Introduction

AI Music is a London-based company that uses artificial intelligence (AI) to transform the music industry. The company provides a platform that uses machine learning algorithms to create original music compositions for various applications, such as advertising, gaming, film, and television. In this case study, we will explore how AI Music is leveraging AI to revolutionize the music creation process and deliver high-quality music compositions that meet the needs of different industries.

Challenge

The traditional music creation process is often time-consuming, expensive, and limited by human creativity. The music industry faces significant challenges in delivering original, high-quality music compositions that meet the unique needs of different applications. Additionally, music creators and producers must navigate copyright issues and legal regulations, which further complicates the music creation process. To overcome these challenges, AI Music needed to develop a platform that could use AI to generate original music compositions that meet the needs of different industries while adhering to copyright and legal requirements.

1. Time-consuming and expensive: The traditional music creation process can be time-consuming and expensive, involving several steps, such as composing, recording, producing, and mastering. Additionally, the costs associated with hiring music producers, composers, and sound engineers can be high, making it difficult for small businesses or independent artists to create high-quality music.
2. Limited by human creativity: Human creativity is subjective and limited, which can make it challenging to create music compositions that meet the specific needs of different industries. Moreover, music creators and producers can have personal biases and preferences, which can result in music compositions that do not align with the unique needs of different industries.
3. Copyright and legal regulations: The music industry is heavily regulated, and music creators and producers must navigate copyright issues and legal regulations when creating music compositions. This can be challenging, as copyright laws vary from country to country, and music creators and producers must ensure that their music compositions are entirely original and free from infringement.

To overcome these challenges, AI Music developed a platform that uses AI to generate original music compositions that meet the specific needs of different industries. By using machine learning algorithms to analyze various parameters, such as mood, tempo, and genre, the platform can create music compositions that fit the unique needs of different industries. Additionally, the platform can generate music that adheres to copyright and legal requirements, making it easier for music creators and producers to create high-quality music compositions that meet the unique needs of different applications.

UX Research Methods

Surveys

AI Music used the SurveyMonkey online survey tool to create their surveys. They created two separate surveys, one targeting music industry professionals and the other targeting potential clients. The music industry professionals survey included questions about music creation pain points, preferences, and trends, as well as questions about how they currently create music compositions and what they look for in a music creation platform. The potential client survey included questions about music needs for different industries, such as advertising, gaming, and film, as well as questions about music preferences and what they look for in a music creation platform.

AI Music distributed the surveys via email to music industry professionals and potential clients who had signed up for their newsletter or expressed interest in their services. They also posted the survey links on their social media pages to reach a wider audience. To encourage participation, they offered respondents the chance to win a gift card to a popular music store.

Once the survey responses were collected, AI Music analyzed the data using Excel and Google Sheets. They identified common pain points, preferences, and trends, and used this information to inform the development of their AI music creation platform. They also used the survey data to create targeted marketing campaigns for different industries based on their music needs and preferences.

User Interviews for our platform

Objective:The objective of the interviews is to gain feedback from music producers and composers on AI Music's music UI engine. The feedback will be used to identify pain points, areas for improvement, and overall satisfaction with the product.

Method:One-on-one interviews will be conducted with music producers and composers who have experience using music creation software. The interviews will be conducted in-person or via video conferencing, depending on the location of the participants. Each interview will last approximately 30-60 minutes.

Participants:Participants will be recruited through social media channels, music industry forums, and personal networks. Participants will be required to have at least 2 years of experience using music creation software and must have produced music for at least one commercial project.

Interview Guide:The interview guide will consist of the following questions:

1. Can you tell us about your experience using the AI Music music UI engine?
2. What do you like about the music UI engine?
3. What are some areas for improvement?
4. How would you rate the overall user experience of the music UI engine?
5. What features would you like to see added to the music UI engine?
6. How does the music UI engine compare to other music creation software you have used?
7. What are your thoughts on the usability of the music UI engine?
8. Are there any pain points you have experienced while using the music UI engine?
9. Would you recommend the music UI engine to other music producers and composers?
10. Is there anything else you would like to share about your experience using the music UI engine?

