# Midi Miner -- A Python library for tonal tension and track classification

arXiv: https://arxiv.org/abs/1910.02049 · HTML: https://mlsanity.com/paper/1910.02049/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control](https://mlsanity.com/paper/2202.05528/) (2022)
- [GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework](https://mlsanity.com/paper/2305.10841/) (2023)
- [MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation](https://mlsanity.com/paper/2309.10738/) (2023)
- [Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment](https://mlsanity.com/paper/2505.12669/) (2025)
- [BandCondiNet: Parallel Transformers-based Conditional Popular Music Generation with Multi-View Features](https://mlsanity.com/paper/2407.10462/) (2024)
- [Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation](https://mlsanity.com/paper/2511.19342/) (2025)

## Abstract (arXiv)

We present a Python library, called Midi Miner, that can calculate tonal tension and classify different tracks. MIDI (Music Instrument Digital Interface) is a hardware and software standard for communicating musical events between digital music devices. It is often used for tasks such as music representation, communication between devices, and even music generation [5]. Tension is an essential element of the music listening experience, which can come from a number of musical features including timbre, loudness and harmony [3]. Midi Miner provides a Python implementation for the tonal tension model based on the spiral array [1] as presented by Herremans and Chew [4]. Midi Miner also performs key estimation and includes a track classifier that can disentangle melody, bass, and harmony tracks. Even though tracks are often separated in MIDI files, the musical function of each track is not always clear. The track classifier keeps the identified tracks and discards messy tracks, which can enable further analysis and training tasks.