ai人工智能的本质和未来
Artificial Intelligence is a subject that sparks the imagination — and splits opinion. Proponents point to the problem-solving potential of machine learning, and AI’s ability to make many laborious and time-consuming tasks a thing of the past. Others worry about the effect it may have on everything from the labour market to the existential survival of the human race. One thing everyone can agree on is that whether good or bad, the effects of AI will be wide, deep and probably irreversible.
人工智能是一门激发想象力并引起意见分歧的学科。 支持者指出了机器学习解决问题的潜力，以及AI完成许多繁琐且耗时的任务的能力已成为过去。 其他人担心它可能对从劳动力市场到人类生存的一切事物产生影响。 每个人都可以同意的一件事是，无论好坏，人工智能的影响将是广泛的，深刻的，并且可能是不可逆的。
The music industry has been subject to the same debate for some time. Some see AI as an exciting new tool that will make business simpler and more secure, as well as opening up hitherto unimagined creative possibilities. Others fear it could kill the careers of musicians, composers, and many of the small businesses that make up the ecosystem of the music industry. Again, where this venn diagram of opinion meets is that change is inevitable.
音乐行业一直受到同样的争论。 有些人将AI视为一种令人兴奋的新工具，它将使业务变得更简单，更安全，并开拓前所未有的创新可能性。 其他人担心它会杀死音乐家，作曲家以及组成音乐产业生态系统的许多小型企业的职业。 同样，维恩观点图遇到的地方是变化是不可避免的。
In this report we have looked at where that change is likely to manifest (or is already having an impact), in order to try and plot a pathway through territory that remains largely uncharted. In doing so we will bear in mind what Drew Silverstein, CEO of Amper Music, told Music Ally last year, “If I were a larger business focused on content creation — musical or non-musical — I would have at the top of my priority list ‘How do we engage with AI music?’ You can partner, build, buy or do nothing. But the worst thing you can do is to do nothing.”
在本报告中，我们研究了这种变化可能在何处显现(或已经产生影响)，以试图绘制出一条在很大程度上仍未知的领域的途径。 这样一来，我们就会牢记Amper Music首席执行官Drew Silverstein去年对Music Ally所说的话：“如果我是一家专注于内容创作(音乐或非音乐)的大型企业，我将当务之急。列表“我们如何与AI音乐互动？” 您可以合作，建立，购买或不做任何事情。 但是最糟糕的事情是什么都不做。”
Music composition
音乐创作
The effect of AI on the creation of music is probably the most frequently misunderstood topic that we’ll be looking at. The biggest sceptics of machine-generated music paint a bleak picture of songs created to order by robots, devoid of human input or emotion. However, the current landscape is very different.
人工智能对音乐创作的影响可能是我们将要探讨的最经常被误解的话题。 机器产生的音乐的最大怀疑者描绘了由机器人定购的歌曲的惨淡景象，没有人的输入或情感。 但是，当前的情况非常不同。
While AI programs are able to produce fully composed songs, even the most enthusiastic advocate would admit that they’ve been something of a mixed bag so far. As with any AI program, the calibre of the output depends very much on the calibre of the input. For a program to ‘learn’ how to compose a piece of music, it must be fed a huge amount of data, generally in the form of existing, human-written songs. And while it can use this data to recognise what constitutes a song in terms of structure, arrangement, dynamics, instrumentation, etc., and even how different combinations of these will result in different types of song, it has no way of quantifying whether the results will be pleasing to human ears (unsurprising when musical taste is so subjective anyway). There is also the thorny issue of how ownership comes into play when machine-generated music is derived from human-written content, which is something we’ll come to later.
尽管AI程序能够产生完整的歌曲，但即使是最热情的倡导者也承认，到目前为止，它们一直是一个混血儿。 与任何AI程序一样，输出的口径很大程度上取决于输入的口径。 对于要“学习”音乐创作方法的程序，必须以通常以现有的人工创作歌曲的形式提供大量数据。 尽管它可以使用这些数据来识别歌曲的结构，排列，动力学，乐器等等，甚至这些歌曲的不同组合如何导致不同类型的歌曲，但它无法量化结果将令人耳悦目(无论如何，当音乐品味如此主观时就不足为奇了)。 还有一个棘手的问题，那就是当机器生成的音乐是从人工编写的内容派生而来时，所有权如何发挥作用，这将在后面介绍。
As it stands, the most successful forays into AI music come when technology has been used to ‘collaborate’ with musicians, rather than replace them. Examples range from the band Yacht feeding an AI their entire back catalogue in order to generate new material, to the artist Holly Herndon building her own AI which was able to ‘harmonise’ with her on a critically acclaimed album and tour.
