# Filterbank design for end-to-end speech separation

arXiv: https://arxiv.org/abs/1910.10400 · HTML: https://mlsanity.com/paper/1910.10400/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 16 of 16)

- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [MossFormer: Pushing the Performance Limit of Monaural Speech Separation using Gated Single-Head Transformer with Convolution-Augmented Joint Self-Attentions](https://mlsanity.com/paper/2302.11824/) (2023)
- [Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes](https://mlsanity.com/paper/2206.11703/) (2022)
- [Learnable MFCCs for Speaker Verification](https://mlsanity.com/paper/2102.10322/) (2021)
- [Phase-Aware Deep Speech Enhancement: It's All About The Frame Length](https://mlsanity.com/paper/2203.16222/) (2022)
- [On permutation invariant training for speech source separation](https://mlsanity.com/paper/2102.04945/) (2021)
- [Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain](https://mlsanity.com/paper/2110.04791/) (2021)
- [Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet](https://mlsanity.com/paper/2402.17701/) (2024)
- [Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification](https://mlsanity.com/paper/2109.12058/) (2021)
- [Learning Filterbanks for End-to-End Acoustic Beamforming](https://mlsanity.com/paper/2111.04614/) (2021)
- [To what extent can ASV systems naturally defend against spoofing attacks?](https://mlsanity.com/paper/2406.05339/) (2024)
- [MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms](https://mlsanity.com/paper/2406.07103/) (2024)
- [IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection](https://mlsanity.com/paper/2506.19014/) (2025)
- [A Modulation Front-End for Music Audio Tagging](https://mlsanity.com/paper/2105.11836/) (2021)
- [Learning Sparse Analytic Filters for Piano Transcription](https://mlsanity.com/paper/2108.10382/) (2021)

## Abstract (arXiv)

Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learned and parameterized filterbanks into complex-valued analytic filterbanks and define a set of corresponding representations and masking strategies. We evaluate these filterbanks on a newly released noisy speech separation dataset (WHAM). The results show that the proposed analytic learned filterbank consistently outperforms the real-valued filterbank of ConvTasNet. Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions. Finally, we show that the STFT achieves its best performance for 2ms windows.