We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence or word. In a recorded speech signal, those pauses introduce a series of time periods during which only noise is present. We leverage these incidental silent intervals to learn a model for automatic speech denoising given only mono-channel audio. Detected silent intervals over time expose not just pure noise but its time-varying features, allowing the model to learn noise dynamics and suppress it from the speech signal. Experiments on multiple datasets confirm the pivotal role of silent interval detection for speech denoising, and our method outperforms several state-of-the-art denoising methods, including those that accept only audio input (like ours) and those that denoise based on audiovisual input (and hence require more information). We also show that our method enjoys excellent generalization properties, such as denoising spoken languages not seen during training.

Speech serves as a ubiquitous input interface for embedded mobile devices. Cloud-based solutions, while offering powerful speech understanding services, raise significant concerns regarding user privacy. To address this, disentanglement-based encoders have been proposed to remove sensitive information from speech signals without compromising the speech understanding functionality. However, these encoders demand high memory usage and computation complexity, making them impractical for resource-constrained wimpy devices.Our solution is based on a key observation that speech understanding hinges on long-term dependency knowledge of the entire utterance, in contrast to privacy-sensitive elements that are short-term dependent. Exploiting this observation, we propose SILENCE, a lightweight system that selectively obscuring short-term details, without damaging the long-term dependent speech understanding performance.The crucial part of SILENCE is a differential mask generator derived from interpretable learning to automatically configure the masking process.We have implemented SILENCE on the STM32H7 microcontroller and evaluate its efficacy under different attacking scenarios.

The meaning of life differs from man to man, from day to day, and from hour to hour -- Viktor E. Frankle, Man's search for meaning. Frankle was not only right about the meaning of life, his saying was correct about machine learning models in production too. ML models perform well when you deploy them in production. Its quality of predictions decay and soon becomes less valuable. This is the primary difference between a software deployment and a machine learning one.

If LG has its way, the company's robots will soon be serving you breakfast, carrying your luggage, and cleaning your floors. Well, assuming they can overcome some pretty basic problems like not working, that is. The promise of a connected-robot future was made repeatedly Monday morning at CES in Las Vegas, with LG's vice president of US marketing, David VanderWaal, taking the stage to show off a line of AI-powered robots that are intended to both integrate with a smart home and work in commercial settings. Unfortunately for all The Jetsons fanboys out there, the biggest impression was made by what was left unsaid. VanderWaal first introduced CLOi, a small robot designed for the home, with an attempt at humanization.