对于在同位噪音,同位语音和空间分离语音中识别句子的频率的重要性
Adam K Bosen1, Peter A Wasiuk2, Lauren Calandruccio3
1Boys Town National Research Hospital, Center for Hearing Research, Omaha, Nebraska 68131, USA.
The Journal of the Acoustical Society of America
|November 15, 2024
概括
频率的重要性功能根据面罩类型和空间布局而变化. 了解这些变异是改善复杂听觉环境中的语音感知的关键.
科学领域:
- 听觉感知是一种听觉感知.
- 精神声学是一种精神声学.
- 语音处理 语音处理
背景情况:
- 频率重要性函数 (FIFS) 对于理解语音感知至关重要.
- 现有的FIF对安静和稳定状态噪声条件有很好的定义.
- 对于复杂的听力环境来说,FIF的通用性仍然未被探索.
研究的目的:
- 为了研究频率的重要性如何随着不同的面具类型 (噪音与两人说话) 和空间配置 (共同位置与分离) 而变化.
- 评估频率重要性估计是否在各种审计场景中一致.
主要方法:
- 通过将来自听觉过器库的输出目标与掩盖比率与关键字识别准确度相关联,估计了频率的重要性.
- 采用了一种新的方法,避免改变目标或面罩信号的声学特性.
- 在不同的条件中比较频率的重要性:噪音中的句子,双语者面罩中的句子和空间分离的双语者面罩中的句子.
主要成果:
- 频率的重要性受到掩面类型和空间布局的显著影响.
- 高频率 (>5 kHz) 的重要性降低,而中频率 (6001900 Hz) 的重要性增加,与噪音相比,两声口罩具有较高的重要性.
- 面罩的空间分离增强了600 Hz至5 kHz之间的频率的重要性.
结论:
- 频率重要性函数不是静态的,并且适应复杂的听力条件.
- 研究结果强调了听觉处理的动态性质,以及需要特定语境的语音感知模型.
- 这项研究提供了关于听众如何在具有挑战性的声学环境中优先考虑光谱信息的关键见解.
更多相关视频
相关概念视频
Determination of Expected Frequency
2.1K
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...
2.1K
Perceiving Loudness, Pitch, and Location
195
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...
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...
195
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K
Classification of Signals
403
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...
403
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
Relative Frequency Distribution
10.5K
A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
10.5K