Data Collection and Analysis:The interviews will be recorded with consent from the participants and transcribed for analysis. The data collected from the interviews will be analyzed for common pain points, areas for improvement, and overall satisfaction with the product. The results will be used to make improvements to the music UI engine and inform future product development.

Participants:

• John Smith: Music producer with 5 years of experience using music creation software
• Sarah Lee: Composer with 3 years of experience using music creation software
• David Kim: Music producer with 8 years of experience using music creation software

Common Pain Points:

• Limited customization options for sound effects and instrument presets
• Difficulty in finding specific sound effects or instruments
• Lack of a built-in tutorial or help section
• Limited compatibility with third-party plugins and virtual instruments

Areas for Improvement:

• Add more customization options for sound effects and instrument presets
• Improve the search function for finding specific sound effects or instruments
• Add a built-in tutorial or help section for new users
• Improve compatibility with third-party plugins and virtual instruments

Overall Satisfaction:

• John and Sarah rated their overall satisfaction with the music UI engine as 8/10
• David rated his overall satisfaction as 6/10 due to the limited customization options and compatibility issues

Features:

• John and Sarah would like to see more options for percussion instruments and sound effects
• David would like to see more customization options for virtual instruments and the ability to import custom sound effects

Usability:

• John and Sarah found the music UI engine easy to use and navigate
• David found the music UI engine slightly difficult to use and suggested the addition of a more advanced user mode

Recommendation:

• All participants would recommend the music UI engine to other music producers and composers

These results were used by AI Music to improve the music UI engine by adding more customization options and improving the search function for sound effects and instruments. They also added a built-in tutorial and help section and worked on improving compatibility with third-party plugins and virtual instruments. The improvements resulted in increased user satisfaction and a more efficient music creation process.

Data Analytics

AI Music used analytics to make design decisions:

Objective:

The objective was to use analytics to gain insights into user behavior and preferences when using AI Music's music creation platform. The insights were used to make design decisions to improve the user experience and increase user engagement.

Method:

AI Music used Mixpanel, a product analytics tool, to track user behavior on their music creation platform. They tracked user activity, such as how long users spent on the platform, which features they used, and how they navigated the platform. They also tracked user preferences, such as which instruments and sound effects were most commonly used.

Data Collection and Analysis:AI Music analyzed the data collected from Mixpanel to gain insights into user behavior and preferences. They found that users spent the most time on the platform when using the virtual drum kit feature and that the most commonly used sound effect was a snare drum. They also found that users often struggled to find specific sound effects and instruments, leading to frustration and decreased engagement.

Design Decisions:Based on these insights, AI Music made the following design decisions to improve the user experience and increase user engagement:

• Made the virtual drum kit feature more prominent on the platform to encourage users to spend more time using it
• Improved the search function for sound effects and instruments to make it easier for users to find what they need
• Added more options for snare drums and other commonly used sound effects to improve user satisfaction

These design decisions resulted in increased user engagement and satisfaction with the platform. The analytics data was continuously monitored and analyzed to identify further opportunities for improvement, ensuring that the platform remained user-focused and effective in meeting the needs of music producers and composers.

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Conclusion

Working closely with the product design team, I helped create a seamless user interface that was intuitive and easy to navigate. My input was essential in developing new features and improving existing ones based on user feedback and analytics data.

Together with other talented individuals, I helped AI Music gain significant traction in the music industry, and our platform became popular among music producers and composers worldwide. Our hard work and innovation eventually led to the acquisition of AI Music by Apple.

The acquisition was a significant milestone for AI Music, and it was a testament to the value of our platform and the potential it had to revolutionize the music industry. I was thrilled to be a part of it and excited to see how our technology would integrate with Apple's suite of music production tools.

It was an incredible feeling to know that my work helped create a platform that transformed the music creation process and helped AI Music become a leading player in the music industry. Being part of the team that made the acquisition by Apple possible was a tremendous honor, and I am grateful for the opportunity to have contributed to this exciting chapter in the history of music production.

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