Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Ultra-light deep MIR by trimming lottery tickets
Current state-of-the-art results in Music Information Retrieval are largely dominated by deep learning approaches. These provide unprecedented accuracy across all tasks. However, the consistently overlooked downside of t…
Audio ClassificationCPUDrum TranscriptionInformation Retrieval+2Text-based classification of interviews for mental health -- juxtaposing the state of the art
Currently, the state of the art for classification of psychiatric illness is based on audio-based classification. This thesis aims to design and evaluate a state of the art text classification network on this challenge. …
Audio ClassificationClassificationGeneral ClassificationLanguage Modeling+3Rethinking CNN Models for Audio Classification
In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification. Even though there is a significant difference between audio Spectrogram and stand…
Audio ClassificationClassificationEnvironmental Sound ClassificationGeneral Classification+2An Ensemble of Convolutional Neural Networks for Audio Classification
In this paper, ensembles of classifiers that exploit several data augmentation techniques and four signal representations for training Convolutional Neural Networks (CNNs) for audio classification are presented and teste…
Audio ClassificationClassificationData AugmentationEnvironmental Sound Classification+2Private Speech Classification with Secure Multiparty Computation
Deep learning in audio signal processing, such as human voice audio signal classification, is a rich application area of machine learning. Legitimate use cases include voice authentication, gunfire detection, and emotion…
Audio ClassificationAudio Signal ProcessingClassificationEmotion Recognition+2A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to …
Audio ClassificationTransfer LearningSelf-Supervised MultiModal Versatile Networks
Videos are a rich source of multi-modal supervision. In this work, we learn representations using self-supervision by leveraging three modalities naturally present in videos: visual, audio and language streams. To this e…
Action Recognition In VideosAudio ClassificationSelf-Supervised Action RecognitionSelf-Supervised Audio ClassificationAdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Normalization techniques are a boon for modern deep learning. They let weights converge more quickly with often better generalization performances. It has been argued that the normalization-induced scale invariance among…
Audio ClassificationImage ClassificationLanguage ModellingObject Detection+1A Deep Neural Network for Audio Classification with a Classifier Attention Mechanism
Audio classification is considered as a challenging problem in pattern recognition. Recently, many algorithms have been proposed using deep neural networks. In this paper, we introduce a new attention-based neural networ…
Audio ClassificationGeneral ClassificationLarge Scale Audiovisual Learning of Sounds with Weakly Labeled Data
Recognizing sounds is a key aspect of computational audio scene analysis and machine perception. In this paper, we advocate that sound recognition is inherently a multi-modal audiovisual task in that it is easier to diff…
Audio ClassificationGeneralised Interpretable Shapelets for Irregular Time Series
The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'. However it has previously suffered from a number of li…
Audio ClassificationIrregular Time SeriesTime SeriesTime Series AnalysisMicrophone Array Based Surveillance Audio Classification
The work assessed seven classical classifiers and two beamforming algorithms for detecting surveillance sound events. The tests included the use of AWGN with -10 dB to 30 dB SNR. Data Augmentation was also employed to im…
Audio ClassificationClassificationData AugmentationGeneral ClassificationA FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results. Although most techniqu…
Audio ClassificationBIG-bench Machine Learningimage-classificationImage ClassificationAudio-Visual Instance Discrimination with Cross-Modal Agreement
We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show t…
Action RecognitionAudio ClassificationContrastive LearningSelf-Supervised Action Recognition+2On Compositions of Transformations in Contrastive Self-Supervised Learning
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we generalize contrastive learning to a wider set …
Action RecognitionAudio ClassificationContrastive LearningRepresentation Learning+1$Π-$nets: Deep Polynomial Neural Networks
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to …
Audio ClassificationGraph Representation LearningImage ClassificationImage Generation+1Speech emotion recognition with deep convolutional neural networks
The speech emotion recognition (or, classification) is one of the most challenging topics in data science. In this work, we introduce a new architecture, which extracts mel-frequency cepstral coefficients, chroma-gram, m…
Audio ClassificationEmotion RecognitionSpeech Emotion RecognitionLearning with Out-of-Distribution Data for Audio Classification
In supervised machine learning, the assumption that training data is labelled correctly is not always satisfied. In this paper, we investigate an instance of labelling error for classification tasks in which the dataset …
Audio ClassificationClassificationGeneral ClassificationAutomatic Classification of Large-Scale Respiratory Sound Dataset Based on Convolutional Neural Network
Auscultation of respiratory sounds is very important for discovering the respiratory disease. However, there is no quantitative evaluation method for the diagnosis of respiratory sounds until now. It is necessary to deve…
Audio ClassificationClassificationSpecificityExploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog
We are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities an…
Audio ClassificationVisual Grounding