Perception and Automated Assessment of Recorded Audio Quality, Especially User Generated Content

录制音频质量（尤其是用户生成内容）的感知和自动评估

• 批准号: EP/J013013/1
• 负责人: Trevor Cox
• 金额: $ 58.23万
• 依托单位: University of Salford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FJ013013%2F1
• 关键词: Perception; Automated; Assessment; Recorded; Audio

Many of us now carry around technologies which allow us to record sound, whether that is the sound of our child's first music concert on a digital camera or a recording of a practical joke on a mobile phone. Nowadays, there are many outlets for this user generated content. Last year alone, 13 million hours of video was uploaded to YouTube. Even professional broadcasters rely on this footage. Mainstream news bulletins regularly use amateur footage of dramatic events (e.g. Concorde crashing) and some TV programmes such as Rude Tube are entirely made up of user generated content.However, the production quality of the sound on user-generated content is often very poor: distorted, noisy, with garbled speech or indistinct music. Our interest lies in the causes of the poor recording, especially what happens between the sound source and the electronic signal emerging from the microphone. Typical problems include: speaking off microphone; distorted speech due to clipping; wind noise and microphone handling noise. We are interested in audio recorded on its own, as well as soundtracks accompanying videos.We want to improve the recording quality so that more user-generated audio can be widely used and re-used creatively. To do this we will develop an understanding of how recording errors are perceived as it is unclear how noise and distortion affects the perception of the audio quality for many sounds. We will develop algorithms for automatically evaluating audio quality from the poor recording.A method for evaluating recorded audio quality has many potential uses. When media is received by a broadcast organisation, whether submitted by an amateur or professional, a rapid quality assessment could determine whether the sound is of broadcast quality without time consuming auditioning. Searching for sounds on the Internet for creative re-use is a frustrating activity as it is difficult to find recordings, and those that are found are often of poor quality. An audio quality assessment method would make it possible to tag and search sound files for content and quality.Even better, it would be possible to use the audio quality rating at the time of recording to try and improve the quality of the captured sound. A simple warning displayed on the recording device would give an opportunity to correct mistakes (a warning light when someone is being recorded off-mic). Furthermore, a rating of audio quality could be used to produce devices which automatically correct common recording errors. The medium term aim of this research is to develop such algorithms to correct common recording errors, however, a pre-requisite is a method by which the quality of audio can be evaluated. And so that is the focus of this proposed project.

我们中的许多人现在随身携带着允许我们记录声音的技术，无论是我们孩子用数码相机举行的第一场音乐会的声音，还是用手机录制的恶作剧的声音。如今，这种用户生成的内容有很多渠道。仅去年一年，就有1300万小时的视频被上传到YouTube上。即使是专业的广播员也依赖于这段视频。主流新闻简报经常使用戏剧性事件的业余镜头(如协和式坠机)，一些电视节目，如Rough Tube，完全由用户生成的内容组成。但是，用户生成的内容的声音制作质量往往非常差：失真、嘈杂、讲话含混或音乐模糊。我们感兴趣的是录音不好的原因，特别是声源和麦克风发出的电子信号之间发生了什么。典型的问题包括：在麦克风外讲话；由于剪裁而导致的语音失真；风噪声和麦克风操作噪声。我们对自己录制的音频以及伴随视频的配乐感兴趣。我们希望提高录制质量，让更多用户生成的音频能够被广泛使用和创造性地重复使用。为此，我们将了解录音错误是如何被感知的，因为噪声和失真如何影响对许多声音的音频质量的感知尚不清楚。我们将开发从劣质录音中自动评估音频质量的算法。评估录制音频质量的方法具有许多潜在用途。当广播机构接收媒体时，无论是由业余人员还是专业人员提交，快速质量评估都可以确定声音是否具有广播质量，而不需要耗时的试听。在互联网上搜索声音以便创造性地重复使用是一项令人沮丧的活动，因为很难找到录音，而且找到的录音质量往往很差。音频质量评估方法将使标记和搜索声音文件的内容和质量成为可能。甚至更好的是，可以使用录制时的音频质量评级来尝试和改进捕获的声音的质量。录音设备上显示的一个简单警告将提供纠正错误的机会(当有人在没有麦克风的情况下被录音时，一个警示灯)。此外，可以使用音频质量评级来生产自动纠正常见记录错误的设备。这项研究的中期目标是开发这样的算法来纠正常见的记录错误，然而，先决条件是一种可以用来评估音频质量的方法。这就是这个拟议项目的重点。

项目成果

Perception and automated assessment of audio quality in user generated content: An improved model

用户生成内容中音频质量的感知和自动评估：改进的模型

• DOI: 10.1109/qomex.2016.7498974
• 发表时间: 2016
• 作者: Fazenda B

Perceived Audio Quality of Sounds Degraded by Nonlinear Distortions and Single-Ended Assessment Using HASQI

因非线性失真而降低的声音感知音频质量以及使用 HASQI 的单端评估

• DOI: 10.17743/jaes.2015.0068
• 发表时间: 2015
• 期刊: Journal of the Audio Engineering Society
• 影响因子: 1.4
• 作者: Kendrick P

Using blind signal processing algorithms to remove wind noise from environmental noise assessments: A wind turbine amplitude modulation case study

使用盲信号处理算法从环境噪声评估中消除风噪声：风力涡轮机调幅案例研究

• DOI: 10.1121/1.4933451
• 发表时间: 2015
• 期刊: The Journal of the Acoustical Society of America
• 作者: Kendrick P

Microphone Handling Noise: Measurements of Perceptual Threshold and Effects on Audio Quality.

• DOI: 10.1371/journal.pone.0140256
• 发表时间: 2015
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Kendrick P;Jackson IR;Fazenda BM;Cox TJ;Li FF
• 通讯作者: Li FF

Robustness and Prediction Accuracy of Machine Learning for Objective Visual Quality Assessment

用于客观视觉质量评估的机器学习的鲁棒性和预测准确性

• DOI: 10.21427/16sm-xn12
• 发表时间: 2014
• 作者: Hines A