RESUMO
We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.
RESUMO
Mass spectrometry is one of the main tools for protein identification in complex mixtures. When the sequence of the protein is known, we can check to see if the known mass distribution of peptides for a given protein is present in the recorded mass distribution of the mixture being analyzed. Unfortunately, this general approach suffers from high false-positive rates, since in a complex mixture, the likelihood that we will observe any particular mass distribution is high, whether or not the protein of interest is in the mixture. In this paper, we propose a scoring methodology and algorithm for protein identification that make use of a new experimental technique, which we call receptor arrays, for separating a mixture based on another differentiating property of peptides called isoelectric point (pI). We perform extensive simulation experiments on several genomes and show that additional information about peptides can achieve an average 30% reduction in false-positive rates over existing methods, while achieving very high true-positive identification rates.
