Artificial intelligence (AI) has brought a new wave of personalized music experiences with countless songs already streaming on Apple Music, Spotify, and SoundCloud. The AI and Deep Learning-based music software is getting a waiting list for new users. Also, some tools can even generate instruments from text, provide users with a starting beat or inspiration, help users edit tunes, and a lot more. 

However, computers have been involved in making music for decades. Then what has changed recently? How artificial intelligence and deep learning have changed the entire industry? In the following blog, we will discuss the concept of artificial intelligence (AI), how it is beneficial and challenging for the music industry, and what are a few top AI tools used to create music these days. 

Artificial Intelligence & Deep Learning – What are they?

Artificial intelligence (AI) refers to a branch of computer science that combines comprehensive datasets to facilitate problem-solving. It encompasses various sub-fields, such as machine learning and deep learning, which are commonly associated with artificial intelligence. Deep learning plays a key role in multiple AI applications and services, enhancing automation and enabling the execution of analytical and physical tasks without the need for human intervention. 

AI is often used to describe the project of creating systems that possess intellectual capabilities similar to those of humans, including reasoning, meaning discovery, generalization, and learning from previous experiences. 

AI systems operate by incorporating vast quantities of labeled training data, examining the data to identify correlations and patterns, and leveraging these patterns to make predictions about future conditions. AI tools are emerging in the music industry, and provide features like AI-track assistance analysis, and overall sound enhancement.    

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How AI and Deep Learning are beneficial to the music industry?

From the creation of songs and music production to marketing and distribution, AI is transforming every aspect of this cherished art form. AI and deep learning algorithms are utilized to customize suggestions, propose new music selections, and curate playlists. Further, AI is employed to enhance the quality of streaming services. For instance, AI-driven tools can identify and eliminate background noise, optimize bitrates, and minimize latency.

AI possesses a significant advantage in music creation through its capacity to analyze extensive volumes of data, enabling the identification of patterns and prediction of trends. This capability aids music producers and marketers in releasing music that is more likely to resonate with their intended audience.

In the future, Artificial intelligence may find application in the creation of virtual reality concerts and immersive experiences. Additionally, AI will continue to contribute to the advancement of novel music streaming platforms and services. AI-based tools can analyze user behavior and preferences, identify emerging trends, and offer recommendations for enhancements. Leveraging AI, music streaming platforms can enhance their service quality and provide users with a more personalized experience.

Leading companies like Spotify and Pandora have harnessed artificial intelligence to generate tailored playlists for their users. These companies also employ AI to support the promotion of new and emerging artists. Spotify, for instance, boasts a team of data scientists who utilize machine learning algorithms to suggest songs based on users’ listening habits. Apple Music, a prominent competitor to Spotify, has engaged in a fierce rivalry that has proven mutually beneficial. Both companies have amassed a significant number of paid subscribers.

What are the music generation models?

• MelodyRNN: MelodyRNN is an LSTM (Long Short-Term Memory) based recurrent neural network (RNN) model. This model comprises multiple neural network architectural configurations, allowing for the modification of pitch range in a MIDI file or the implementation of training approaches such as the aforementioned ‘attention’ technique.
  
  This tool, developed by Magenta, provides a set of commands for creating a dataset from a MIDI file. It collects melodies from each track, which helps train the model. This tool’s code is totally open-source. They trained three models from the start during the development phase, each employing a different sort of melody: jazz melodies, batch songs, and children’s songs.
  
  
• Music Transformer: Magenta also developed a model titled Music Transformer, which uses transformers to produce music. This model can generate nearly 60 seconds of audio in the form of MIDI files, surpassing LSTM-based models in terms of coherency.
  
  Unlike typical transformer approaches, where attention vectors build an absolute relationship between tokens, the attention layers in this algorithm use relative attention. This means that the model predicts the relationship between tokens based on their proximity to one another.
  
  
• MuseNet: MuseNet, an OpenAI program, produces MIDI files using transformers. These melodies can be created either from scratch or as an accompaniment to an existing melody.
  
  One major difference is that MuseNet uses full attention rather than relative attention. This allows for the creation of longer pieces of music with enhanced melodic coherence, lasting up to 4 minutes. However, it may jeopardize short-term coherence.
  
  
• MusicVAE: Moving on to MusicVAE, it utilizes a hierarchical recurrent variational autoencoder, which is a deep learning technique used for learning latent representations and generating musical scores. In the following explanation, we will delve into the various components of this architecture and provide illustrative examples. Before that, it is essential to grasp the concept of an autoencoder.

What are the challenges of AI in the music industry?

AI and deep learning in music present several challenges. The primary issue is the ethical and legal implications of artificially generated music. The question is “Who owns the copyright to music tracks generated by AI?”. Is it AI-generated music original, or it should be derivative work based on existing music? Another challenge may be that it can be utilized by bad actors and unethical players to mimic artists and use their voices in malicious ways. 

The following are a few challenges that AI might impose on the music industry:

• Loss of Human Connection: Excessive reliance on AI-generated music or virtual performances may diminish the human connection found in live music and collaborative music creation.
  
  
• Disruption of the Music Industry: AI technologies have the potential to disrupt traditional music industry roles, impacting job opportunities and altering creativity, particularly in songwriting, composing, and session musician roles.
  
  
• Lack of Human Emotion and Creativity: AI-generated music may lack the emotional depth and authentic creativity that human musicians bring to their work, potentially resulting in formulaic and predictable compositions. This could lead to a lack of diversity and innovation in the industry.

5 AI tools for producing music

• Magenta: Magenta Studio, a set of music plugins, utilizes advanced machine-learning techniques to generate music. It can function as a standalone application or as an Ableton Live plugin.
  
  
• Orb Producer Suite: Orb Producer Suite empowers producers to create melodies, basslines, and wavetable synthesizer sounds with cutting-edge technology, resulting in limitless musical patterns and loops.
  
  
• Amper: Amper requires minimal input to generate original music, catering to content creators of all kinds with unique compositions, performances, and recordings, without using pre-created material or licensed music.
  
  
• AIVA: AIVA composes emotive soundtracks for ads, video games, or movies, while also offering variations of existing songs. The app’s music engine simplifies video production by eliminating the need for music licensing.
  
  
• MuseNet: MuseNet, managed by OpenAI, generates songs with up to 10 instruments and in 15 styles. Currently, it offers AI-generated music consumption, but not the ability to create custom music.

Final thoughts

AI possesses the capacity to bring substantial changes to the music industry. Although there are numerous potential advantages of incorporating AI in music production, various challenges must be addressed. As the music industry continues to evolve, it will be fascinating to witness how AI continues to influence music creation, production, and distribution. 

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