Can sound quality be measured?

文章探讨了音频设计领域中过于依赖测量而忽视聆听的问题,指出电子学和扬声器测量与音乐的真实声音体验关系有限。作者通过实例说明,尽管测量技术在某些情况下能改善声音质量,但聆听才是最终决定声音是否“好听”的关键因素。讨论了不同类型的测量与实际声音品质之间的不一致性,并引用了与作家 Brent Butterworth 的对话,强调了测量揭示扬声器问题时可能被忽视的耳朵的主观感受,以及一些表现不佳的扬声器在主观评价中仍然可以听起来不错的情况。
  by Steve Guttenberg October 1, 2011 10:16 AM PDT

Is this what good sound looks like?

(Credit: Brent Butterworth)

I've met a lot of audio designers in my time, and all of the best ones have one thing in common, they have great "ears." They know what good sound sounds like. The opposite camp is populated with engineers that rely exclusively on measurements to "prove" their designs are better. To my way of thinking, the second group rarely makes great sounding products. Audio is too complex to be analyzed with just numbers alone.

Nowadays I'm meeting more digital audio engineers specializing in designing room and speaker correction software. They are usually very nice people, and their graphs and tests always look impressive on their laptops, but the presentations fall apart when I listen to their sound. Results vary from not bad to truly horrendous, but great sound is the least likely end product of their hard work.

Apparently, they were so focused on measuring sound they forgot to listen, or hire someone who actually knows what good sound sounds like. If the goal was to achieve better measurements I'd congratulate them for their accomplishments. But it's not, and discovering exactly what types of measurements indicate improved sound quality is an art. An art few of these engineers have mastered.

There are a couple of reasons why measurements fail to correlate with subjective sound quality assessments. First, electronics and speaker measurements have little to do with the sound of music. Test tones are too simple and predictable; music is far more complex and random. Reproducing the sound of a violin or a drum kit are exceedingly difficult tasks, and since the real goal of any hi-fi is to play music and not test tones, the designer's first priority should be making products that sound "good" for the intended market. For example, if you're designing DJ headphones, you aren't trying to deliver the most accurate bass. Far from it, you want to pump up the bass. Amplifier designers shouldn't waste their time trying to create an amp to drive simple test loads, they need to make an amp that handles the complexities associated with real speakers playing music. And amplifier designers don't know which speakers are going to be used with their amps. Every speaker presents a different type of "load" to the amp.

In the 1970s there was a big push to lower the "total harmonic distortion" specifications of amplifiers to ever lower levels, based on the belief that if they measured better they would sound better. They were measuring the wrong things. The narrow focus of designing for measurements rarely produces bona-fide sonic improvements. That's not to say that in the hands of truly talented engineers measurements aren't useful, they absolutely are, but measurements are no substitute for listening.

I've heard all of the latest auto setup and calibration systems featured in Denon, Onkyo, Pioneer, Sony, and Yamaha receivers, and the results are hit or miss. Granted, these systems can improve the sound, but more often than not, the processing merely changes the sound. In those cases, I can achieve better results by listening and making adjustments with the manual speaker setup. The processed sound might measure better, but again, that doesn't necessarily correlate to sound quality enhancement.

I recently discussed the measurement quandary with my friend, writer Brent Butterworth, who believes measurements are useful tools, but we never came to a meeting of minds on this matter. I'm paraphrasing here, but he said that measurements that reveal flaws in the sound of a speaker might go unnoticed by the ear, and that some speakers that don't measure well, can still sound subjectively good. So there you have it.

So if trained engineers struggle to derive useful information from measurements, I can't imagine how consumers looking at a wiggly line on a chart helps them decide which amp or speaker to buy. I'm not referring to specifications or numbers like watts per channel or driver sizes; I'm talking about charts and graphs that plot "spectrum of 1 kHz sinewave, D.C. to 1 kHz," or "anechoic response of tweeter on axis." If you have ever picked up useful information from peering at charts in audio reviews, and you're not an engineer, please share your insights in the Comments section.

在数据处理领域,测量值(measured value)和归一化值(normalized value)是两个关键概念。测量值通常是指从原始数据中直接获取的数值,例如传感器读数、图像像素强度、文本中的词频等。这些数值可能具有不同的量纲和范围,因此在进行进一步分析或建模之前,通常需要对它们进行标准化或归一化处理。 归一化值是指经过某种变换后,将原始测量值缩放到一个特定范围(如 [0, 1] 或 [-1, 1])或使其具有特定统计特性(如均值为 0、方差为 1)的值。归一化的目的在于消除不同特征之间的量纲差异,从而提高模型训练的稳定性和收敛速度。常见的归一化方法包括最小-最大归一化(Min-Max Normalization)和 Z-Score 标准化(Z-Score Standardization)[^1]。 在一些应用场景中,尤其是当数据质量不是主要关注点时,可以忽略质量描述符(quality descriptor),仅使用测量值和归一化值进行分析。例如,在图像处理中,区域提议和特征计算的时间可以分摊到所有类别上,从而提升整体计算效率。此时,归一化后的特征矩阵通常用于后续的分类或检测任务,例如在目标检测中使用支持向量机(SVM)进行分类,特征矩阵和 SVM 权重矩阵之间的点积计算成为主要的类相关计算任务[^2]。 以下是一个简单的 Python 示例,展示如何对测量值进行最小-最大归一化: ```python import numpy as np def min_max_normalization(data): min_val = np.min(data) max_val = np.max(data) normalized_data = (data - min_val) / (max_val - min_val) return normalized_data # 示例测量值 measured_values = np.array([10, 20, 30, 40, 50]) normalized_values = min_max_normalization(measured_values) print("Measured Values:", measured_values) print("Normalized Values:", normalized_values) ``` 上述代码将输入的测量值归一化到 [0, 1] 范围内。在实际应用中,归一化方法的选择取决于数据的分布特性以及模型的需求。
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