Music similarity software often gives you one score. This score combines melody, rhythm, and timbre into one result. MERIT changes this approach. It separates these elements. You get three distinct scores for melody, rhythm, and timbre. This helps you understand how two pieces of music match in specific areas.
Your computer needs to meet these basic standards to run MERIT:
- Operating System: Windows 10 or Windows 11.
- Processor: A modern multi-core processor from Intel or AMD.
- Memory: At least 8GB of RAM.
- Storage: 500MB of free disk space for the program and model files.
- Graphics: A basic graphics card that supports modern software displays.
- Internet: A connection to download the required audio files and software models.
Follow these steps to set up the software on your Windows computer.
- Visit this page to download the application.
- Look for the release section on the right side of the screen.
- Select the file ending in .exe to start your download.
- Locate the file in your downloads folder once the process finishes.
- Double-click the file to begin the installation.
- Follow the prompts on your screen to complete the setup process.
- Open the MERIT icon from your desktop or start menu.
The software interface provides a clear space to perform music analysis. You do not need to write code to use it.
Click the Browse button to find the first audio file on your computer. Repeat this for the second audio file. MERIT supports common audio formats like MP3 and WAV.
Click the Analyze button after you select your files. The software processes the audio and creates the similarity profile. This takes a few seconds depending on the length of your files.
The screen displays three separate bars after the calculation finishes:
- Melody: This bar shows how closely the tunes match.
- Rhythm: This bar shows how closely the beat patterns match.
- Timbre: This bar shows how closely the sound quality and instrumentation match.
Each bar shows a number between zero and one. A one means the audio segments are identical in that specific area. A zero means they share no common features.
Many tools fail to distinguish between the components of music. If you compare a jazz track to a classical piece, you might see a single low score. This does not tell you why the music sounds different. MERIT reveals that they might share the same rhythm even if the melody differs. Collectors, music scholars, and audio engineers gain better insights with this specific feedback.
Keep these tips in mind to get the best results from the software:
- File Quality: Use high-quality audio files. Compressed files with low bitrates might affect the accuracy of the similarity scores.
- Length: Use segments of music that last between ten and thirty seconds. This provides enough data for the engine to create precise comparisons.
- Hardware: Close other intensive programs while running the analysis. This ensures your computer has enough power to finish the task quickly.
- Updates: Check the website periodically for newer versions. Updates often improve the speed and accuracy of the analysis model.
Do I need a paid license to use this? No. This tool is free to use under the MIT license. You can use it for your projects without paying any fees.
Does the software send my music over the internet? No. The processing happens on your local computer. Your files stay private and do not leave your machine.
Can I compare more than two files at once? The current version compares two files per session. You can run new pairs as often as you like to continue your work.
What happens if the software stops during analysis? Make sure your audio files are not open in another program. Sometimes, other apps lock files, which prevents MERIT from reading them. Close other media players and try the process again.
Where can I find more help? The GitHub project page provides a list of issues managed by community contributors. You can read through existing posts to find solutions for common setup errors or usage questions.
MERIT stands for Multi-Factor Disentangled Music Similarity. It represents ongoing work in the field of audio research. Researchers created this tool to help solve the problem of collapsed similarity scores. You benefit from modern machine learning techniques without needing to understand the underlying mathematics or programming. If you have experience with code, you can also view the repository to see how the model functions. For most users, the desktop application serves as the primary way to interact with the system. You will find that the interface mirrors standard Windows behavior to ensure a smooth and familiar experience.