Signal Enhancement with Variable Span Linear Filters [recurso electrónico] / by Jacob Benesty, Mads G. Christensen, Jesper R. Jensen.
Tipo de material: TextoSeries Springer Topics in Signal Processing ; 7Editor: Singapore : Springer Singapore : Imprint: Springer, 2016Edición: 1st ed. 2016Descripción: IX, 172 p. 25 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789812877390Tema(s): Engineering | Engineering | Signal, Image and Speech ProcessingFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5102.9TA1637-1638TK7882.S65Recursos en línea: Libro electrónicoTipo de ítem | Biblioteca actual | Colección | Signatura | Copia número | Estado | Fecha de vencimiento | Código de barras |
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Libro Electrónico | Biblioteca Electrónica | Colección de Libros Electrónicos | 1 | No para préstamo |
Introduction -- General Concept with Filtering Vectors -- General Concept with Filtering Matrices -- Single-Channel Signal Enhancement in the STFT Domain -- Multichannel Signal Enhancement in the Time Domain -- Multichannel Signal Enhancement in the STFT Domain -- Binaural Signal Enhancement in the Time Domain.
This book introduces readers to the novel concept of variable span speech enhancement filters, and demonstrates how it can be used for effective noise reduction in various ways. Further, the book provides the accompanying Matlab code, allowing readers to easily implement the main ideas discussed. Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal-to-noise ratio, Wiener, and tradeoff filters (including their new generalizations) can be obtained using the variable span filter framework. It then illustrates how the variable span filters can be applied in various contexts, namely in single-channel STFT-based enhancement, in multichannel enhancement in both the time and STFT domains, and, lastly, in time-domain binaural enhancement. In these contexts, the properties of these filters are analyzed in terms of their noise reduction capabilities and desired signal distortion, and the analyses are validated and further explored in simulations.