就目前而言，人工智能技术最成功的尝试是使用技术与音乐家“合作”而不是取代音乐家。 例子包括乐队Yacht为AI提供整个AI目录以生成新素材，再到艺术家Holly Herndon构建自己的AI，从而能够与她在一张备受赞誉的专辑和巡回演唱中“和谐”。
Even brands have gotten involved. Last year Glaceau Smartwater sponsored a collaboration between Toro Y Moi and AI startup Endel, which resulted in four tracks named after Smartwater products, while LG worked with Amper Music and Australian artist Betty Who to create “the most motivational song ever” after analysing hundreds of motivational songs and playlists. The result was performed at the LG-sponsored Color Run event in Los Angeles.
甚至品牌也参与其中。 去年，Glaceau Smartwater赞助了Toro Y Moi与AI创业公司Endel的合作，该合作产生了四首以Smartwater产品命名的曲目 ，而LG与Amper Music和澳大利亚艺术家Betty Who合作，分析了数百种音乐之后创作了“有史以来最具动力的歌曲”励志歌曲和播放列表。 结果是在LG赞助的洛杉矶Color Run活动中进行的。
Many artists and programmers also share excitement about the possibility of using AI to create entirely new forms of music. As Stephen Phillips, CEO of AI company Popgun, believes, “AI shouldn’t just compose original music. It should sound completely different. It should be pleasurable, but also ‘Holy hell! Like nothing I’ve ever heard before!’”. While AI may never be able to make artistic and creative decisions in the way we understand them, it certainly has the capacity to facilitate and inspire human artists to make bold and groundbreaking ones.
许多艺术家和程序员也对使用AI创造全新音乐形式的可能性感到兴奋。 正如AI公司Popgun首席执行官Stephen Phillips所相信的那样，“ AI不应只是创作原创音乐。 听起来应该完全不同。 它应该是令人愉快的，但也应该是'神圣的地狱！ 就像我从未听过的一样！'”。 尽管AI可能永远无法以我们理解它们的方式做出艺术和创意决定，但它肯定有能力促进和激发人类艺术家做出大胆而开创性的决定。
Whether by accident or design, the way AI interprets musical data can also throw up interesting results. See SKYGGE, aka French artist Benoit Carré, who fed the program Flow-Machines with French pop from the ’80s and sculpted the feedback into the 50-minute record ‘Hello World’, which is claimed to be the first fully AI-assisted album. Last year he followed it up with ‘Black is the Colour’, which swapped the French pop database for traditional American folk songs. In both cases the results bear the obvious mark of the source material, yet warped into fresh and unexpected forms.
无论是偶然还是设计，AI解释音乐数据的方式也可能带来有趣的结果。 参见SKYGGE，又名法国艺术家BenoitCarré，他用80年代的法国流行音乐为Flow-Machines程序提供节目，并将反馈雕刻到50分钟的唱片《 Hello World》中，据称这是第一张完全由AI辅助的专辑。 去年，他推出了“ Black is the Colour”，将法国的流行数据库替换为传统的美国民间歌曲。 在这两种情况下，结果均带有原始材料的明显标记，但会变形为新鲜且出乎意料的形式。
However, Carré’s creative input into the music cannot be understated. As compelling as the idea is that you could feed The Beatles’ back catalogue into an AI program and be instantly rewarded with ‘new’ Lennon and McCartney compositions, so far such attempts have failed to produce convincing lyrics, let alone music. (Similar experiments by the Elon Musk-backed OpenAI have proved interesting rather than enjoyable, as well as potentially raising a raft of legal questions.)
