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Global Generative AI in Music Market: By Component (Software, Services); By Type (GANs, AR-CNNs, Transformer-based Models); By Application (Post-Music Mastering, Composition of Music, Streaming Music, Making of New Sound, Others); By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East, and Africa) Global Industry Analysis, COVID-19 Impact, and Industry Forecast, 2018-2032.

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Market Overview/Outlook (2022 to 2032)

The global market for Generative AI in Music is expected to grow between 2023 and 2032 due to factors like creativity and innovation, automated composition, personalization and customization, and collaboration with human artists.

The Global Generative AI in Music Market was valued at USD 0.30 Billion in 2022 and is projected to reach USD 2.70 Billion by 2032, registering a CAGR of 28.6% for the forecast period 2023-2032.

Global Generative AI in Music Market Drivers
  • Creativity and Innovation: Generative AI has enabled musicians and composers to delve into unconventional and experimental musical styles that might have been difficult to achieve through traditional methods. This, in turn, has fostered creativity by breaking away from established norms. With the ability to create entirely new and novel compositions, generative AI systems spark innovation by introducing fresh musical ideas, structures, and arrangements that can inspire artists to explore uncharted territory. AI algorithms assist in the songwriting process by providing creative suggestions, generating musical motifs, and offering alternative chord progressions.
  • Automated Composition: Generative AI is a technology that can automatically compose music, providing musicians and composers with a quick way to generate musical ideas and motifs. This not only saves time during the initial stages of music creation but also allows artists to concentrate on refining and expanding upon the generated content. Automated composition is a powerful tool for generating inspiration and new musical ideas. AI algorithms can produce compositions with unique patterns, harmonies, and structures, encouraging human composers to explore creative directions they may not have considered before.
  • Personalization and Customization: Music platforms can take advantage of generative algorithms to offer recommendations beyond basic genre or artist suggestions. Generative AI analyzes detailed aspects, such as tempo, instrumentation, and emotional tone, to provide recommendations that closely match each user's unique musical preferences. With generative AI, it becomes possible to create personalized music compositions that cater to individual users. Users can input their specific preferences, such as preferred instruments, styles, or moods, and the AI generates music that fits those preferences.
  • Collaboration with Human Artists: Collaborations with human rights initiatives can highlight the significance of conserving and advancing cultural diversity in music. Generative AI can be utilized to explore and incorporate a wide range of musical traditions, thus helping to preserve cultural heritage. Human rights values often stress inclusivity and representation. Generative AI in music can contribute to a more comprehensive musical landscape by producing and promoting works that reflect a variety of perspectives and voices.
Global Generative AI in Music Market – Merger and Acquisitions
  • Apple Has Bought a Startup That Uses AI to Make Music to Fit Your Mood (2022)
Apple Inc. acquired a startup called AI Music that uses artificial intelligence to generate tailor-made music, according to a person with knowledge of the matter, adding technology that could be used across its slate of audio offerings. The purchase of AI Music, a London-based business founded in 2016, was completed in recent weeks. The company had about two dozen employees before the deal.
  • Voicemod acquires music tech and AI outfit Voctro Labs (2021)
Voicemod, a Valencia-headquartered AI voice augmentation company, announced yesterday it had bought its Barcelona-based audio tech peer Voctro Labs. Voctro Labs' four-person team will join the Voicemod operation with immediate effect, taking charge of the latter's R&D department. The merger will integrate the two products, which both use machine learning to automatically modulate live mic streams, targeting content creators, livestreamers and video platforms.
  • Songtradr Expands Its B2b Music Technology Solutions - Acquires Leading Advanced Ai Search Company, Musicube (2023)
Songtradr, the world's leading B2B music company, announced today the acquisition of leading AI metadata and music search company, Musicube. The transaction expands the company's portfolio of tech-enabled music solutions designed to license the right music for any content, driving higher ROI and enabling brands to measurably engage their target audience. Using neural networks and proprietary artificial intelligence, Musicube's semantic search has reached product leadership in both quality and data depth.
  • Spotify is acquiring Sonantic, (2021)
Streaming giant Spotify, most famous for its music and podcast streaming services targeting consumers, is making an acquisition of a voice AI company to expand its reach in audio technology opening the door not just to building more functionality on Spotify itself, but other potential business opportunities elsewhere, too. Today, the company announced that it is acquiring Sonantic, a London-based startup that has built an AI engine to create very realistic-sounding, yet simulated, human voices from text.
  • SoundCloud buys AI that claims to predict hit songs (2022)
SoundCloud has acquired audio AI company Musiio, which makes tech that can “listen” to new music and purportedly identify the hits. The acquisition, announced Tuesday, is meant to help SoundCloud sort through its immense library of amateur music and will “become core to SoundCloud’s discovery experience,” the company said in a statement.
Challenges Impacting the Global Generative AI in Music Market
  • Quality and Authenticity Concerns: There are concerns that generative AI may not be able to replicate the emotional depth and nuanced artistic expression that is intrinsic to human-created music. The subjective and complex nature of artistic expression could prove challenging for AI models to capture authentically. Critics argue that AI-generated music might not be truly creative, instead just mimicking existing patterns from training data. This raise concerns that AI-generated compositions may lack the originality and innovative thinking that are associated with human creativity.
  • Ethical and Bias Issues: Generative AI models, which are trained on biased or culturally specific datasets, can produce music that reflects and perpetuates cultural biases and stereotypes unintentionally. This may result in ethically questionable or offensive content. If the training data for such models lacks diversity, the generated music may not represent a wide range of musical styles, genres, or cultural influences effectively. This limitation poses a threat to the inclusivity of AI-generated music. The algorithms employed in generative AI may demonstrate biases that exist in the training data, leading to uneven representation and unfair treatment of certain musical styles or demographics.
  • Intellectual Property and Copyright Challenges: Copyright laws typically require a human author for creative works. However, with generative AI, the absence of a human composer complicates the application of existing copyright frameworks. This raises questions about the eligibility of AI-generated music for copyright protection. AI-generated music can be considered a derivative work, and understanding the boundaries of fair use and the extent to which AI-generated content can be considered transformative under copyright law is a legal gray area. Additionally, determining the duration of protection for AI-generated works may require legal clarification, as it doesn't fit traditional copyright timelines that have a limited duration after which the work enters the public domain.
  • Data Privacy Concerns: User-generated content, such as personal music compositions and preferences, can be used to train generative AI models. However, this raises concerns about privacy if the data is not handled securely or if users are not aware of how their information is used. Without clear consent mechanisms and transparency about data usage, user trust can be undermined. Users may not want their musical preferences, creations, or personal data to be utilized for AI training without their explicit understanding and agreement.
Attributes Details
Market Size in 2022 USD 0.30 Billion
Market Forecast in 2032 USD 2.70 Billion
Compound Annual Growth Rate (CAGR) 28.6 %
Unit Revenue (USD Million) and Volume (Kilo Tons)
Segmentation By Component, By Type, By Application & By Region
By Component
  • Software
  • Services
By Type
  • GANs
  • AR-CNNs
  • Transformer-based Models
By Application
  • Post-Music Mastering
  • Composition of Music
  • Streaming Music
  • Making of New Sound
  • Others
By Region
  • North America: U.S and Canada
  • Europe: Germany, Italy, Russia, U.K, Spain, France, Rest of Europe
  • APAC: China, Australia, Japan, India, South Korea, South East Asia, Rest of Asia Pacific
  • Latin America: Brazil, Argentina, Chile
  • The Middle East And Africa: South Africa, GCC, Rest of MEA
Base Year 2022
Historical Year 2018 - 2022
Forecast Year 2023 - 2032
Category-wise Analysis:

