Speech Enhancement: A Signal Subspace Perspective (Paperback)
暫譯: 語音增強:信號子空間視角

Jacob Benesty, Jesper Rindom Jensen, Mads Graesboll Christensen, Jingdong Chen

  • 出版商: Academic Press
  • 出版日期: 2014-01-14
  • 定價: $2,150
  • 售價: 8.0$1,720
  • 語言: 英文
  • 頁數: 138
  • 裝訂: Paperback
  • ISBN: 0128001399
  • ISBN-13: 9780128001394
  • 相關分類: 語音辨識 Speech-recognition
  • 立即出貨 (庫存=1)

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商品描述

Speech enhancement is a classical problem in signal processing, yet still largely unsolved. Two of the conventional approaches for solving this problem are linear filtering, like the classical Wiener filter, and subspace methods. These approaches have traditionally been treated as different classes of methods and have been introduced in somewhat different contexts. Linear filtering methods originate in stochastic processes, while subspace methods have largely been based on developments in numerical linear algebra and matrix approximation theory.

This book bridges the gap between these two classes of methods by showing how the ideas behind subspace methods can be incorporated into traditional linear filtering. In the context of subspace methods, the enhancement problem can then be seen as a classical linear filter design problem. This means that various solutions can more easily be compared and their performance bounded and assessed in terms of noise reduction and speech distortion. The book shows how various filter designs can be obtained in this framework, including the maximum SNR, Wiener, LCMV, and MVDR filters, and how these can be applied in various contexts, like in single-channel and multichannel speech enhancement, and in both the time and frequency domains.


    • First short book treating subspace approaches in a unified way for time and frequency domains, single-channel, multichannel, as well as binaural, speech enhancement.
    • Bridges the gap between optimal filtering methods and subspace approaches.
    • Includes original presentation of subspace methods from different perspectives.

    商品描述(中文翻譯)

    語音增強是信號處理中的一個經典問題,但至今仍然在很大程度上未得到解決。解決此問題的兩種傳統方法是線性濾波,例如經典的 Wiener 濾波器,以及子空間方法。這些方法通常被視為不同類別的技術,並在不同的背景下介紹。線性濾波方法起源於隨機過程,而子空間方法則主要基於數值線性代數和矩陣近似理論的發展。

    本書彌合了這兩類方法之間的差距,展示了如何將子空間方法背後的思想融入傳統的線性濾波中。在子空間方法的背景下,增強問題可以被視為一個經典的線性濾波器設計問題。這意味著各種解決方案可以更容易地進行比較,並且其性能可以在噪聲減少和語音失真方面進行界定和評估。本書展示了如何在這一框架下獲得各種濾波器設計,包括最大信噪比 (SNR)、Wiener 濾波器、LCMV 濾波器和 MVDR 濾波器,以及如何在單通道和多通道語音增強的不同背景下應用這些濾波器,並在時間和頻率域中進行應用。

    - 首本以統一方式處理子空間方法的短書,涵蓋時間和頻率域、單通道、多通道以及雙耳語音增強。
    - 彌合最佳濾波方法和子空間方法之間的差距。
    - 包含從不同角度對子空間方法的原創性介紹。

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