nacional Autónoma de México, Ciudad de México. México 2Universidad Nacional Autónoma de México, Instituto de Investigaciones en Matemáticas Avanzadas y en Sistemas, Ciudad de México. México |
Año: | 2020 |
Periodo: | Jul-Sep |
Volumen: | 24 |
Número: | 3 |
Paginación: | 1257-1270 |
País: | México |
Idioma: | Inglés |
Tipo de documento: | Artículo |
Resumen en inglés | Online audio source separation has been an important part of auditory scene analysis and robot audition. The main type of technique to carry this out, because of its online capabilities, has been spatial filtering (or beamforming), where it is assumed that the location (mainly, the direction of arrival; DOA) of the source of interest (SOI) is known. However, these techniques suffer from considerable interference leakage in the final result. In this paper, we propose a two step technique: 1) a phase-based beamformer that provides, in addition to the estimation of the SOI, an estimation of the cumulative environmental interference; and 2) a BLSTM-based TF binary masking stage that calculates a binary mask that aims to separate the SOI from the cumulative environmental interference. In our tests, this technique provides a signal-to-interference ratio (SIR) above 20 dB with simulated data. Because of the nature of the beamformer outputs, the label permutation problem is handled from the beginning. This makes the proposed solution a lightweight alternative that requires considerably less computational resources (almost an order of magnitude) compared to current deep-learning based techniques, while providing a comparable SIR performance. |
Disciplinas: | Ciencias de la computación |
Palabras clave: | Procesamiento de datos |
Keyword: | Beamforming, BLSTM, Permutation problem, Binary mask, Data processing |
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