但是，Carré对音乐的创造性投入不可低估。 这个想法令人信服，因为您可以将甲壳虫乐队的后继目录输入AI程序，并立即获得“新的”列侬和麦卡特尼作曲家的奖励，到目前为止，此类尝试未能产生令人信服的歌词 ，更不用说音乐了。 (由埃隆·马斯克(Elon Musk)支持的OpenAI进行的类似实验被证明是有趣而不是令人愉快的，并且有可能引发一系列法律问题。)
Elsewhere, AI companies from Boomy to Humtap have appeared in order to help ‘democratise’ songwriting, using tools that can turn a hummed melody or tapped rhythm into a fully-fledged song. The recent inaugural AI Song Contest also showed that music created using Artificial Intelligence is becoming more and more sophisticated. However, the bottom line remains that for the foreseeable future, human input and judgement is very much required to put the ‘art’ into artificial intelligence.
在其他地方，出现了从Boomy到Humtap的AI公司，以帮助“民主化”歌曲创作，其使用的工具可以将嗡嗡作响的旋律或轻拍的节奏转变成一首成熟的歌曲。 最近的首届AI歌曲大赛也表明，使用人工智能创作的音乐正在变得越来越复杂。 然而，最重要的是，在可预见的未来，将“艺术”转化为人工智能非常需要人类的投入和判断。
Music production
音乐制作
While the impact of AI on music composition might seem somewhat nebulous, the effect it’s made on music production is more on the practical side — less about reinventing the wheel than smoothing it down.
虽然AI对音乐创作的影响似乎有些模糊，但它对音乐制作的影响更多是在实践方面–与其说是重新发明轮子，不如说是降低轮子的平滑度。
For example, by analysing a large dataset of recordings, an AI studio tool can work out the best compression settings for a variety of different genres, or help speed up particularly repetitive or laborious studio processes.
例如，通过分析大量的录音数据集，AI Studio工具可以为各种不同类型的音频确定最佳的压缩设置，或帮助加快特别重复或费力的工作室流程。
Even more helpful for many musicians has been the development of AI programs such as LANDR, which makes the subtle, complex and often expensive process of mastering a track accessible to anyone, while constantly adjusting and improving its own algorithms. While such programs may not be able to compete with top-of-the-range mastering or studio engineers just yet, the ability to turn demo-quality recordings into professional-sounding tracks at the touch of a button will not only help musicians save money but also potentially allow composers to turn around broadcast-quality music more quickly.
对许多音乐家来说，更有益的是开发诸如LANDR之类的AI程序，该程序使任何人都可以访问精妙，复杂且通常昂贵的母带制作过程，同时不断调整和改进自己的算法。 尽管此类程序可能尚无法与顶级母带或录音室工程师竞争，但只需按一下按钮就可以将演示质量的录音转换成专业音轨，这不仅可以帮助音乐家节省资金而且还可能允许作曲家更快地处理广播质量的音乐。
Music for Sync
同步音乐
If AI is bringing down the cost of producing music, then it follows that it will likely bring down the cost of production music. In fact, of all the doomsday scenarios proffered by AI sceptics, the prediction that it will destroy the livelihoods of artists who compose for music libraries is perhaps the likeliest to come to pass. Indeed, AI companies have wasted no time venturing into that territory.
如果AI降低了音乐制作成本，那么它很可能会降低音乐制作成本。 实际上，在AI怀疑论者提出的所有世界末日场景中，关于它会破坏为音乐库创作的艺术家的生计的预测也许最有可能通过。 的确，人工智能公司没有浪费任何时间进入这一领域。
While AI music libraries may not be quite ready to challenge traditional music libraries in terms of quality or variety, their ultra low overheads are likely to drive down fees for royalty-free production music. Indeed, many of the companies to have made moves into this space, such as Mubert, Escrett and Evoke Music, have explicitly targeted low budget markets, from indie videogame and app developers to YouTubers who need cheap background music. The technology of companies such as Scored and MatchTune (who recently partnered with production library BMG), also automatically shapes music to match what’s happening on-screen. (Having access to BMG’s 15,000 tracks should also improve MatchTune’s dataset, potentially increasing the quality of its own AI-generated output.)
