# Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1511.05520 · HTML: https://mlsanity.com/paper/1511.05520/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [Show Me the Instruments: Musical Instrument Retrieval from Mixture Audio](https://mlsanity.com/paper/2211.07951/) (2022)
- [Music Instrument Classification Reprogrammed](https://mlsanity.com/paper/2211.08379/) (2022)
- [Timbre Classification of Musical Instruments with a Deep Learning Multi-Head Attention-Based Model](https://mlsanity.com/paper/2107.06231/) (2021)

## Abstract (arXiv)

Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm. In these "shallow" architectures, feature engineering and learning are typically disjoint and unrelated. Additionally, feature engineering is difficult, and typically depends on extensive domain expertise. In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification. In this model, feature extraction and learning algorithms are trained together in an end-to-end fashion. We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.