# Invariances and Data Augmentation for Supervised Music Transcription

arXiv: https://arxiv.org/abs/1711.04845 · HTML: https://mlsanity.com/paper/1711.04845/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2017.

## Built on by (most cited first, 12 of 12)

- [Unaligned Supervision For Automatic Music Transcription in The Wild](https://mlsanity.com/paper/2204.13668/) (2022)
- [ReconVAT: A Semi-Supervised Automatic Music Transcription Framework for Low-Resource Real-World Data](https://mlsanity.com/paper/2107.04954/) (2021)
- [A Data-Driven Analysis of Robust Automatic Piano Transcription](https://mlsanity.com/paper/2402.01424/) (2024)
- [Omnizart: A General Toolbox for Automatic Music Transcription](https://mlsanity.com/paper/2106.00497/) (2021)
- [Jointist: Joint Learning for Multi-instrument Transcription and Its Applications](https://mlsanity.com/paper/2206.10805/) (2022)
- [Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training](https://mlsanity.com/paper/2302.00286/) (2023)
- [Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation](https://mlsanity.com/paper/2410.14122/) (2024)
- [Count The Notes: Histogram-Based Supervision for Automatic Music Transcription](https://mlsanity.com/paper/2511.14250/) (2025)
- [Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation](https://mlsanity.com/paper/2202.09198/) (2022)
- [Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis](https://mlsanity.com/paper/2202.11518/) (2022)
- [A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability](https://mlsanity.com/paper/2405.12847/) (2024)
- [Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset](https://mlsanity.com/paper/2608.06165/) (2026)

## Abstract (arXiv)

This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performing model in the 2017 MIREX Multiple Fundamental Frequency Estimation evaluation. This class of models shares parameters in the log-frequency domain, which exploits the frequency invariance of music to reduce the number of model parameters and avoid overfitting to the training data. All models in this paper were trained with supervision by labeled data from the MusicNet dataset, augmented by random label-preserving pitch-shift transformations.