By Component:

  • Software: Generative AI software has been developed to simplify the process of creating music. Providers of generative AI models can analyze the various elements of a music piece, such as lyrics, melody, and rhythm, and use that analysis to enhance the overall sound quality. Popular software tools for generating music using generative AI techniques include Amper Music, AIVA, Soundful, Boomy, Open AI, and Ecrett Music.
  • Services: Generative AI music solutions offer unique opportunities to music industries and can provide them with a competitive advantage in the market. Companies in this field prioritize delivering fast and accurate solutions to their customers, while also aiming to improve their technical capabilities to solve even more problems with music-generation tools. This focus on technical advancement is the primary factor driving growth in this segment.

By Type:

  • GANs: The GAN synthesizers are capable of generating complete audio content much faster than real-time on a modern GPU, and nearly 50,000 times faster than a standard WaveNet. This breakthrough technology has the potential to revolutionize the music industry since it provides 90% more accuracy than traditional methods for creating music clips.
  • AR-CNNs: Technologies have the ability to enhance the user's experience by overlaying generated music content onto their real-world environment. This can be achieved through AR-CNNs, where CNNs analyze and process audio and visual inputs for augmented musical experiences. AR-CNNs can be applied to create interactive music visualizations in augmented reality. This might involve mapping generated music patterns or elements to visual representations in real-time, providing users with a multisensory experience. AR-CNNs can also be used to integrate spatial audio technologies into augmented reality music applications. This would involve creating music that dynamically adapts to the user's location and movement in physical space.
  • Transformer-based Models: Transformer-based models, such as the GPT (Generative Pre-trained Transformer) series, are excellent in sequence modeling. In music generation, capturing complex patterns and structures is crucial. Transformer architectures are effective in learning long-range dependencies, making them particularly useful in this context. Transformers are known for their ability to capture contextual information, which is important in music as it helps to understand the relationships between different musical elements like notes, chords, and melodies. This contextual understanding contributes to more coherent and musically meaningful compositions. Music often involves dependencies and patterns that span long sequences.
Regional Analysis:

