ConvTasNet: An Overview of a Revolutionary Audio Separation TechniqueConvTasNet is a groundbreaking deep learning approach to audio separation, which builds on the success of the original TasNet architecture. This technique is capable of efficiently separating individual sound sources from a mixture of sounds in both speech and music domains. In this article, we will explore ConvTasNet's principles, methodology, and its applications in various industries such as music production, voice recogniti
What is SepFormer for Speech Separation?
SepFormer is a neural network created to separate speech signals in a recording. It uses a transformer-based architecture that is designed to learn both short and long-term dependencies. The SepFormer is mainly composed of multi-head attention and feed-forward layers, and it adopts a dual-path framework introduced by the DPRNN to mitigate the quadratic complexity of transformers. It replaces RNNs with a multiscale pipeline composed of transformers to acc
VoiceFilter-Lite is a system that separates speech signals to enhance the accuracy of speech recognition in noisy environments. This single-channel source separation model is efficient and runs directly on the device.
What is VoiceFilter-Lite?
VoiceFilter-Lite is a speech recognition technology that relies on a machine learning algorithm to separate speech signals from background noise in real-time streaming applications. The system is designed to enhance speech recognition accuracy by filter