# Sound Event Detection in Urban Audio With Single and Multi-Rate PCEN

arXiv: https://arxiv.org/abs/2102.03468 · HTML: https://mlsanity.com/paper/2102.03468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Trainable Frontend For Robust and Far-Field Keyword Spotting](https://mlsanity.com/paper/1607.05666/) (2016)
- [Adaptive pooling operators for weakly labeled sound event detection](https://mlsanity.com/paper/1804.10070/) (2018)
- [Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization](https://mlsanity.com/paper/1911.00417/) (2019)

## Built on by (most cited first, 3 of 3)

- [Blind Acoustic Room Parameter Estimation Using Phase Features](https://mlsanity.com/paper/2303.07449/) (2023)
- [Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification](https://mlsanity.com/paper/2109.12058/) (2021)
- [Learnable Nonlinear Compression for Robust Speaker Verification](https://mlsanity.com/paper/2202.05236/) (2022)

## Abstract (arXiv)

Recent literature has demonstrated that the use of per-channel energy normalization (PCEN), has significant performance improvements over traditional log-scaled mel-frequency spectrograms in acoustic sound event detection (SED) in a multi-class setting with overlapping events. However, the configuration of PCEN's parameters is sensitive to the recording environment, the characteristics of the class of events of interest, and the presence of multiple overlapping events. This leads to improvements on a class-by-class basis, but poor cross-class performance. In this article, we experiment using PCEN spectrograms as an alternative method for SED in urban audio using the UrbanSED dataset, demonstrating per-class improvements based on parameter configuration. Furthermore, we address cross-class performance with PCEN using a novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED performance with MRPCEN, demonstrating improvements to cross-class performance compared to traditional single-rate PCEN.