Ia et Traitement audio

The impact of AI in the field of mastering and mixing

The integration of artificial intelligence (AI) in the field of sound processing has opened new perspectives and generated many discussions. AI algorithms are capable of analyzing large amounts of audio data. Indeed they are able to learn and recognize the characteristics of the sound to improve the sound quality. However, it is essential to understand the advantages, limitations and implications of this technology. In this article, we will explore the role of AI and unravel the myths and realities surrounding it. We will also discuss the benefits and limitations of AI-assisted audio processing.

Understanding AI in the service of sound: How algorithms improve sound quality?

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First, artificial intelligence (AI) in mastering represents a major breakthrough in the audio industry. Using sophisticated algorithms, the AI analyzes large amounts of audio data, recognizes key sound characteristics and optimizes the sound.

For example, an algorithm can detect the dominant good or bad frequencies of a track and equalize the sound accordingly. The AI can also adjust the different characteristics to achieve a target LUFS level.

Additionally, the AI can also automatically adjust dynamics to avoid saturation or unwanted peaks.

This powerful tool therefore allows sound engineers to improve sound quality effectively.

This advance, although imperfect, proves valuable when the sound engineer has the knowledge to select the best solutions proposed by the AI.

Myths and facts

The integration of artificial intelligence in mastering has raised questions and misconceptions.

It is therefore essential to dispel these myths and understand the reality of this technology.

Contrary to some beliefs, AI does not replace the sound engineer, but rather complements their expertise.

AI, as a powerful tool, speeds up the audio processing process and offers intelligent recommendations based on references carefully chosen by the engineer.

However, the final decision and the technical and artistic judgment remain with the sound engineer. He brings his experience and his sensitivity to obtain an optimal sound result.

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How do humans and AI work together for optimal results?

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Some AI plugins can analyze the sound content of a track and automatically suggest or apply settings.

The sound engineer will then adjust these parameters to achieve the desired sound.

Additionally, some AI plugins can also offer advanced features such as automatic noise removal, audio restoration, or even simulation of specific acoustic environments.

This combination of human expertise and AI capabilities through these audio plugins helps to optimize the mixing and/or mastering process and achieve high quality results.

Audio plugins using artificial intelligence

Mastering the mix : REFERENCE

The “Reference” plugin lives up to its name, as it allows a real-time comparison between the sound characteristics of your track and a reference chosen by the sound engineer.

This analysis tool is extremely accurate and efficient, and will allow you to adjust the frequency spectrum and dynamics of your mix to match your reference.

During the comparison, the plugin will automatically equalize the perceived level between the two titles for a reliable comparison in terms of volume.

In addition, this plugin offers the possibility to specifically analyze certain parts of the song, for example by creating a loop on the chorus.

Sonible Smart : EQ 2

This plugin leverages artificial intelligence to automatically equalize tracks, detecting and correcting frequency issues, to achieve optimal sonic balance.

This saves time in the subsequent processing process. However, it is still essential to carefully check the solutions offered by the plugin and to carry out A/B tests to ensure that the result obtained corresponds to the artistic and technical expectations.

Accusonus : ERA Bundle

This collection of plugins takes advantage of artificial intelligence to automatically eliminate unwanted noise, such as background noise, hiss or clicks. In my opinion, this is one of the best uses of AI in audio processing.

Conclusion

The integration of artificial intelligence in audio processing opens up new possibilities for sound engineers, producers and artists. Through data analysis and machine learning, AI can help improve sound quality and speed up the process of certain repetitive tasks. However, it is important to recognize that AI does not replace human expertise, but rather complements it. Human-AI collaboration helps achieve optimal results by leveraging the benefits of both approaches.

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