EnhanceMusic: Machine Learning Challenges to Revolutionise Music Listening for People with Hearing Loss

增强音乐：机器学习挑战彻底改变听力损失者的音乐聆听方式

• 批准号: EP/W019434/1
• 负责人: Trevor Cox
• 金额: $ 168.09万
• 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: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW019434%2F1
• 关键词: EnhanceMusic; Machine; Learning; Challenges; Revolutionise

Every culture has music. It brings people together and shapes society. Music affects how we feel, tapping into the pleasure circuits of the brain. In the UK each year, the core music industry contributes £3.5bn to the economy (UK Music 2012) with 30 million people attending concerts and festivals (UK Music 2017). Music listening is widespread in shops, movies, ceremonies, live gigs, on mobile phones, etc.Music is important to health and wellbeing. As a 2017 report by the All-Party Parliamentary Group on Arts, Health & Wellbeing demonstrates, "The arts can help keep us well, aid our recovery and support longer lives better lived. The arts can help meet major challenges facing health and social care: ageing, long-term conditions, loneliness and mental health. The arts can help save money in the health service and social care."1 on 6 people in the UK has a hearing loss, and this number will increase as the population ages (RNID). Poorer hearing makes music harder to appreciate. Picking out lyrics or melody lines is more difficult; the thrill of a musician creating a barely audible note is lost if the sound is actually inaudible, and music becomes duller as high frequencies disappear. This risks disengagement from music and the loss of the health and wellbeing benefits it creates. We need to personalise music so it works better for those with a hearing loss. We will consider:1. Processing and remixing mixing desk feeds for live events or multitrack recordings.2. Processing of stereo recordings in the cloud or on consumer devices.3. Processing of music as picked up by hearing aid microphones.For (1) and (2), the music can be broadcast directly to a hearing aid or headphones for reproduction.For (1), having access to separate tracks for each musical instrument gives greater control over how sounds are processed. This is timely with future Object-Based Audio formats allowing this approach.(2) is needed because we consume much recorded music. It's more efficient and effective to pre-process music than rely on hearing aids to improve the sound, as this allows more sophisticated signal processing.(3) is important because hearing aids are the solution for much live music. But, the AHRC Hearing Aids for Music project found that 67% of hearing-aid users had some difficulty listening to music with hearing aids. Hearing aid research has focussed mostly on speech with music listening being relatively overlooked.Audio signal processing is a very active and fast-moving area of research, but typically fails to consider those with a hearing loss. The latest techniques in signal processing and machine learning could revolutionise music for those with a hearing impairment. To achieve this we need more researchers to consider hearing loss and this can be achieved through a series of signal processing challenges. Such competitions are a proven technique for accelerating research, including growing a collaborative community who apply their skills and knowledge to a problem area.We will develop tools, databases and objective models needed to run the challenges. This will lower barriers that currently prevent many researchers from considering hearing loss. Data would include the results of listening tests into how real people perceive audio quality, along with a characterisation of each test subject's hearing ability, because the music processing needs to be personalised. We will develop new objective models to predict how people with a hearing loss perceive audio quality of music. Such data and tools will allow researchers to develop novel algorithms.The scientific legacy will be new approaches for mixing and processing music for people with a hearing loss, a test-bed that readily allows further research, better understanding of the audio quality required for music, and more audio and machine learning researchers considering the hearing abilities of the whole population for music listening.

每个文化都有音乐。它将人们聚集在一起，塑造社会。音乐会影响我们的感受，影响大脑的愉悦回路。在英国，核心音乐产业每年为经济贡献35亿英镑（UK Music 2012），有3000万人参加音乐会和节日（UK Music 2017）。听音乐在商店、电影、仪式、现场演出、移动的电话等中很普遍。音乐对健康和幸福很重要。正如2017年关于艺术，健康和福祉的所有党派议会小组的报告所表明的那样，“艺术可以帮助我们保持健康，帮助我们恢复并支持更长的生活。艺术可以帮助应对健康和社会保健面临的主要挑战：老龄化，长期条件，孤独和心理健康。艺术可以帮助节省医疗服务和社会保障的资金。在英国，每六个人中就有一个人患有听力损失，随着人口老龄化，这个数字还会增加。听力差使音乐更难欣赏。挑选歌词或旋律线是更困难的;音乐家创造一个几乎听不见的音符的兴奋失去了如果声音实际上是听不见的，音乐变得沉闷的高频消失。这可能会使人脱离音乐，失去音乐所带来的健康和福祉。我们需要个性化的音乐，以便更好地为听力损失患者服务。我们将考虑：1.处理和混音现场活动或多轨录音混音台饲料。2.在云中或消费者设备上处理立体声录音。3.助听器麦克风拾取的音乐的处理。对于（1）和（2），音乐可以直接广播到助听器或耳机进行再现。对于（1），可以访问每个乐器的单独音轨，从而可以更好地控制声音的处理方式。这是及时的，未来的基于对象的音频格式允许这种方法。(2)因为我们消费了大量的唱片。预处理音乐比依靠助听器来改善声音更有效，因为这允许更复杂的信号处理。(3)助听器是很重要的，因为助听器是许多现场音乐的解决方案。但是，AHRC音乐助听器项目发现，67%的助听器使用者在使用助听器听音乐时有困难。助听器的研究主要集中在语音上，而音乐听相对被忽视。音频信号处理是一个非常活跃和快速发展的研究领域，但通常没有考虑到听力损失的人。信号处理和机器学习的最新技术可以为听力障碍者带来音乐革命。为了实现这一目标，我们需要更多的研究人员考虑听力损失，这可以通过一系列信号处理挑战来实现。这种竞赛是一种经过验证的加速研究的技术，包括发展一个协作社区，将他们的技能和知识应用到一个问题领域。我们将开发运行挑战所需的工具，数据库和目标模型。这将降低目前阻止许多研究人员考虑听力损失的障碍。数据将包括听力测试的结果，以了解真实的人如何感知音频质量，沿着每个测试对象的听力能力，因为音乐处理需要个性化。我们将开发新的客观模型来预测听力损失患者如何感知音乐的音频质量。这些数据和工具将使研究人员能够开发新的算法。科学遗产将是为听力损失人群混合和处理音乐的新方法，一个易于进行进一步研究的测试平台，更好地了解音乐所需的音频质量，以及更多的音频和机器学习研究人员考虑整个人群听音乐的听力能力。

项目成果

A systematic review of measurements of real-world interior car noise for the "Cadenza" machine-learning project

对“Cadenza”机器学习项目的真实车内噪声测量进行系统回顾

• DOI: 10.1121/10.0019041
• 发表时间: 2023
• 期刊: The Journal of the Acoustical Society of America
• 作者: Firth J

The Clarity & Cadenza Challenges

清晰度

• DOI: 10.61782/fa.2023.0876
• 发表时间: 2024
• 作者: Akeroyd M

Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users

• DOI: 10.3389/fpsyg.2024.1310176
• 发表时间: 2024-02-21
• 期刊: FRONTIERS IN PSYCHOLOGY
• 影响因子: 3.8
• 作者: Bannister，Scott;Greasley，Alinka E.;Whitmer，William M.
• 通讯作者: Whitmer，William M.