TL;DR for operators Kammoun, Leglaive, Alameda-Pineda, and Gerkmann propose a source-free way to adapt a pretrained speech-enhancement model to one noisy utterance at inference time.1 The system uses one second of unlabeled deployment audio and a separately trained model of clean speech as its adaptation signal. It resets the enhancement model to its pretrained parameters before each new utterance, so adaptation is local rather than cumulative. ...
TL;DR for operators A team shrinking an audio classifier for a low-cost screening device has more than one place to spend scarce compute. The network can stay larger, or the input representation can do more work before the signal reaches the network. That trade-off became visible when this heart-sound CNN was reduced from three convolutional blocks to two. Using a plain log-mel spectrogram, modified accuracy—a score that gives equal weight to abnormal-case sensitivity and normal-case specificity—fell from 0.910 to 0.826. With a front-end that normalizes each frequency band against its recent energy so locally unusual sounds stand out more clearly, called PCEN, it fell only from 0.915 to 0.894. A front-end representing the same sound at several time-frequency resolutions, called multi-resolution log-mel, similarly fell from 0.916 to 0.894. ...