2022
UX/UI
Figma / Illustrator / Photoshop
Mobile
While there are numerous applications for music recognition, none currently offer the capability to identify samples solely based on an initial scan of the song.
Provide user friendly music sample recognition app that fills the gap in existing applications by offering the unique capability to identify samples solely based on an initial scan of the song's melody.
The target audience for the project are music enthusiasts, producers, and music production students seeking advanced tools and features for sample recognition, analysis, and exploration within the realm of music production.
I conducted a product comparison by evaluating two additional music recognition apps, analyzing their functionalities, user interfaces, and target demographics to gain a better understanding of the competitive landscape.
Music Sample Recognition
Offers accurate recognition of multiple samples within a song.
Provides information about sampled music origins and connections.
Identifies complete songs but lacks specific sample recognition features.
Library Management
Allows users to easily add recognized samples to their library for organization and quick access.
Does not include built-in library management features.
Offers basic song saving and access features but lacks robust library management capabilities.
Search History
Maintains a comprehensive search history with filtering options for easy navigation.
Offers limited search history functionality without advanced filtering options.
Provides basic search history display but lacks advanced features and synchronization.
Target Audience
Designed for music enthusiasts, producers, and students seeking advanced sample recognition tools.
Appeals primarily to music enthusiasts interested in sample exploration.
Targeted towards a broad audience for identifying complete songs quickly.
User Experience
Prioritizes a sleek, intuitive interface for efficient sample recognition and management workflows.
Provides a user-friendly interface for exploring sample connections.
Known for its simple interface and quick song identification, but lacks specialized features for sample recognition.
To gain a comprehensive understanding of user pain points, needs, and desires, I conducted interviews with 10 electronic music enthusiasts aged 20-35. These interviews provided valuable insights into their experiences, preferences, and challenges regarding music sampling.
Details
Anna, a music blogger and occasional DJ, values understanding song sources due to her critical analysis background. With a strong musical foundation, she enjoys playing instruments at home and writing songs.
Details
Aviv, a software engineer from Haifa, is passionate about electronic music and exploring music through technology. He finds it challenging to identify subtle samples in electronic tracks, limiting his learning and experimentation.
Details
Shahar aspires to be a music producer, so he is interested in exploring samples to understand how to use them in the future. He spends hours on his computer watching blogs and videos on the subject.
Based on user interviews, personas, and market comparison, it's clear there's a strong demand for a tailored music sample recognition app. Interviews highlighted common pain points like sample quality and interface usability.
To understand how things are going to work I constructed the app flow using a flowchart.
Then to understand how things are going to look I created low-fidelity and high-fidelity wireframes.
Enable users to accurately identify multiple samples within a song
The app enables efficient navigation through multiple song samples for accurate recognition, even with various sampled elements.
Provide a list view interface for easy navigation and management of samples to improve user interaction efficiency.
Enable users to add recognized songs to their personal library, ensuring convenient organization and accessibility of favorite tracks and samples
