AI in music production. The future of music creation

When it comes to AI in music production, it’s best at co-creation, and especially, making atmospheric ambient sounds, removing unwanted background noise from podcasts and presentations, as well as changing voice, which will later be able to read text using TTS tools. Whether you are a musician, a music lover, or simply curious about the intersection of technology and creativity, this post will provide a look into the fascinating world of AI in music. Read on.

AI in music creation – table of contents:

Music on demand
AI noise reduction
Voice cloning or voice conversion?
Summary

Music on demand

Among the tools for creating music based on textual prompts or by selecting appropriate parameters: genre, tempo and tone, applications such as Mubert or Beatoven.ai are worth looking into.

Mubert produces quite nice tracks based on a simple description. Their main advantage is that they are easy to generate in the browser and can be used commercially. They will work well in elevators or business presentations created with generative AI. To get reasonably satisfactory results, however, you need to spend some time experimenting and clarifying your expectations.

To ease the search for AI music that meets your needs, you can buy pre-made tracks curated by the founders of Mubert at Mubert Business — Royalty-free music for restaurants, retail, coworkings & public spaces.

Beatoven.ai works slightly differently. Here, instead of choosing a genre of music, its tempo or style, you focus on the mood. This is because Beatoven.ai uses advanced AI music generation techniques to compose unique music to match a specific part of a movie, presentation or podcast.

Music generation starts with the selection of a musical style or genre. The next step is to upload your video or voice recording so that the artificial intelligence can match the musical theme to the finished content. The most interesting feature, however, is the possibility to mark the places where you want the mood of the generated music to change.

MuseNet and Jukebox, two research projects run by Open AI, should also be mentioned here. They attract a thriving community of musicians and programmers. As a result, the solutions used in them can be also found in many other commercial applications.

AI noise reduction

However, if you prefer to leave the making of creative music to people, it’s important to remember that AI can also act as a sound engineer or their assistant. That’s because it allows you to remove unwanted sounds from audio files.

This slightly less impressive, but very useful feature of artificial intelligence can really save a lot of time. After all, tools for reducing unwanted sounds play a big role in the creation of corporate materials, such as conference recordings, training and webinars, podcasts, or customer instructions.

AI-powered programs are simple to use and adequate for most purposes:

Magic Dust from Podcastle – it effectively reduces background sounds without affecting the quality of the recording.
Audio Studio – it is a very fast tool to remove noise and automatically set the optimal volume level.
Noise Eraser – as its name suggests, it effectively eliminates unwanted sounds from your corporate audio content. One of its greatest advantages is its availability not only on PCs, but also as mobile apps for Android and iOs platforms. This means that the recordings made with mobile devices can be processed directly on them.

With AI, you can easily remove background noise, such as crackling or rustling. The tools automatically detect and eliminate noise, so you don’t have to worry about voice distortion during sound processing, and the audio file sounds clear and professional. There is also no need to use professional microphones or hire a specialist to remove noise.

Voice cloning or voice conversion?

AI will work well if you need to:

add intros and outros to podcasts automatically,
have a single voice to read personalized ads,
create audio versions of knowledge bases, and
customize company materials for the visually impaired.

Working with voice when creating such corporate materials mainly relies on two technologies. These are:

Voice cloning – it allows you to create podcasts or training recordings by generating audio directly from texts when, for example, we don’t have constant access to a recording studio or a quiet environment.
Voice conversion – this tool enables you to change your voice in real-time or from a pre-recorded audio, allowing the presenter to deliver your words using their own voice (speech-to-speech). As a result, your brand voice remains consistent, and the entire team can produce content with a uniform sound.

For these purposes, you can use tools like Podcastle Revoice which makes it possible to create a digital copy of your own voice. An interesting option for voice modification and voice conversion using speech-to-speech technology is Resemble.ai, as well as leading tools used in the production of computer games, commercials and movies, which also allow you to create your own unique voices: Replica and Eleven Labs.

The future of music creation

Ai music apps can be helpful in generating sounds, removing noise from audio files or creating voice narrations using text-to-speech (TTS) technology. There are tools like Mubert or Beatoven.ai that let you create music based on textual prompts or mood. Artificial intelligence can also serve as an audio engineer. Research projects such as MuseNet and Jukebox are contributing to the development of AI in music.

For now, generative AI is poor at creating harmonious and well-structured long musical forms. Looking beyond its current use cases, what are the potential benefits of incorporating AI in music for businesses, and how else can this innovative technology be leveraged? As we look ahead, we can only imagine the numerous surprises that the next few years of AI development will bring.

You’ve just learned more about AI in music. Read also: 3 awesome AI writers you must try out today.

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Author: Robert Whitney
JavaScript expert and instructor who coaches IT departments. His main goal is to up-level team productivity by teaching others how to effectively cooperate while coding.
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