  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 TAU-NIGENS 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 log-mel spectrograms with
generalized cross-correlation spectra.
