Sound Event Envelope Estimation in Polyphonic Mixtures
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | IEEE |
| Pages | 935-939 |
| Number of pages | 5 |
| ISBN (Electronic) | 978-1-4799-8131-1 |
| ISBN (Print) | 978-1-4799-8132-8 |
| DOIs | |
| Publication status | Published - 17 Apr 2019 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Duration: 1 Jan 1900 → 1 Jan 2000 |
Publication series
| Name | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| ISSN (Print) | 1520-6149 |
| ISSN (Electronic) | 2379-190X |
Conference
| Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
|---|---|
| Period | 1/01/00 → 1/01/00 |
Abstract
Sound event detection is the task of identifying automatically the presence and temporal boundaries of sound events within an input audio stream. In the last years, deep learning methods have established themselves as the state-of-the-art approach for the task, using binary indicators during training to denote whether an event is active or inactive. However, such binary activity indicators do not fully describe the events, and estimating the envelope of the sounds could provide more precise modeling of their activity. This paper proposes to estimate the amplitude envelopes of target sound event classes in polyphonic mixtures. For training, we use the amplitude envelopes of the target sounds, calculated from mixture signals and, for comparison, from their isolated counterparts. The model is then used to perform envelope estimation and sound event detection. Results show that the envelope estimation allows good modeling of the sounds activity, with detection results comparable to current state-of-the art.
Keywords
- acoustic signal detection, acoustic signal processing, learning (artificial intelligence), sound event envelope estimation, polyphonic mixtures, sound event detection, input audio stream, deep learning methods, binary activity indicators, amplitude envelopes, target sound event classes, sounds activity, Training, Estimation, Event detection, Acoustics, Signal to noise ratio, Automobiles, Dogs, Sound event detection, Envelope estimation, Deep Neural Networks
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