基于染色子频段提取的第五的轨迹 - 音乐表示,分析和分类的新方法
概括
这项研究引入了一种新的音频音乐分析方法,使用第五的轨迹. 该技术从音频信号中提取有价值的和声信息,用于音乐分类.
科学领域:
- 音乐信息检索 音乐信息检索
- 数字信号处理 数字信号处理
- 音乐学 音乐学 音乐学
背景情况:
- 传统的音乐分析通常依赖于象征形式,如MIDI.
- 将分析概念从象征性领域调整到音频领域具有挑战.
- 五度的轨迹在基于MIDI的音乐分析中显示出了希望.
研究的目的:
- 开发和验证一种用于分析音乐音频记录的新方法.
- 为了适应第五的轨迹概念的音频信号处理.
- 评估拟议方法在音乐分类中的实用性.
主要方法:
- 实施了音乐音频的短期光谱分析.
- 将光谱时间框架映射到基于音调类强度的五分之一的签名上.
- 计算特征点来创建五分之一的轨迹.
- 利用了8996个音频音乐作品的数据集,跨越10个类型.
主要成果:
- 五度的轨迹有效地捕获来自音频的和结构信息.
- 从音频轨迹中获得的特征系数与基于MIDI的方法可比.
- 该方法显示了在音频音乐分类中使用的潜力.
结论:
- 拟议的第五度曲线轨迹方法是音频音乐分析的一个可行的方法.
- 这种技术为音乐信息检索任务提供了有价值的和特征.
- 这些发现支持这种方法在自动化音乐分类系统中的应用.
相关概念视频
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations
884
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
884
Classification of Signals
374
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...
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...
374
Discrete Fourier Transform
205
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
205
Harmonic Mean
3.1K
The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
3.1K
Linear Approximation in Frequency Domain
84
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
84
Relative Frequency Histogram
5.4K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.4K


