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相关概念视频

Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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Perceiving Loudness, Pitch, and Location01:21

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

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fMRI Mapping of Brain Activity Associated with the Vocal Production of Consonant and Dissonant Intervals
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基于NLP的音乐处理用于作曲家分类.

Somrudee Deepaisarn1, Sirawit Chokphantavee2, Sorawit Chokphantavee2

  • 1Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12120, Thailand. s.deepaisarn@gmail.com.

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概括

这项研究引入了一种新的音乐作曲家分类方法,使用自然语言处理技术,如SentencePiece和Word2vec. 音乐词/子词向量的标准偏差被证明是非常有效的,获得了完美的作曲家分类分数.

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科学领域:

  • 数字音乐处理 数字音乐处理
  • 计算音乐学 计算音乐学
  • 机器学习 机器学习

背景情况:

  • 根据作曲家分类音乐是很困难的,因为灵活的音乐结构和主观的解释.
  • 现有的方法可能无法完全捕捉音乐创作的细微差别.
  • 使用来自MIDI和音频来源的虚拟钢琴音乐数据.

研究的目的:

  • 开发一种创新的方法来使用自然语言处理 (NLP) 技术来表示音乐作品.
  • 探索使用SentencePiece和Word2vec来创建音乐词/子词向量.
  • 评估这种代表方案对作曲家分类的有效性.

主要方法:

  • 利用音高和持续时间作为关键的音乐特征.
  • 应用了SentencePiece和Word2vec来表示旋律作为音乐单词/子词向量.
  • 采用k-最近邻居,随机森林,后勤回归,支向量机器和多层感知子进行分类.
  • 多种功能提取方法,分类算法和音乐窗口大小.

主要成果:

  • 分类性能受到特征提取方法的显著影响.
  • 音乐词/子词向量标准偏差成为最有效的特征.
  • 在作曲家分类中获得了1.00的高F1分数.
  • 在测试的分类模型中没有发现显著的性能差异.

结论:

  • 提出的基于NLP的方法有效地代表了作曲家识别的音乐作品.
  • 音乐词/子词向量标准偏差是这个任务的一个强大的功能.
  • 该方法显示了数字音乐处理和音乐信息检索的巨大潜力.