Converting MIDI to Scilab Matrix: Tips and Code Examples

Converting MIDI to Scilab Matrix: Tips and Code Examples Converting Musical Instrument Digital Interface (MIDI) files into Scilab matrices allows engineers, researchers, and data scientists to analyze musical structures, process acoustic data, and apply mathematical algorithms to audio signals. This guide provides a direct workflow, optimization tips, and clean code examples to parse MIDI files into numerical matrices within Scilab. The Concept: Mapping MIDI Data to Matrices

A MIDI file does not contain raw audio. Instead, it stores event messages like note onset, pitch, velocity, and duration. To process this in Scilab, these sequential events must be mapped into a structured matrix format.

A standard approach represents a MIDI track as an N × 4 or N × 5 matrix, where N is the number of notes: Row (Note) Column 1: Start Time (Seconds or Ticks) Column 2: Pitch (MIDI Note Number) Column 3: Velocity (Volume 0-127) Column 4: Duration (Seconds or Ticks) Column 5: Channel 60 (Middle C) Step-by-Step Implementation in Scilab

Because Scilab lacks a built-in, native .mid file parser, the most reliable approach is to use standard toolbox extensions or parse a CSV representation of the MIDI file. Step 1: Pre-processing MIDI to CSV (Recommended)

Before loading the data into Scilab, convert the MIDI file to a readable text format like CSV. Tools like midicsv (a command-line utility) instantly transform binary MIDI data into structured text. midicsv input.mid text_midi.csv Use code with caution. Step 2: Reading and Parsing in Scilab

Once your data is in a structured text format, use Scilab’s string manipulation and matrix functions to extract note events.

Below is a complete script template to parse a simplified MIDI-CSV structure into a numerical Scilab matrix:

// Scilab Script: Extracting Note Events to Matrix clear; clc; // 1. Load the CSV data // Assuming the CSV contains: StartTime, Pitch, Velocity, Duration csv_path = “text_midi.csv”; raw_data = csvRead(csv_path, “,”, [], “string”); // 2. Initialize matrix variables num_rows = size(raw_data, 1); midi_matrix = []; // 3. Loop through rows and filter for note-on events // This logic filters out header information and extracts active notes for i = 1:num_rows // Check if the row contains valid note data (adjust column index based on your CSV tool) if raw_data(i, 3) == “Note_on_c” then start_time = evstr(raw_data(i, 2)); pitch = evstr(raw_data(i, 5)); velocity = evstr(raw_data(i, 6)); // Temporarily store duration or calculate it from corresponding Note_off // For simplicity, we log basic event parameters here midi_matrix = [midi_matrix; start_time, pitch, velocity]; end end // 4. Display results disp(“Successfully converted MIDI events to Scilab Matrix:”); disp(“Columns: [Start Time (Ticks), Pitch (0-127), Velocity (0-127)]”); disp(midi_matrix(1:min(10, size(midi_matrix, 1)), :)); // Show first 10 notes Use code with caution. Advanced Method: Utilizing the SciNet / SIVP Toolboxes

If you prefer to work entirely within Scilab without external command-line utilities, you can leverage Java object bridging. Since Scilab integrates seamlessly with the Java Virtual Machine (JVM), you can call standard Java MIDI libraries directly from your Scilab workspace.

// Loading Java MIDI classes inside Scilab javaclose(); jimport javax.sound.midi.MidiSystem; jimport java.io.File; // Load the file midi_file = javaNew(“java.io.File”, “input.mid”); sequence = MidiSystem.getSequence(midi_file); // Extract sequence properties ticks = sequence.getTickLength(); disp(“Total sequence length in ticks: ” + string(ticks)); Use code with caution. Performance Optimization Tips

When dealing with large, multi-track symphonic MIDI files, data handling in Scilab can slow down. Use these optimization tips to ensure high performance:

Pre-allocate Matrices: Avoid growing matrices dynamically inside loops (e.g., matrix = [matrix; new_row]). This forces Scilab to reallocate memory constantly. Instead, count the lines first and pre-allocate using midi_matrix = zeros(total_notes, 4).

Vectorize Vector Operations: Convert time ticks to seconds all at once using element-wise operators (time_seconds = time_ticks ./ resolution) instead of converting line-by-line inside a loop.

Filter Unused Channels: MIDI files often contain pitch bends, system exclusive (SysEx) data, and tempo changes. Filter these out during the text-reading phase to keep your numerical matrix clean and lean. Conclusion

Converting MIDI data to a Scilab matrix bridges the gap between musical arrangement and mathematical synthesis. Whether you choose the highly reliable CSV-parsing pipeline or interface directly with Java classes, structured matrix logging allows you to easily deploy Fast Fourier Transforms (FFT), note density algorithms, and machine learning models on musical data. If you would like to expand this implementation, tell me:

What tool or command-line utility you prefer for your pre-processing steps.

The exact matrix dimensions or features (like note duration calculations) you need to extract.

Whether you want to apply specific signal processing algorithms to the matrix afterward.