# EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use

arXiv: https://arxiv.org/abs/2207.05508 · HTML: https://mlsanity.com/paper/2207.05508/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2022.

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

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [End-to-end learning for music audio tagging at scale](https://mlsanity.com/paper/1711.02520/) (2017)
- [Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks](https://mlsanity.com/paper/1304.1018/) (2013)
- [Exploring Filterbank Learning for Keyword Spotting](https://mlsanity.com/paper/2006.00217/) (2020)

## Built on by (most cited first, 8 of 8)

- [Fitting Auditory Filterbanks with Multiresolution Neural Networks](https://mlsanity.com/paper/2307.13821/) (2023)
- [Instabilities in Convnets for Raw Audio](https://mlsanity.com/paper/2309.05855/) (2023)
- [Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition](https://mlsanity.com/paper/2401.02099/) (2024)
- [The Caf'e in Amsterdam: When the Incumbent Becomes the Oracle](https://mlsanity.com/paper/2607.13393/) (2026)
- [Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method](https://mlsanity.com/paper/2407.10828/) (2024)
- [Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)](https://mlsanity.com/paper/2412.00312/) (2024)
- [Acoustic Classification of Maritime Vessels using Learnable Filterbanks](https://mlsanity.com/paper/2505.23964/) (2025)
- [AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers](https://mlsanity.com/paper/2512.03637/) (2025)

## Abstract (arXiv)

In audio classification, differentiable auditory filterbanks with few parameters cover the middle ground between hard-coded spectrograms and raw audio. LEAF (arXiv:2101.08596), a Gabor-based filterbank combined with Per-Channel Energy Normalization (PCEN), has shown promising results, but is computationally expensive. With inhomogeneous convolution kernel sizes and strides, and by replacing PCEN with better parallelizable operations, we can reach similar results more efficiently. In experiments on six audio classification tasks, our frontend matches the accuracy of LEAF at 3% of the cost, but both fail to consistently outperform a fixed mel filterbank. The quest for learnable audio frontends is not solved.