# Shennong: a Python toolbox for audio speech features extraction

arXiv: https://arxiv.org/abs/2112.05555 · HTML: https://mlsanity.com/paper/2112.05555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Contrastive Learning of General-Purpose Audio Representations](https://mlsanity.com/paper/2010.10915/) (2020)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [The Zero Resource Speech Challenge 2017](https://mlsanity.com/paper/1712.04313/) (2017)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units](https://mlsanity.com/paper/2010.05967/) (2020)
- [End-to-End Speech Recognition From the Raw Waveform](https://mlsanity.com/paper/1806.07098/) (2018)
- [Surfboard: Audio Feature Extraction for Modern Machine Learning](https://mlsanity.com/paper/2005.08848/) (2020)
- [Vocal markers from sustained phonation in Huntington's Disease](https://mlsanity.com/paper/2006.05365/) (2020)

## Built on by (most cited first, 1 of 1)

- [Improving End-to-End SLU performance with Prosodic Attention and Distillation](https://mlsanity.com/paper/2305.08067/) (2023)

## Abstract (arXiv)

We introduce Shennong, a Python toolbox and command-line utility for speech features extraction. It implements a wide range of well-established state of art algorithms including spectro-temporal filters such as Mel-Frequency Cepstral Filterbanks or Predictive Linear Filters, pre-trained neural networks, pitch estimators as well as speaker normalization methods and post-processing algorithms. Shennong is an open source, easy-to-use, reliable and extensible framework. The use of Python makes the integration to others speech modeling and machine learning tools easy. It aims to replace or complement several heterogeneous software, such as Kaldi or Praat. After describing the Shennong software architecture, its core components and implemented algorithms, this paper illustrates its use on three applications: a comparison of speech features performances on a phones discrimination task, an analysis of a Vocal Tract Length Normalization model as a function of the speech duration used for training and a comparison of pitch estimation algorithms under various noise conditions.