# SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition

arXiv: https://arxiv.org/abs/2502.00310 · HTML: https://mlsanity.com/paper/2502.00310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 82 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 17 of 17)

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- [Deep Learning is Robust to Massive Label Noise](https://mlsanity.com/paper/1705.10694/) (2017)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications](https://mlsanity.com/paper/2005.02144/) (2020)
- [Multi-level Wavelet Convolutional Neural Networks](https://mlsanity.com/paper/1907.03128/) (2019)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Convolutional Neural Networks Analyzed via Convolutional Sparse Coding](https://mlsanity.com/paper/1607.08194/) (2016)
- [WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis](https://mlsanity.com/paper/1911.07925/) (2019)
- [Fourier Neural Networks: A Comparative Study](https://mlsanity.com/paper/1902.03011/) (2019)
- [Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition](https://mlsanity.com/paper/2110.03435/) (2021)
- [Joint Time-Frequency Scattering](https://mlsanity.com/paper/1807.08869/) (2018)
- [Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis](https://mlsanity.com/paper/2001.02015/) (2020)
- [Deep scattering network for speech emotion recognition](https://mlsanity.com/paper/2105.04806/) (2021)

## Abstract (arXiv)

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://github.com/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition.