Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes. Currently, such IVAs are mostly audio-base…
Audio ClassificationResponse GenerationData augmentation approaches for improving animal audio classification
In this paper we present ensembles of classifiers for automated animal audio classification, exploiting different data augmentation techniques for training Convolutional Neural Networks (CNNs). The specific animal audio …
Audio ClassificationClassificationData AugmentationGeneral ClassificationEnvironmental Sound Classification with Parallel Temporal-spectral Attention
Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the u…
Acoustic Scene ClassificationAudio ClassificationClassificationEnvironmental Sound Classification+3Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation
Audio data augmentation is a key step in training deep neural networks for solving audio classification tasks. In this paper, we introduce Audiogmenter, a novel audio data augmentation library in MATLAB. We provide 15 di…
Audio ClassificationData AugmentationLungBRN: A Smart Digital Stethoscope for Detecting Respiratory Disease Using bi-ResNet Deep Learning Algorithm
Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medi…
Audio ClassificationDiagnosticLung Sound ClassificationSound ClassificationSelf-Supervised Learning by Cross-Modal Audio-Video Clustering
Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other with good accuracy. Their intrinsic dif…
Action RecognitionAudio ClassificationClusteringDeep Clustering+4An End-to-End Audio Classification System based on Raw Waveforms and Mix-Training Strategy
Audio classification can distinguish different kinds of sounds, which is helpful for intelligent applications in daily life. However, it remains a challenging task since the sound events in an audio clip is probably mult…
Audio ClassificationClassificationGeneral ClassificationMulti-Label Classification+1Cross-modal supervised learning for better acoustic representations
Obtaining large-scale human-labeled datasets to train acoustic representation models is a very challenging task. On the contrary, we can easily collect data with machine-generated labels. In this work, we propose to expl…
Audio ClassificationGeneral ClassificationSegment Relevance Estimation for Audio Analysis and Weakly-Labelled Classification
We propose a method that quantifies the importance, namely relevance, of audio segments for classification in weakly-labelled problems. It works by drawing information from a set of class-wise one-vs-all classifiers. By …
Audio ClassificationClassificationGeneral ClassificationLabel-efficient audio classification through multitask learning and self-supervision
While deep learning has been incredibly successful in modeling tasks with large, carefully curated labeled datasets, its application to problems with limited labeled data remains a challenge. The aim of the present work …
Audio ClassificationClassificationData AugmentationGeneral Classification+1The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Spiking neural networks are the basis of versatile and power-efficient information processing in the brain. Although we currently lack a detailed understanding of how these networks compute, recently developed optimizati…
Audio ClassificationGeneral ClassificationCertifying Neural Network Audio Classifiers
We present the first end-to-end verifier of audio classifiers. Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g…
Audio ClassificationDefensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks
As deep neural networks become widely adopted for solving most problems in computer vision and audio-understanding, there are rising concerns about their potential vulnerability. In particular, they are very sensitive to…
Adversarial DefenseAudio Classificationimage-classificationImage ClassificationSpectroBank: A filter-bank convolutional layer for CNN-based audio applications
We propose and investigate the design of a new convolutional layer where kernels are parameterized functions. This layer aims at being the input layer of convolutional neural networks for audio applications. The kernels …
Audio ClassificationAI for Earth: Rainforest Conservation by Acoustic Surveillance
Saving rainforests is a key to halting adverse climate changes. In this paper, we introduce an innovative solution built on acoustic surveillance and machine learning technologies to help rainforest conservation. In part…
Audio ClassificationBIG-bench Machine LearningClassificationEnvironmental Sound Classification+2Universal Adversarial Audio Perturbations
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios. We propose two methods for finding su…
Audio ClassificationCompact Global Descriptor for Neural Networks
Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convolutional operations to enlarge receptive…
Audio ClassificationDeep AttentionImage ClassificationObject DetectionDeep Neural Baselines for Computational Paralinguistics
Detecting sleepiness from spoken language is an ambitious task, which is addressed by the Interspeech 2019 Computational Paralinguistics Challenge (ComParE). We propose an end-to-end deep learning approach to detect and …
Audio ClassificationBIG-bench Machine LearningFeature EngineeringOn the performance of residual block design alternatives in convolutional neural networks for end-to-end audio classification
Residual learning is a recently proposed learning framework to facilitate the training of very deep neural networks. Residual blocks or units are made of a set of stacked layers, where the inputs are added back to their …
Audio ClassificationClassificationGeneral Classificationimage-classification+1Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging…
Activity RecognitionAudio ClassificationBayesian InferenceGeneral Classification+4