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

每种文化都有音乐。它将人们凝聚在一起，塑造了社会。音乐影响我们的感受，利用大脑的愉悦回路。在英国，核心音乐产业每年为经济贡献35亿GB(英国音乐2012)，有3000万人参加音乐会和音乐节(英国音乐2017)。听音乐在商店、电影、仪式、现场演出、手机等地方很普遍。音乐对健康和幸福很重要。正如跨党派议会艺术、健康和福利小组2017年的一份报告所表明的那样，“艺术可以帮助我们保持健康，帮助我们恢复健康，让我们活得更长。艺术可以帮助应对健康和社会护理面临的主要挑战：老龄化、长期疾病、孤独和心理健康。艺术可以帮助节省医疗服务和社会护理方面的资金。”英国有六分之一的人患有听力损失，随着人口老龄化，这个数字还会增加。听力较差会让人更难欣赏音乐。挑选歌词或旋律更难；如果声音实际上听不到，音乐家创作几乎听不到的音符的兴奋感就会消失，随着高频的消失，音乐变得更加沉闷。这有可能脱离音乐，并失去音乐带来的健康和福祉好处。我们需要将音乐个性化，这样它才能更好地为听力受损的人服务。我们将考虑：1.为现场活动或多轨录音处理和混音桌面提要。在云中或在消费设备上处理立体录音。由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上进行复制。对于(1)，每种乐器都有单独的音轨，可以更好地控制声音的处理方式。随着未来基于对象的音频格式允许这种方法，这是及时的。(2)因为我们消耗大量录制的音乐，所以需要这样做。与依靠助听器改善声音相比，对音乐进行预处理更有效率和效果，因为这允许更复杂的信号处理。(3)重要的是，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%的助听器用户在使用助听器听音乐时存在一些困难。助听器的研究主要集中在语音上，而对音乐的研究相对较少。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方面的最新技术可能会给听力受损的人带来音乐革命。要做到这一点，我们需要更多的研究人员来考虑听力损失，而这可以通过一系列信号处理挑战来实现。这样的比赛是一种经过验证的加速研究的技术，包括发展一个将他们的技能和知识应用到问题领域的协作社区。我们将开发运行挑战所需的工具、数据库和客观模型。这将降低目前阻碍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，以及每个测试对象的听力特征，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的人如何感知音乐的音频质量。这样的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的人混合和处理音乐的新方法，一个随时可以进行进一步研究的试验台，更好地理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听音乐人群的听力。

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.