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Title: SALSA: spatial cue-augmented log-spectrogram features for polyphonic sound event localization and detection
Authors: Nguyen, Thi Ngoc Tho
Watcharasupat, Karn N.
Nguyen, Ngoc Khanh
Jones, Douglas L.
Gan, Woon-Seng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Nguyen, T. N. T., Watcharasupat, K. N., Nguyen, N. K., Jones, D. L. & Gan, W. (2022). SALSA: spatial cue-augmented log-spectrogram features for polyphonic sound event localization and detection. IEEE/ACM Transactions On Audio, Speech, and Language Processing, 30, 1749-1762.
Project: MOE2017-T2-2-060
Journal: IEEE/ACM Transactions on Audio, Speech, and Language Processing
Abstract: Sound event localization and detection (SELD) consists of two subtasks, which are sound event detection and direction-of-arrival estimation. While sound event detection mainly relies on time-frequency patterns to distinguish different sound classes, direction-of-arrival estimation uses amplitude and/or phase differences between microphones to estimate source directions. As a result, it is often difficult to jointly optimize these two subtasks. We propose a novel feature called Spatial cue-Augmented Log-SpectrogrAm (SALSA) with exact time-frequency mapping between the signal power and the source directional cues, which is crucial for resolving overlapping sound sources. The SALSA feature consists of multichannel log-spectrograms stacked along with the normalized principal eigenvector of the spatial covariance matrix at each corresponding time-frequency bin. Depending on the microphone array format, the principal eigenvector can be normalized differently to extract amplitude and/or phase differences between the microphones. As a result, SALSA features are applicable for different microphone array formats such as first-order ambisonics (FOA) and multichannel microphone array (MIC). Experimental results on the TAUNIGENS Spatial Sound Events 2021 dataset with directional interferences showed that SALSA features outperformed other state-of-the-art features. Specifically, the use of SALSA features in the FOA format increased the F1 score and localization recall by 6 % each, compared to the multichannel log-mel spectrograms with intensity vectors. For the MIC format, using SALSA features increased F1 score and localization recall by 16 % and 7 %, respectively, compared to using multichannel logmel spectrograms with generalized cross-correlation spectra.
ISSN: 2329-9290
DOI: 10.1109/TASLP.2022.3173054
Schools: School of Electrical and Electronic Engineering 
Research Centres: Centre for Infocomm Technology (INFINITUS) 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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