尽管AI音乐库可能尚未准备好在质量或种类上挑战传统音乐库，但其超低的管理费用可能会降低免版税制作音乐的费用。 确实，许多已经涉足这一领域的公司，例如Mubert，Escrett和Evoke Music，都明确瞄准了低预算市场，从独立的视频游戏和应用程序开发人员到需要廉价背景音乐的YouTubers。 诸如Scored和MatchTune之类的公司的技术(最近与生产库BMG合作)也自动调整音乐的形状以匹配屏幕上发生的事情。 (能够访问BMG的15,000条音轨还可以改善MatchTune的数据集，从而有可能提高其AI生成的输出的质量。)
However, the biggest impact AI has made on music libraries so far has been on functionality. Whereas traditional music libraries have relied on humans tagging tracks correctly, AI will increasingly be able to fill in track metadata far more quickly. Moreover, a good AI with a vast dataset will be able to analyse each track without human subjectivity, theoretically making it more accurate and thus returning better search results. As Amadeus Code CEO Taishi Fukuyama puts it, “we’ll learn what kinds of keywords people are searching for, and recommend music that’s in our database… All you’d have to say is ‘My YouTube channel is about sport, camaraderie, leadership and competition’ and it would understand that, and show whatever we have available.”
然而，到目前为止，人工智能对音乐库的最大影响在于功能。 传统音乐库依靠人类正确标记曲目，而AI将越来越能够更快地填写曲目元数据。 此外，具有大量数据集的良好AI可以在没有人类主观性的情况下分析每条轨道，从理论上讲使其更准确，从而返回更好的搜索结果。 正如Amadeus代码首席执行官福山大史(Taishi Fukuyama)所说：“我们将了解人们正在搜索哪些类型的关键字，并推荐我们数据库中的音乐……您所要说的是：“我的YouTube频道涉及体育，友情，领导力和竞争”，它就会理解这一点，并展示我们所拥有的一切。”
Copyright
版权
As AI makes inroads into music licensing, it raises some thorny questions around intellectual copyright and creative ownership. For a start, to ‘train’ an AI to make music, you first need to feed it a vast amount of music, and this often means copying endless songs so the program can read them and learn from them. However, while this is considered ‘fair usage’ in many territories such as the USA, Japan and China, the act of copying for commercial purposes requires permission in Europe, where rightsholders have the option of opting out from such usage.
随着AI进入音乐许可领域，它引发了一些有关知识产权和创意所有权的棘手问题。 首先，要“训练”人工智能来制作音乐，首先需要向其提供大量音乐，这通常意味着复制无尽的歌曲，以便程序可以读取它们并从中学习。 但是，尽管在许多地区(例如美国，日本和中国)将其视为“合理使用”，但出于商业目的的复制行为需要欧洲的许可，欧洲的权利人可以选择不使用这种使用。
Then there is the question of who a computer-generated piece of music belongs to. Is it the person who input the data? The programmer of the AI who made it possible? Or the many, many musicians whose music would’ve been in the dataset used to train that AI? While apps such as Boomy, which non-musicians can use to create and distribute music using their AI, allows those users to either earn a cut of streaming royalties or buy the copyright of their finished tracks outright, the fact remains that technology is changing far too quickly for copyright law to keep up.
还有一个问题是，计算机生成的音乐属于谁。 是输入数据的人吗？ AI的程序员使之成为可能？ 还是很多很多音乐人的音乐本来可以用来训练该AI的音乐人？ 非音乐人可以使用其应用程序通过诸如Boomy之类的应用程序来创建和发布音乐，而这些应用程序可以使这些用户获得流媒体版税或直接购买其已完成曲目的版权，但事实是，技术正在发生巨大变化太快了，版权法跟不上。
Sophie Goosens, of law firm Reed Smith, explains further: “If you look at the history of copyright, it was invented in a world of scarcity, where only a handful of people could access the means of production. In a world where making music can happen at the push of a button, the volume of songs created might challenge other copyright concepts, including the concept of ‘originality’ which is indispensable for copyright protection to exist, at least as far as European copyright is concerned.”
