noise identification

简明释义

噪音识别

英英释义

例句

1.The team conducted noise identification 噪声识别 to improve the sound quality of the new headphones.

团队进行了noise identification 噪声识别 以改善新耳机的音质。

2.During the testing phase, engineers performed noise identification 噪声识别 to determine the sources of unwanted sounds.

在测试阶段，工程师进行了noise identification 噪声识别 以确定不需要的声音来源。

3.The software uses advanced algorithms for noise identification 噪声识别 in audio recordings.

该软件使用先进的算法进行音频录音中的noise identification 噪声识别 。

4.In environmental studies, noise identification 噪声识别 helps assess the impact of urbanization on wildlife.

在环境研究中，noise identification 噪声识别 有助于评估城市化对野生动物的影响。

5.In the field of acoustics, noise identification 噪声识别 is crucial for designing quieter environments.

在声学领域，noise identification 噪声识别 对于设计更安静的环境至关重要。

作文

In today's fast-paced world, the ability to filter out irrelevant information is more critical than ever. This concept is especially relevant in various fields such as engineering, environmental science, and even social media management. One of the key processes involved in this filtering is known as noise identification. The term noise identification (噪声识别) refers to the method of distinguishing unwanted or irrelevant data from meaningful signals. Whether we are dealing with sound, visual data, or information on digital platforms, understanding how to identify noise can significantly enhance our decision-making processes.For instance, in environmental monitoring, scientists often collect vast amounts of data regarding air quality, temperature, and other factors. However, this data can be cluttered with noise, which may arise from faulty sensors, external disturbances, or even natural variations. By employing noise identification techniques, researchers can sift through the data to isolate genuine trends and anomalies. This not only improves the accuracy of their findings but also aids in formulating effective environmental policies.Similarly, in the realm of audio engineering, noise identification plays a crucial role. Sound engineers must frequently work with recordings that contain background noise, which can detract from the quality of the primary audio. By utilizing advanced software and algorithms for noise identification (噪声识别), they can remove unwanted sounds and enhance the clarity of the desired audio. This process is vital for producing high-quality music, podcasts, and other audio content.Moreover, in the digital age, social media platforms are inundated with an overwhelming amount of information. Users often find it challenging to navigate through the noise of advertisements, spam, and irrelevant posts. Here, noise identification becomes essential to curate content that is relevant and engaging. Algorithms designed for noise identification can help in filtering out undesirable content, allowing users to focus on what truly matters to them. This not only improves user experience but also increases engagement rates on these platforms.The importance of noise identification extends beyond technical applications; it also has implications for personal productivity. In our daily lives, we are bombarded with distractions that can lead to decreased focus and efficiency. By practicing noise identification, individuals can learn to recognize and eliminate distractions, enabling them to concentrate better on their tasks. This skill can be cultivated through mindfulness practices, time management techniques, and the use of technology to block distracting notifications.In conclusion, noise identification (噪声识别) is a vital process that permeates various aspects of our lives. From environmental science to audio engineering and personal productivity, the ability to distinguish between noise and meaningful information can lead to more informed decisions and improved outcomes. As we continue to navigate an increasingly complex world, honing our skills in noise identification will undoubtedly serve us well, helping us to cut through the clutter and focus on what truly matters.

在当今快节奏的世界中，过滤掉无关信息的能力比以往任何时候都更为重要。这个概念在工程、环境科学甚至社交媒体管理等多个领域尤为相关。涉及这种过滤的关键过程之一被称为噪声识别。术语噪声识别（noise identification）是指区分无用或无关数据与有意义信号的方法。无论我们处理的是声音、视觉数据还是数字平台上的信息，理解如何识别噪声可以显著提高我们的决策过程。例如，在环境监测中，科学家们常常收集大量关于空气质量、温度和其他因素的数据。然而，这些数据可能会被噪声所干扰，噪声可能来源于故障传感器、外部干扰或自然变异。通过采用噪声识别技术，研究人员可以筛选数据，从而孤立出真实的趋势和异常情况。这不仅提高了他们研究结果的准确性，还帮助制定有效的环境政策。同样，在音频工程领域，噪声识别也发挥着至关重要的作用。声音工程师经常需要处理包含背景噪声的录音，而这些噪声会影响主要音频的质量。通过利用先进的软件和算法进行噪声识别（noise identification），他们可以去除不必要的声音，提高所需音频的清晰度。这个过程对于制作高质量的音乐、播客和其他音频内容至关重要。此外，在数字时代，社交媒体平台充斥着大量的信息。用户常常发现很难在广告、垃圾邮件和无关帖子之间导航。在这里，噪声识别变得至关重要，以便策划相关且吸引人的内容。旨在进行噪声识别的算法可以帮助过滤掉不必要的内容，使用户能够专注于真正重要的事情。这不仅改善了用户体验，还提高了这些平台的参与率。噪声识别的重要性超越了技术应用；它对个人生产力也有着深远的影响。在我们的日常生活中，我们受到的干扰往往会导致注意力下降和效率降低。通过实践噪声识别，个人可以学会识别和消除干扰，从而更好地集中精力于任务。这种技能可以通过正念练习、时间管理技巧和使用技术来阻止分散注意力的通知来培养。总之，噪声识别（noise identification）是一个渗透到我们生活各个方面的重要过程。从环境科学到音频工程再到个人生产力，区分噪声和有意义信息的能力可以导致更明智的决策和更好的结果。随着我们继续在一个日益复杂的世界中航行，磨练我们的噪声识别技能无疑会对我们大有裨益，帮助我们穿透杂音，专注于真正重要的事情。