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Noise-invariant Neurons in the Avian Auditory Cortex: Hearing the Song in Noise

Figure 5

Noise reduction algorithm.

We implemented a biologically inspired noise-filtering algorithm using an analysis/synthesis paradigm (top row) where the synthesis step is based on a STRF filter bank decomposition. The bottom row shows the model neural responses obtained from a sound (spectrogram of noise-corrupted song) using the filter bank of biologically realistic STRFs. These responses are then weighed optimally with weights d1,..,dM to select the combination of responses that are most noise-invariant. The weighted responses are then transformed into frequency space by multiplying the weighted responses by the frequency marginal of the corresponding STRF (color-matched on the figure) to obtain gains as a function of frequency. The top row illustrates how these time-varying frequency gains can then be applied to a decomposition of the sound into frequency channels allowing for the synthesis step and an estimate of the clean signal. This technology is available for licensing via UC Berkeley's Office of Technology Licensing (Technology: Modulation-Domain Speech Filtering For Noise Reduction; Tech ID: 22197; Lead Case: 2012-034-0).

Figure 5