Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Investigating Multi-Feature Selection and Ensembling for Audio Classification
Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns--particularly in class…
Audio ClassificationClassificationfeature selectionZero-Shot Audio Classification using Image Embeddings
Supervised learning methods can solve the given problem in the presence of a large set of labeled data. However, the acquisition of a dataset covering all the target classes typically requires manual labeling which is ex…
Audio ClassificationClassificationZero-shot Audio ClassificationZero-Shot LearningFeature-informed Embedding Space Regularization For Audio Classification
Feature representations derived from models pre-trained on large-scale datasets have shown their generalizability on a variety of audio analysis tasks. Despite this generalizability, however, task-specific features can o…
Audio ClassificationClassificationRobust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks
Spiking neural networks (SNNs), particularly the single-spike variant in which neurons spike at most once, are considerably more energy efficient than standard artificial neural networks (ANNs). However, single-spike SSN…
Audio ClassificationImage ClassificationRobust classificationTime Series+1Augmentation-induced Consistency Regularization for Classification
Deep neural networks have become popular in many supervised learning tasks, but they may suffer from overfitting when the training dataset is limited. To mitigate this, many researchers use data augmentation, which is a …
Audio ClassificationClassificationData AugmentationLearning Rate Curriculum
Most curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learning approach termed Learning Rate Curric…
Audio ClassificationQNLIRTESpeech Emotion RecognitionAutomatic Tuberculosis and COVID-19 cough classification using deep learning
We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough a…
Audio ClassificationClassificationCough ClassificationDeep Learning+1Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a u…
Audio ClassificationAudio Signal ProcessingVocalsound: A Dataset for Improving Human Vocal Sounds Recognition
Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring. However, existing datasets have a relatively small number …
Audio ClassificationOn Negative Sampling for Audio-Visual Contrastive Learning from Movies
The abundance and ease of utilizing sound, along with the fact that auditory clues reveal a plethora of information about what happens in a scene, make the audio-visual space an intuitive choice for representation learni…
Action RecognitionAudio ClassificationContrastive LearningForm+2A DOMAIN TRANSFER BASED DATA AUGMENTATION METHOD FOR AUTOMATED RESPIRATORY CLASSIFICATION
Automated auscultation has become a hot topic in the medical field for diagnostic and predictive analytic. Automated auscultation aims to improve the classification of respiratory sounds recorded by electronic stethoscop…
Audio ClassificationData AugmentationDiagnosticATST: Audio Representation Learning with Teacher-Student Transformer
Self-supervised learning (SSL) learns knowledge from a large amount of unlabeled data, and then transfers the knowledge to a specific problem with a limited number of labeled data. SSL has achieved promising results in v…
Audio ClassificationInstrument RecognitionRepresentation LearningSelf-Supervised Audio Classification+3End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network
While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on nu…
Audio ClassificationClassificationEnvironmental Sound Classificationimage-classification+3Few-Shot Speaker Identification Using Depthwise Separable Convolutional Network with Channel Attention
Although few-shot learning has attracted much attention from the fields of image and audio classification, few efforts have been made on few-shot speaker identification. In the task of few-shot learning, overfitting is a…
Audio ClassificationFew-Shot LearningSpeaker IdentificationMAP-SNN: Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks
Spiking Neural Network (SNN) is considered more biologically realistic and power-efficient as it imitates the fundamental mechanism of the human brain. Recently, backpropagation (BP) based SNN learning algorithms that ut…
Audio ClassificationDiversityMetaAudio: A Few-Shot Audio Classification Benchmark
Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification. This work aims to alleviate this relia…
Audio ClassificationClassificationFew-Shot Audio ClassificationFew-Shot Learning+3Underwater single-channel acoustic signal multitarget recognition using convolutional neural networks
The radiated noise from ships is of great significance to target recognition, and several deep learning methods have been developed for the recognition of underwater acoustic signals. Previous studies have focused on sin…
Audio ClassificationAudio Multiple Target ClassificationBinary ClassificationMAE-AST: Masked Autoencoding Audio Spectrogram Transformer
In this paper, we propose a simple yet powerful improvement over the recent Self-Supervised Audio Spectrogram Transformer (SSAST) model for speech and audio classification. Specifically, we leverage the insight that the …
Audio ClassificationDecoderLearning neural audio features without supervision
Deep audio classification, traditionally cast as training a deep neural network on top of mel-filterbanks in a supervised fashion, has recently benefited from two independent lines of work. The first one explores "learna…
Audio ClassificationSelf-Supervised LearningSepTr: Separable Transformer for Audio Spectrogram Processing
Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community. This is because signals are often represented as sp…
Audio ClassificationSpeech Emotion RecognitionTime Series Analysis