Making music has always been a human endeavor, requiring a deep understanding of musical theory and years of practice to develop the skills necessary to compose and perform works that move and inspire others. However, with the advent of artificial intelligence, the boundaries of what is possible in music creation are rapidly changing.
One of the most exciting developments in AI music composition is the creation of algorithms capable of generating original pieces of music based on a set of inputs or a sample of existing music. These algorithms work by analyzing musical patterns and structures and using this information to create new pieces that are similar in style and form.
There are several approaches to creating AI music, including deep learning models and generative models. Deep learning algorithms such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) train on large datasets of existing music to recognize patterns and structures in the data. Once trained, these models can be used to create new pieces of music by inputting input parameters or the original melody.
Not too long ago, Google introduced the MusicLM AI model, which is already trained and capable of generating music based on a text query. This means that today it is already possible to write a request, for example, “music for dancing”, and the AI will issue a ready-made melody at your request. But not everything is so happy.
First, Google doesn’t open access to MusicLM yet. Secondly, the music generated by MusicLM is not unique. The developers noticed that part of the created composition is a piece of music on which the algorithm was “trained”.
Indeed, one of the biggest challenges in AI musical composition is to ensure the musical coherence and expressiveness of the resulting pieces. This requires the algorithm to have a deep understanding of music theory and structure, as well as the ability to generate creative and original ideas. In addition, algorithms must be able to manipulate the rhythm, melody, harmony, and phrasing of music in order to create emotionally engaging and expressive works.
As you can see, the task is not easy. But after the algorithm is trained and access to it is open, the musician can start generating new pieces of music by passing input parameters or the initial melody to the AI. Most likely it will also be possible to use the algorithm to create variations of existing fragments or to create new fragments from scratch.
In conclusion, the use of artificial intelligence in musical composition has the potential to revolutionize the way we think and create music. With the right algorithms and training data, AI can be used to create truly original and expressive pieces of music that rival those of human composers. Whether you’re an experienced composer or just starting out, AI can be a powerful tool to explore new creative directions and expand your musical horizons.
