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We were interested in computing a mini-batch-capable end-to end algorithm to identify statistically independent components (ICA) in large scale and high-dimensional datasets. Current algorithms typically rely on pre-whitened data and do not integrate the two procedures of whitening and ICA estimation. Our online approach estimates a whitening and a rotation matrix with stochastic gradient descent on centered or uncentered data. We show that this can be done efficiently by combining Batch Karhunen-Löwe-Transformation with Lie group techniques.

This study explored how the frequencies and bandwidths of the acoustic resonances of physical tube models of the vocal tract differ when they have hard versus soft walls. For each of 10 tube shapes representing different vowels, two physical models were made: one with rigid plastic walls, and one with soft silicone walls. For all models, the acoustic transfer functions were measured and the bandwidths and frequencies of the first three resonances were determined.

Speech enhancement has recently achieved great success with various
deep learning methods. However, most conventional speech enhancement
systems are trained with supervised methods that impose two
significant challenges. First, a majority of training datasets for speech
enhancement systems are synthetic. When mixing clean speech and
noisy corpora to create the synthetic datasets, domain mismatches
occur between synthetic and real-world recordings of noisy speech
or audio. Second, there is a trade-off between increasing speech

We introduce ImportantAug, a technique to augment training data
for speech classification and recognition models by adding noise
to unimportant regions of the speech and not to important regions.
Importance is predicted for each utterance by a data augmentation
agent that is trained to maximize the amount of noise it adds while
minimizing its impact on recognition performance. The effectiveness
of our method is illustrated on version two of the Google Speech
Commands (GSC) dataset. On the standard GSC test set, it achieves

Convolutional neural networks (CNNs) achieve remarkable performance in a wide range of fields. However, intensive memory access of activations introduces considerable energy consumption, impeding deployment of CNNs on resource-constrained edge devices. Existing works in activation compression propose to transform feature maps for higher compressibility, thus enabling dimension reduction. Nevertheless, in the case of aggressive dimension reduction, these methods lead to severe accuracy drop.

Naturalistic music typically contains repetitive musical patterns that are present throughout the song. These patterns form a signature, enabling effortless song recognition. We investigate whether neural responses corresponding to these repetitive patterns also serve as a signature, enabling recognition of later song segments on learning initial segments. We examine EEG encoding of naturalistic musical patterns employing the NMED-T and MUSIN-G datasets.