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

每种文化都有音乐。它将人们聚集在一起，塑造社会。音乐影响我们的感觉，进入大脑的快乐回路。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音乐节（UK music 2017）。在商店、电影、仪式、现场演出、手机等场所，听音乐是很普遍的。音乐对健康和幸福很重要。正如2017年跨党派议会艺术、健康和福利小组的一份报告所表明的那样，“艺术可以帮助我们保持健康，帮助我们康复，并支持更长寿、更美好的生活。”艺术可以帮助应对健康和社会护理面临的重大挑战：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关怀方面的资金。“在英国，每6个人中就有1人有听力损失，随着人口老龄化，这个数字还会增加。”听力较差的人更难欣赏音乐。挑选歌词或旋律更加困难；如果一个音乐家创造出一个几乎听不见的音符，那么他的兴奋感就会消失，而随着高频的消失，音乐也会变得沉闷。这可能会让他们脱离音乐，失去音乐带来的健康和福祉。我们需要个性化音乐，让它更适合听力损失的人。我们将考虑：1。处理和混音台馈送现场事件或多轨录音。在云端或消费者设备上处理立体声录音。通过助听器麦克风接收到的音乐处理。对于(1)和(2)，音乐可以直接播放到助听器或耳机中进行回放。对于(1)，为每种乐器提供单独的音轨可以更好地控制声音的处理方式。这与未来允许这种方法的基于对象的音频格式是及时的。(2)是需要的，因为我们消费了大量的录制音乐。对音乐进行预处理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)很重要，因为助听器是许多现场音乐的解决方案。但是，美国人权协会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遇到了一些困难。助听器的研究主要集中在语言上，而音乐听力相对被忽视了。音频信号处理是一个非常活跃和快速发展的研究领域，但通常没有考虑到那些听力损失。信号处理和机器学习方面的最新技术可能会彻底改变听障人士的音乐。为了实现这一目标，我们需要更多的研究人员来考虑听力损失，这可以通过一系列的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，包括发展一个将他们的技能和知识应用于问题领域的协作社区。我们将开发应对挑战所需的工具、数据库和客观模型。这将降低目前阻止许多研究人员考虑听力损失的障碍。数据将包括真实的人如何感知音频质量的听力测试结果，以及每个测试对象的听力能力特征，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质。这些数据和工具将允许研究人员开发新的算法。科学遗产将是为听力损失的人混合和处理音乐的新方法，一个容易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到整个人群听音乐的听力能力。

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.