Towards relatable explainable AI with the perceptual process
Machine learning models need to provide contrastive explanations, since people often seek
to understand why a puzzling prediction occurred instead of some expected outcome …
to understand why a puzzling prediction occurred instead of some expected outcome …
An interpretable deep learning model for automatic sound classification
Deep learning models have improved cutting-edge technologies in many research areas,
but their black-box structure makes it difficult to understand their inner workings and the …
but their black-box structure makes it difficult to understand their inner workings and the …
[HTML][HTML] Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture
The use of autonomous recordings of animal sounds to detect species is a popular
conservation tool, constantly improving in fidelity as audio hardware and software evolves …
conservation tool, constantly improving in fidelity as audio hardware and software evolves …
Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture
The use of autonomous recordings of animal sounds to detect species is a popular
conservation tool, constantly improving in fidelity as audio hardware and software evolves …
conservation tool, constantly improving in fidelity as audio hardware and software evolves …
Filterbank design for end-to-end speech separation
Single-channel speech separation has recently made great progress thanks to learned
filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been …
filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been …
Towards modeling raw speech in gender identification of children using sincNet over ERB scale
This article reveals the results of age-dependent gender identification from raw speech
using the recently developed non-native children's English speech corpus. Convolutional …
using the recently developed non-native children's English speech corpus. Convolutional …
E2E-SINCNET: Toward fully end-to-end speech recognition
Modern end-to-end (E2E) Automatic Speech Recognition (ASR) systems rely on Deep
Neural Networks (DNN) that are mostly trained on handcrafted and pre-computed acoustic …
Neural Networks (DNN) that are mostly trained on handcrafted and pre-computed acoustic …
Cgcnn: Complex gabor convolutional neural network on raw speech
Convolutional Neural Networks (CNN) have been used in Automatic Speech Recognition
(ASR) to learn representations directly from the raw signal instead of hand-crafted acoustic …
(ASR) to learn representations directly from the raw signal instead of hand-crafted acoustic …
[PDF][PDF] Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs.
Raw waveform acoustic modelling has recently received increasing attention. Compared
with the task-blind hand-crafted features which may discard useful information …
with the task-blind hand-crafted features which may discard useful information …
[HTML][HTML] Passive sonar automated target classifier for shallow waters using end-to-end learnable deep convolutional LSTMs
Automated target recognition systems are increasingly employed in sonar systems to reduce
manning and associated challenges. Although passive acoustic target recognition is an …
manning and associated challenges. Although passive acoustic target recognition is an …