North America:
  • United States: The United States is a global center for technological innovation, particularly in Silicon Valley and other tech-centric regions, where many AI research institutions, startups, and technology companies are located. This innovation ecosystem contributes to the development of generative AI technologies. The US is home to some of the world's leading research institutions and universities specializing in artificial intelligence, which drive cutting-edge research in AI, including its applications in the music industry.
  • Canada: Canada is home to several research and innovation centers that focus on artificial intelligence (AI), including those involved in music technology. Collaborations between academia, research institutions, and industry players can promote innovation in generative AI for music applications. The Canadian government has shown support for AI research and development through various initiatives and funding programs. This support can encourage companies and startups in Canada to explore and invest in generative AI for music.
Asia Pacific:
  • China: China has a huge population and an increasing number of internet users. This large user base provides a significant market for applications related to generative AI in music, including music composition, recommendation systems, and interactive music experiences. The country has a thriving digital entertainment industry, including online streaming platforms, gaming, and multimedia content. Generative AI in music can be useful in creating adaptive soundtracks for games, personalized music recommendations, and enhancing the overall digital entertainment experience.
  • India: India boasts a thriving technology industry with a large number of skilled engineers, data scientists, and researchers. Such a robust talent pool plays a crucial role in fostering innovation in artificial intelligence, particularly in generative AI applied to music. India is quick to adopt emerging technologies and as generative AI continues to gain prominence globally, Indian businesses and developers are in a unique position to actively contribute to the development of AI applications in the music industry. India's rich startup ecosystem is home to many startups that focus on AI and music technology.
  • Southeast Asia: The Southeast Asian region is home to a large population of young, tech-savvy individuals who are passionate about music and technology. This demographic could potentially increase the demand for innovative applications of AI in the music creation and consumption process. Southeast Asia is renowned for its cultural diversity, which includes a rich tapestry of musical traditions. Generative AI can be utilized to explore and integrate diverse musical styles, catering to the varied tastes and preferences in the region.
  • Western Europe: Innovative solutions can result from collaboration between Western European AI researchers, tech companies, and the music industry. These kinds of partnerships could lead to the incorporation of generative AI into streaming services, software for creating music, and other applications. The powerful and well-established music industries are located in Western Europe. Major record labels, recording studios, and well-known musicians foster an environment that is favorable to the music industry's adoption of cutting-edge technologies like generative AI.
  • Eastern Europe: Eastern Europe may see a strong cultural appreciation of music, which could lead to a demand for cutting-edge technologies that improve music production and consumption. This need can be met by generative AI in music, which provides original and imaginative solutions. The market share can be greatly impacted by companies' and artists' willingness to use generative AI tools for music production, composition, and even live performances. These technologies have the potential to play a significant role in the music business if they are accepted and shown to be beneficial.
Latin America:
  • Brazil: The presence of innovative technology startups and entrepreneurs in Brazil could lead to the development of generative AI tools and platforms for music production. Brazil has a rich musical culture, and a strong cultural inclination towards incorporating technology into music production could drive demand and adoption of generative AI tools. Government initiatives and policies that support research and development in AI, especially in the creative industries, are likely to contribute to the growth of the generative AI market in the music market.
  • Mexico: Collaboration between academia and industry, as well as partnerships between technology and music companies, could accelerate the development and adoption of generative AI in the music market. Government initiatives, funding, and policies that support the growth of AI technology and the music industry can create an enabling environment for the development of generative AI in music.
Middle East and Africa:
  • Middle East: Generative AI in music enables the creation of original and unique songs. In regions with rich cultural heritage, such as the Middle East, artists and musicians can draw inspiration from traditional music, and generative AI can help create innovative blends of traditional and contemporary sounds. Helpful. Generative AI can analyze individual tastes and create personalized music experiences. This level of customization is likely to resonate with  Middle Eastern audiences, who have a wide variety of musical tastes.
  • Africa: Africa's music industry continues to grow, and there is a potential market for generative AI applications in music composition, production, and performance. If local companies and startups tap into this potential, it could lead to a significant share of generative AI in the music market. Investments in education and research initiatives related to AI and music technology can further strengthen Africa’s presence in the music generation AI  market. Supporting institutions focused on AI applications in music can lead to the development and advancement of expertise.
  • Australia and New Zealand: Australia and New Zealand both have vibrant music industries with rich cultural heritages. The presence of a strong music industry is likely to increase the demand for innovative technologies such as generative AI to improve the music creation and production process. Cities such as Sydney, Melbourne, Auckland and Wellington have become technology hubs, fostering the growth of start-ups and technology companies. Startups specializing in generative AI for music in these regions could contribute to  significant market share.

List of Prominent Players:
  • Shutterstock Inc.
  • Aiva Technologies SARL.
  • Soundful
  • Ecrett music
  • Boomy Corporation
  • OpenAI
  • Amadeus Code
  • Others
Segmentation of Global Generative AI in Music Market:

By Component:
  • Software
  • Services
By Type:
  • GANs
  • AR-CNNs
  • Transformer-based Models 
By Application:
  • Post-Music Mastering
  • Composition of Music
  • Streaming Music
  • Making of New Sound
  • Others
By Region:
  • North America (U.S., Canada, Mexico)
  • Europe (Germany, France, UK, Italy, Spain, Rest of Europe)
  • Asia Pacific (China, Japan, India, Southeast Asia, Rest of APAC)
  • Latin America (Brazil, Argentina, Rest of Latin America)
  • Middle East & Africa (GCC Countries, UAE, Rest of MEA)
No Methodology
No Available