![]() Note that your mileage may vary and that different operating systems can introduce significant “jitter” into the application by taking control of the CPU and invalidating the various CPU caches. The message being transferred between the two CPU cores was a simple, incrementing number, but literally could be anything. It retains the essence and spirit of the Disruptor and utilizes a lot of the same abstractions and concepts, but does not maintain the same API.On my MacBook Pro (Intel Core i7-4960HQ CPU 2.60GHz) using Go 1.4.2, I was able to push over 900 million messages per second (yes, you read that right) from one goroutine to another goroutine. This is a port of the LMAX Disruptor into the Go programming language. ![]() Go-disruptor - A port of the LMAX Disruptor to the Go language. The proposed model is based on the Demucs architecture, originally proposed for music source-separation: (Paper, Code). Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. It is optimized on both time and frequency domains, using multiple loss functions. The proposed model is based on an encoder-decoder architecture with skip-connections. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. ![]() We provide a PyTorch implementation of the paper: Real Time Speech Enhancement in the Waveform Domain. ![]() Denoiser - Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain ![]()
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