Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

XuetangX

语音信号数字处理

Beijing Institute of Technology via XuetangX

Overview

       本课程在教学设计上主要采用课堂教学和课后实践相结合的方式,教师讲授以多媒体教学为基础,配备中英文课件以及丰富音视频资料,学生课后设计涵盖编程实践和文献调研,提升学生自主学习能力。本课程研究生教学学习过程中进行分组交流和讨论,针对课程相关技术点设计案例并建立学习小组,课堂上引导小组进行相互交流和研讨,通过课程提问、学生互相提问以及针对各自科研领域 结合课程内容进行创新性发问、批判性发问等方式,引导学生理论联系实际,并能灵活运用课程知识和各自科研领域融会贯通,通过多角度提问的形式活跃课程氛围,促进知识点的吸收和理解。 本课程引入国外一流名校相关课程先进教学经验,进一步完善课件、设计、 研讨等环节的设置,培养学生使用英语进行学习与交流的能力,包括书面与口头表达能力。课程案例以及实验内容基于开源软件,加强科研实践,培养研究生创 新意识与动手能力。 本课程充分利用已有的研究生创新平台以及语音室等软硬件资源,面向课程 相关研究生开展创新实验实践活动,安排学生在实际应用中进行体验,从用户体 验角度提出自己的问题和想法,从而提高对技术深度思考的能力。 本课程通过课堂讲授、分组研讨、专家讲座、编程实践、英文交流等全方位、 多角度的教学设计与实践,激发学生学习兴趣和主动性,培养研究生创新性、批 判性、颠覆性思维,并恰当采用多媒体、网络等现代教学技术、方法和手段,显著提高教学效果。

Syllabus

  • 1. Introduction
    • 1.1 Outline
    • 1.2 Application of digital speech signal processing
    • 1.3 Development history of speech signal processing
    • 1.4 Basic Knowledge of speech signal
    • Test
  • 2. Fundamentals of Speech processing
    • 2.1 Speech processing techniques
    • 2.2 Short-time analysis and windowing
    • 2.3 Short-time parameters of speech signal
    • 2.4 Pitch estimation
    • Test
  • 3. Speech production and perception
    • 3.1 Speech chain
    • 3.2 Speech production
    • 3.3 Speech perception
    • 3.4 Speech quality test
    • Test
  • 4. Linear predictive analysis(LPA)
    • 4.1 Basic concepts of LP
    • 4.2 Estimation of LP coefficients
    • 4.3 LP spectrum and basic features
    • 4.4 Alternative Representations of LP parameters
    • Test
  • 5. Homomorphic analysis(HA)
    • 5.1 Homomorphic systems
    • 5.2 Cepstrum and complex cepstrum
    • 5.3 Homomorphic filtering
    • 5.4 Homomorphic analysis of speech signal
    • Test
  • 6. Vector quantization(VQ)
    • 6.1 Basic concept of VQ
    • 6.2 VQ system design
    • 6.3 Codebook design algorithms
    • 6.4 Different types of VQ
    • Test
  • 7. Hidden Markov Models(HMM)
    • 7.1 Prerequisite knowledge
    • 7.2 Basic concept of HMM
    • 7.3 Three basic problems of HMM
    • 7.4 Insights of HMM
    • Test
  • 8. Speech Coding
    • 8.1 Overview of speech coding
    • 8.2 Speech coding strategies
    • 8.3 Code Excited Linear Prediction (CELP)
    • 8.4 Key technologies of speech coding
    • Test
  • 9. Speech Recognition
    • 9.1 Overview of speech recognition
    • 9.2 Basic speech recognition systems
    • 9.3 Models in speech recognition
    • 9.4 Challenges for speech recognition
    • Test
  • 10. Speech Enhancement
    • 10.1 Overview of speech enhancement
    • 10.2 Speech separation problem
    • 10.3 Monaural speech denoising
    • 10.4 Other speech enhancement techniques
    • Test
  • 11. Speech Synthesis
    • 11.1 Overview of speech synthesis
    • 11.2 Methods of speech synthesis(PartI)
    • 11.3 Methods of speech synthesis(PartII)
    • 11.4 Prosody and PSOLA
    • Test
  • 12. Latest development in speech signal processing
    • 12.1 Background of Deep Learning
    • 12.2 Outline of neural networks
    • 12.3 Deep learning based speech recognition
    • 12.4 Deep learning based speech separation
    • Test
  • Exam

    Taught by

    Jin Wang

    Tags

    Reviews

    Start your review of 语音信号数字处理

    Never Stop Learning.

    Get personalized course recommendations, track subjects and courses with reminders, and more.

    Someone learning on their laptop while sitting on the floor.