振动是否定义了类型,还是相反? 一种新的参数方法来分析声振动
Theodora Nestorova1, Ivan Nestorov2, Joshua B Gilbert3
1Schulich School of Music, McGill University, 555 Sherbrooke St. W, H3A 1E3, Montréal, Québec, Canada; Department of Communication Disorders and Sciences/Speech-Language Pathology Program, Viterbo University, 900 Viterbo Dr., La Crosse, WI 54601.
概括
振动分析现在可以解释声乐表现的类型特定变化. 新的方法准确地区分了基于振动模式的歌剧,音乐剧和爵士歌唱风格.
科学领域:
- 声声声学和生物声学
- 音乐表演科学 音乐表演科学
- 语音和听力科学 语音和听力科学
背景情况:
- 声乐振动是一种复杂的现象,存在于各种音乐类型中.
- 传统的振动分析方法,通常基于西方古典歌剧,未能捕捉到其他风格的自然变化.
- 需要进行振动分析,考虑多种类型的规律性,可变性和稳定性.
研究的目的:
- 开发和验证一个新的系统来分析声音振动,以适应类型特定的特征.
- 研究振动声的变性作为歌剧,音乐剧和爵士歌唱风格的独特特征.
- 提出一种新的,多相振动模型,以更准确地表示.
主要方法:
- 对来自15位专业歌手的声乐录音进行声学分析,涉及三个类型 (歌剧,音乐剧,爵士乐).
- 进行了鼻状提取,调度过和光谱分析 (快速里埃变换长期平均光谱).
- 包括标准偏差,变化系数 (CV) 和回归模型在内的统计分析,随后与声学教师进行感知调查.
主要成果:
- 振动变化率,用变化系数 (CV) 量化,有效地区分音乐类型.
- 与歌剧歌手相比,音乐剧和爵士歌手的CV显著更高.
- 一个4参数逻辑回归模型证明了复杂的振动模式的更准确的表示,感知数据证实了基于振动变量的类型分类.
结论:
- 提出的感知相关的振动模型和时间变化的参数为分析复杂的振动模式提供了一种新的方法.
- 这种方法通过建立特定的规范值来支持更具包容性的语音培训.
- 这些发现有助于在教育和临床语音环境中对生态有效的振动评价.
相关概念视频
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations
948
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...
948
Double Resonance Techniques: Overview
192
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
Spin decoupling is usually achieved by...
192
Variability: Analysis
135
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
135
Classification of Signals
422
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...
422
Mass Spectrum: Interpretation
1.1K
An unknown compound can be established by identifying the molecular ion peak in the mass spectrum. The molecular ion peak is often weak or absent due to the predominance of fragmentation in high-energy electron beams. In such cases, a low-energy electron beam can be used to scan the spectrum to enhance the intensity of the molecular ion peak. Additionally, chemical ionization, field ionization, and desorption ionization spectra are used to obtain a relatively intense molecular ion peak.
To...
To...
1.1K
Determination of Expected Frequency
2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K