里德·史密斯(Reed Smith)律师事务所的索菲·古森(Sophie Goosens)进一步解释说：“如果您看看版权的历史，它是在一个稀缺的世界中发明的，那里只有少数人可以使用生产资料。 在一个只需按一下按钮就可以制作音乐的世界中，创作的歌曲数量可能会挑战其他版权概念，包括“原创性”概念，这对于版权保护的存在是必不可少的，至少就欧洲版权而言关心。”
Ironically, AI has also made great waves in targeting copyright infringement. Companies like Pex monitor the Internet for rights violations using audio and video fingerprinting algorithms, which are able to identify tracks even if the media is compressed, cropped, recorded in a noisy environment, or otherwise modified. Pex also recently bought Dubset (formerly The Future FM), an online mixed-audio distribution platform that allows DJs to upload, host and share their mixes, podcasts, etc; assuring them that the underlying rightsholders sampled within the mix will be paid royalties for their works.
具有讽刺意味的是，人工智能在打击版权侵权方面也掀起了波澜。 像Pex这样的公司使用音频和视频指纹算法来监视Internet是否侵犯版权，即使在压缩，裁剪，在嘈杂的环境中记录或以其他方式修改媒体的情况下，该算法也可以识别曲目。 Pex最近还收购了在线混合音频分发平台Dubset (以前称为The Future FM)，该平台允许DJ上载，托管和共享其混音，播客等。 确保他们从混合中取样的潜在权利人将获得其作品的版税。
Even here there are issues though. Sometimes fingerprinting algorithms attribute royalty-free samples purchased on sites such as Loopmasters to a certain artist, and therefore distribute royalties to that single artist by mistake. During the Covid-19 livestreaming boom a related issue has cropped up when the algorithm falsely flags an infringement during a DJ set on platforms such as Facebook and Instagram, and automatically stops the video connection.
即使在这里也有问题。 有时，指纹算法会将在Loopmasters等网站上购买的免版税样本归因于某位艺术家，因此错误地将版权费分配给了该位艺术家 。 在Covid-19实时流媒体热潮期间，当算法在Facebook和Instagram等平台上进行DJ设置时错误地标记侵权并自动停止视频连接时，一个相关的问题就浮出水面了。
Another potential issue concerns deepfakes (super-realistic videos, photos, or audio falsified through sophisticated AI). Roc Nation recently filed takedown requests of fake, AI-created clips of JAY-Z rapping Shakespeare soliloquies, although again the law remains murky.
另一个潜在的问题涉及深层造假(通过复杂的AI伪造的超真实视频，照片或音频)。 洛克国家(Roc Nation)最近提出了删除请求，要求伪造由AI创建的JAY-Z莎士比亚独奏片段，尽管该法律仍然含糊不清 。
The Future
未来
Other notable applications of music in AI include Spotify’s personalised playlists, Deezer’s AI-enabled ability to spot and label explicit content, ‘adaptive music’ apps that generate music depending on the listener’s location and local weather, and A&Rs using AI to identify potential rising stars. All of these offer a clue as to what opportunities AI may open up next.
音乐在AI中的其他显着应用包括Spotify的个性化播放列表，Deezer的AI支持的功能来发现和标记显式内容，根据听众的位置和当地天气生成音乐的“自适应音乐”应用以及使用AI识别潜在的后起之秀的A＆R 。 所有这些都为AI接下来可能带来什么机会提供了线索。
For instance, regarding personalisation, if it’s possible for listeners to influence the music they’re listening to, it stands to reason that music can also influence listeners. You can already target music fans based on their listening habits, but if certain tempos, harmonies and even frequencies can provoke different reactions in the human brain, can music be automatically generated to match physical activity, body clocks, or even to encourage spending? Or play tracks that mention certain brands, products or activities?
例如，关于个性化，如果听众有可能影响他们正在听的音乐，则可以推断音乐也可以影响听众。 您已经可以根据他们的收听习惯来确定音乐迷的目标，但是如果某些节奏，和声甚至频率可以在人脑中引起不同的React，那么音乐是否可以自动生成以匹配身体活动，人体时钟甚至鼓励消费？ 还是播放提及某些品牌，产品或活动的曲目？
AI is also being used to identify artists who already appeal to consumers of particular brands, which can inform sync, sponsorship and strategy decisions. This will only get more sophisticated, and potentially even more automated, as the technology (and the data) develops.
