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Very deep convolutional neural networks for raw waveforms
TLDR
This work proposes very deep convolutional neural networks that directly use time-domain waveforms as inputs that are efficient to optimize over very long sequences, necessary for processing acoustic waveforms.
View on IEEE
Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval
TLDR
This paper proposes a novel framework that simultaneously utilizes multi-modal features (different visual characteristics, audio inputs, and text) by a fusion strategy for efficient retrieval and explores several loss functions in training the embedding.
View on ACM
A Comparison of Five Multiple Instance Learning Pooling Functions for Sound Event Detection with Weak Labeling
TLDR
This paper builds a neural network called TALNet, which is the first system to reach state-of-the-art audio tagging performance on Audio Set, while exhibiting strong localization performance on the DCASE 2017 challenge at the same time.
View on IEEE
Universal Phone Recognition with a Multilingual Allophone System
TLDR
This work proposes a joint model of both language-independent phone and language-dependent phoneme distributions that can build a (nearly-)universal phone recognizer that, when combined with the PHOIBLE [1] large, manually curated database of phone inventories, can be customized into 2,000 language dependent recognizers.
View on IEEE
Adversarial camera stickers: A physical camera-based attack on deep learning systems
TLDR
This work shows that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet misclassify target objects as a different (targeted) class.
View PDF on arXiv
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
TLDR
This is the first real-world adversarial attack against a commercial grade VA wake-word detection system and can effectively reduce the recognition F1 score of the emulated model from 93.4% to 11.0%.
View PDF on arXiv
A comparison of Deep Learning methods for environmental sound detection
TLDR
This work presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes.
View on IEEE
Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection
TLDR
This paper proposes a small-footprint multiple instance learning (MIL) framework for multi-class AED using weakly annotated labels and shows that audio embeddings extracted by the convolutional neural networks significantly boost the performance of all MIL models.
View via Publisher
Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms
TLDR
A prototype system called FORK is developed using off-the-shelf components that performs the entire depth data processing on a cheaper and low power ARM processor in real-time to detect humans and track them in a very efficient way.
View on IEEE
Adversarial camera stickers: A Physical Camera Attack on Deep Learning Classifier
TLDR
It is shown that by placing a carefully crafted and mainly-translucent sticker over the lens of a camera, one can create universal perturbations of the observed images that are inconspicuous, yet reliably misclassify target objects as a different (targeted) class.
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