3 research outputs found
Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling
We consider the problem of separating speech sources captured by multiple
spatially separated devices, each of which has multiple microphones and samples
its signals at a slightly different rate. Most asynchronous array processing
methods rely on sample rate offset estimation and resampling, but these offsets
can be difficult to estimate if the sources or microphones are moving. We
propose a source separation method that does not require offset estimation or
signal resampling. Instead, we divide the distributed array into several
synchronous subarrays. All arrays are used jointly to estimate the time-varying
signal statistics, and those statistics are used to design separate
time-varying spatial filters in each array. We demonstrate the method for
speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal
Enhancement (IWAENC 2018
The 2015 Signal Separation Evaluation Campaign
International audienceIn this paper, we report the 2015 community-based Signal Separation Evaluation Campaign (SiSEC 2015). This SiSEC consists of four speech and music datasets including two new datasets: " Professionally produced music recordings " and " Asynchronous recordings of speech mixtures ". Focusing on them, we overview the campaign specifications such as the tasks, datasets and evaluation criteria. We also summarize the performance of the submitted systems