人工智能还被用于识别已经吸引了特定品牌消费者的艺术家，这些艺术家可以为同步，赞助和战略决策提供信息。 随着技术(和数据)的发展，这只会变得更加复杂，甚至可能更加自动化。
As that happens, we may start to see AI have an impact on the transactional side of sync too. If the underlying data is strong enough, AI programs may allow for a more automated process, where tracks could be licensed immediately for a variety of purposes, with a variable fee that is paid straight to the creator. This may not be something to get too excited about just yet (we’re still waiting for the promised new dawn of Blockchain after all…) but it shows the breadth of possibility.
发生这种情况时，我们可能会开始看到AI也对同步的事务性方面产生影响。 如果基础数据足够强大，则AI程序可以允许更自动化的过程，在这种情况下，可以立即出于各种目的对曲目进行许可，并直接向创作者支付可变费用。 也许这还不是一件令人兴奋的事情(毕竟，我们仍在等待承诺的区块链的新曙光……)，但这表明了可能性的广度。
Finally, AI has the potential to not just produce our pop music, but the performers themselves. Yamaha’s Vocaloid technology has already helped to bring legendary Japanese singer Hibari Misora ‘back to life’, but it’s also creating new personalities such as the computer-generated influencer turned pop star Lil Miquela. Even more ambitious is the Auxuman stable of fully formed AI artists being conceived by artist Ash Koosha, who sees this as the start of not so much an incursion into the existing music landscape, but perhaps the creation of a whole new one:
最后，人工智能不仅有潜力制作我们的流行音乐，而且还有表演者本身的潜力。 雅马哈的Vocaloid技术已经帮助日本传奇歌手Hibari Misora 复活 ，但它也创造了新的个性，例如由计算机生成的有影响力的流行歌手Lil Miquela 。 更雄心勃勃的是由艺术家Ash Koosha构想的，由完全成型的AI艺术家组成的Auxuman稳定室，他认为这并不是侵入现有音乐领域的开端，而是可能是一个全新的创造：
“We focus on experiential content. And we’re focusing on Generation Alpha, the next generation, who are just growing up, and who are going to be more comfortable in being introduced to characters who are not human. We think the next social media is like a game world. We’re starting by becoming very good at building these characters, but the future might be these large-scale simulated worlds. And then these people are going to be there, and you can visit them from time to time, and make friends. Maybe that’s the Netflix of the future! Although yes, it’s still far-fetched for now, so we are working only on great digital talent. As people start to understand their influence and communicate with them, we will start to build the case for that world.”
“我们专注于体验内容。 我们关注的是下一代Alpha一代，他们刚刚长大，他们将更容易被介绍给非人类角色。 我们认为下一个社交媒体就像一个游戏世界。 我们从非常擅长构建这些角色开始，但是未来可能是这些大规模的模拟世界。 然后这些人将在那里，您可以不时拜访他们并结交朋友。 也许这就是未来的Netflix！ 尽管是的，但它现在仍然遥不可及，因此我们仅致力于出色的数字人才。 当人们开始理解他们的影响力并与他们沟通时，我们将开始为这个世界建立理由。”
Far-fetched perhaps, but as AI programs and datasets continue to improve, then its scope may expand even further than the current limits of our imagination. For now it’s already providing a helping hand to both music makers and music users, and perhaps that’s the best way of looking at AI — as a potential assistant, not a potential competitor.
也许牵强附会，但随着AI程序和数据集的不断完善，其范围可能会超出我们当前想象的极限。 目前，它已经为音乐制作者和音乐用户提供了帮助，也许这是看AI的最佳方式-作为潜在的助手，而不是潜在的竞争对手。
Author: Kier Wiater Carnihan
作者：Kier Wiater Carnihan
Visit record-play.net for more music industry insights.
访问record-play.net以获取更多音乐行业见解。
翻译自: https://medium.com/swlh/how-ai-is-shaping-the-future-of-the-music-industry-f10d31f53be3
ai人工智能的本质和未来