# TensorFlow Audio Models in Essentia

arXiv: https://arxiv.org/abs/2003.07393 · HTML: https://mlsanity.com/paper/2003.07393/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Discovering and Steering Interpretable Concepts in Large Generative Music Models](https://mlsanity.com/paper/2505.18186/) (2025)
- [Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio](https://mlsanity.com/paper/2407.14364/) (2024)
- [Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation](https://mlsanity.com/paper/2507.17937/) (2025)
- [MIRFLEX: Music Information Retrieval Feature Library for Extraction](https://mlsanity.com/paper/2411.00469/) (2024)
- [KARMA-MV: A Benchmark for Causal Question Answering on Music Videos](https://mlsanity.com/paper/2605.08175/) (2026)
- [Multi-task Learning with Metadata for Music Mood Classification](https://mlsanity.com/paper/2110.04765/) (2021)
- [Echoes of Humanity: Exploring the Perceived Humanness of AI Music](https://mlsanity.com/paper/2509.25601/) (2025)
- [On the Human and Computer Alignment of Attribute-Based Music Matches](https://mlsanity.com/paper/2609.00987/) (2026)
- [TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking](https://mlsanity.com/paper/2609.18585/) (2026)

## Abstract (arXiv)

Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexibility of use, easy extensibility, and real-time inference. To show the potential of this new interface with TensorFlow, we provide a number of pre-trained state-of-the-art music tagging and classification CNN models. We run an extensive evaluation of the developed models. In particular, we assess the generalization capabilities in a cross-collection evaluation utilizing both external tag datasets as well as manual annotations tailored to the taxonomies of our models.