2012 NIPS NeurIPS 2012

Hierarchical spike coding of sound

Abstract

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.

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Speech & Audio
📈 Trend Setter — Prosody Analysis
🧭 Keyword Pioneer — hierarchical audio coding
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio
🐣 Hot Topic Early Bird — sparse coding