Project Proposal Feedback

[pooyanjamshidi]

Feedback on Melomusic Proposal:

First off, I really like the concept behind Melomusic! The idea of transcribing music into sheet music using a machine learning model is not only useful but also addresses a real-world gap for musicians and composers who typically begin with audio before formalizing their ideas into notation. Below are my thoughts and suggestions for refining the project and making sure it’s on track.

Strengths:

• Well-Defined Problem: The problem is clearly stated. Transcribing music from audio and identifying repeated melodies is a common challenge, and providing an AI solution could make life a lot easier for composers and musicians. Your project aims to go beyond simple transcription by identifying and highlighting repeated patterns, which adds an extra layer of value.
• Use of CNNs for Spectrogram Analysis: Leveraging CNNs to analyze spectrograms is a solid choice. CNNs excel at identifying patterns in image-like data, and spectrograms are a natural fit for that. By mapping audio data onto a time-frequency grid, you can capture both temporal and frequency-based patterns in the music.
• Novel Feature (Melody Identification): Your plan to extend the project to identify and track repeating melody lines is a key differentiator from existing tools like Klangio. This feature has practical applications, especially for music producers and songwriters who want to identify common motifs.

Suggestions:

1. Clarify Dataset Choice and Preprocessing:

   • You mention using audio files and corresponding sheet music as the dataset. While this is a great start, make sure you clarify what kinds of music will be included in your dataset (e.g., classical, pop, jazz) and how diverse the dataset will be. Different genres can present unique challenges for transcription, especially in terms of rhythm complexity or timbral variation.
   • Additionally, be specific about how you’ll preprocess the audio files into spectrograms. Are you using standard transformations like Short-Time Fourier Transform (STFT)? Will you use any techniques to handle noise or variations in audio quality?

2. Handling Polyphonic Music:

   • Transcribing polyphonic music (where multiple notes are played simultaneously) is significantly more difficult than monophonic transcription. How do you plan to handle complex polyphonic audio? Some of the papers you referenced (like Kwon et al., 2018) focus on polyphonic transcription—consider building on their work for this aspect of your project.
   • You could consider breaking down the problem into smaller tasks, starting with monophonic music and gradually extending to polyphonic cases as your model improves.

3. Melody Identification and Tracking:

   • Melody tracking adds a nice layer to the project, but how will you differentiate melodies from harmonies or accompanying lines? Are you planning to use any specific models or techniques to identify melody lines versus background instrumentation? Consider using existing music information retrieval methods that focus on melody extraction and tracking.

4. Evaluation Metrics:

   • You’ve outlined that you’ll use both quantitative (accuracy of note transcription) and qualitative (readability of sheet music) methods for evaluation, which is a good start. However, be sure to specify how you’ll measure accuracy. Will it be note-wise accuracy, chord accuracy, or rhythm accuracy? Each of these factors contributes to the overall performance of the model, so breaking them down into individual metrics could give you better insight into your system’s strengths and weaknesses.
   • For melody identification, consider metrics like precision and recall to measure how well your system detects and highlights repeated motifs or patterns.

5. System Readability:

   • A qualitative evaluation of sheet music readability is important, but consider defining what makes sheet music “readable.” This could include things like proper spacing of notes, correct usage of time signatures, or how easily a musician can interpret the output. You might want to bring in musicians to provide subjective feedback during your testing phase.

6. Time and Scope Management:

   • Your proposal is ambitious, so managing the scope is crucial. Start by focusing on a core functionality (like basic note transcription for monophonic music) and then move on to more complex tasks like polyphonic transcription or melody identification. This incremental approach will help ensure you have a working prototype by the deadline.

7. Comparison to Existing Tools:

   • You briefly mention tools like Klangio, but it would be helpful to expand on how your system will differ. Will your transcription be more accurate? Will your melody identification system provide better insights? Consider running a few comparisons with existing products during your evaluation to highlight the unique value of Melomusic.

Additional Suggestions:

• Real-World Application and Deployment:

  • Think about how this system will be used in real-world scenarios. Will musicians input raw audio files, or will they need to upload cleaned, pre-processed files? What format will the sheet music be exported in (PDF, MIDI, MusicXML)? These considerations could affect your design decisions, especially in terms of user interface and experience.

• Generalization Across Genres:

  • Different genres present unique challenges (e.g., swing rhythm in jazz, heavy syncopation in pop). As you build your model, consider how well it generalizes across musical styles. It might be useful to create genre-specific versions of the model if you encounter challenges with generalization.

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Conclusion:

Melomusic is an exciting and useful project with a clear goal. You’re tackling a real problem in the music industry, and your proposed approach using CNNs to transcribe audio into sheet music and track melodies makes sense. My main advice would be to clarify your dataset choice and evaluation methods, and make sure to break down the project into manageable steps so that you can deliver a polished proof of concept before adding complexity.

I’m looking forward to seeing how this develops! Good luck!