Temporal coding of local spectrogram features for robust sound recognition

Abstract

There is much evidence to suggest that the human auditory system uses localised time-frequency information for the robust recognition of sounds. Despite this, conventional systems typically rely on features extracted from short windowed frames over time, covering the whole frequency spectrum. Such approaches are not inherently robust to noise, as each frame will contain a mixture of the spectral information from noise and signal. Here, we propose a novel approach based on the temporal coding of Local Spectrogram Features (LSFs), which generate spikes that are used to train a Spiking Neural Network (SNN) with temporal learning. LSFs represent robust location information in the spectrogram surrounding keypoints, which are detected in a signal-driven manner such that the effect of noise on the temporal coding is reduced. Our experiments demonstrate the robust performance of our approach across a variety of noise conditions, such that it is able to outperform the conventional frame-based baseline methods.