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Hierarchical spike coding of sound

Hierarchical spike coding of sound

This video was recorded at Video Journal of Machine Learning Abstracts - Volume 3. We develop a probabilistic generative model for representing acoustic event structure at multiple scales via a two-stage hierarchy. The first stage consists of a spiking representation which encodes a sound with a sparse set of kernels at different frequencies positioned precisely in time. The coarse time and frequency statistical structure of the first-stage spikes is encoded by a second stage spiking representation, while fine-scale statistical regularities are encoded by recurrent interactions within the first-stage. When fitted to speech data, the model encodes acoustic features such as harmonic stacks, sweeps, and frequency modulations, that can be composed to represent complex acoustic events. The model is also able to synthesize sounds from the higher-level representation and provides significant improvement over wavelet thresholding techniques on a denoising task.


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