# Learnable Wavelet Packet Transform for Data-Adapted Spectrograms

arXiv: https://arxiv.org/abs/2201.11069 · HTML: https://mlsanity.com/paper/2201.11069/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [Adaptive wavelet distillation from neural networks through interpretations](https://mlsanity.com/paper/2107.09145/) (2021)
- [Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series](https://mlsanity.com/paper/2105.00899/) (2021)
- [AclNet: efficient end-to-end audio classification CNN](https://mlsanity.com/paper/1811.06669/) (2018)
- [Learning Sparse Wavelet Representations](https://mlsanity.com/paper/1802.02961/) (2018)
- [Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring Signals](https://mlsanity.com/paper/1810.05550/) (2018)

## Built on by (most cited first, 5 of 5)

- [Robust Time Series Denoising with Learnable Wavelet Packet Transform](https://mlsanity.com/paper/2206.06126/) (2022)
- [Learning sparsity-promoting regularizers for linear inverse problems](https://mlsanity.com/paper/2412.16031/) (2024)
- [RINS-T: Robust Implicit Neural Solvers for Time Series Linear Inverse Problems](https://mlsanity.com/paper/2510.17396/) (2025)
- [Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios](https://mlsanity.com/paper/2307.01429/) (2023)
- [Offload Rethinking by Cloud Assistance for Efficient Environmental Sound Recognition on LPWANs](https://mlsanity.com/paper/2502.15285/) (2025)

## Abstract (arXiv)

Capturing high-frequency data concerning the condition of complex systems, e.g. by acoustic monitoring, has become increasingly prevalent. Such high-frequency signals typically contain time dependencies ranging over different time scales and different types of cyclic behaviors. Processing such signals requires careful feature engineering, particularly the extraction of meaningful time-frequency features. This can be time-consuming and the performance is often dependent on the choice of parameters. To address these limitations, we propose a deep learning framework for learnable wavelet packet transforms, enabling to learn features automatically from data and optimise them with respect to the defined objective function. The learned features can be represented as a spectrogram, containing the important time-frequency information of the dataset. We evaluate the properties and performance of the proposed approach by evaluating its improved spectral leakage and by applying it to an anomaly detection task for acoustic monitoring.