OPAW: Independent Component Analysis

Independent Component Analysis (ICA) fascinates me [DEMO]. It's a neat method for isolating signals from a signal mix, as long as multiple recordings of the signal mix from different perspectives are available. An application would be isolating speakers in a conference room with many people talking (over each other), while a set of different microphones … Continue reading OPAW: Independent Component Analysis

OPAW: Optimal Bounds for Open Addressing Without Reordering

This paper by Martin Farach-Colton, Andrew Krapivin and William Kuszmaul shook the computer science interwebs last year as it proposed a much faster implementation for hash maps. An important disclaimer: unless you are way more bewandered in data structures than me, you're forgiven to believe that this paper revolutionises mainstream hash map implementations like the … Continue reading OPAW: Optimal Bounds for Open Addressing Without Reordering

OPAW: An observer-based approach to the sorites paradox and the logic derived from that

The sorites paradox asks: at what point does a heap stop being a heap? The compact version: let's take a large heap, remove one grain, then it surely is still a heap. Rinse and repeat. What is the grain count threshold after which a heap isn't a heap any more? It's one of those logic … Continue reading OPAW: An observer-based approach to the sorites paradox and the logic derived from that

OPAW: Real-Time Target Sound Extraction

In this instalment of "One Paper a Week", we're looking at Waveformer, a neural network for extracting specific waveforms from a sound mix in real-time. If you're thinking "Independent Component Analysis", you're not alone: ICA can also extract a desired signal from a mix of signals (similarly to how we are able to understand a … Continue reading OPAW: Real-Time Target Sound Extraction